Applied_Machine_Learning_.ipynb

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      "source": [
        "# Table of Content\n",
        "1. Machine Learning and Types\n",
        "2. Application of Machine Learning\n",
        "3. Steps of Machine Learning\n",
        "4. Factors help to choose algorithm\n",
        "5. Algorithm\n",
        "         Linear Regression\n",
        "         TheilSenRegressor\n",
        "         RANSAC Regressor\n",
        "         HuberRegressor\n",
        "         Logistic Regression\n",
        "         GaussianProcessClassifier\n",
        "         Support Vector Machine\n",
        "         Nu-Support Vector Classification\n",
        "         Naive Bayes Algorithm\n",
        "         KNN\n",
        "         Perceptron\n",
        "         Random Forest\n",
        "         Decision Tree\n",
        "         Extra Tree\n",
        "         AdaBoost Classifier\n",
        "         PassiveAggressiveClassifier\n",
        "         Bagging Classifier\n",
        "         Gradient Boosting\n",
        "                 Light GBM\n",
        "                 XGBoost\n",
        "                 Catboost\n",
        "                 Stochastic Gradient Descent\n",
        "         Lasso\n",
        "         RidgeC lassifier CV\n",
        "         Kernel Ridge Regression\n",
        "         Bayesian Ridge\n",
        "         Elastic Net Regression\n",
        "         LDA\n",
        "         K-Means Algorithm\n",
        "         CNN\n",
        "         LSTM\n",
        "         PCA\n",
        "         Apriori\n",
        "         Prophet\n",
        "6. Evaluate Algorithms\n",
        "                \n",
        "   \n"
      ]
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      "cell_type": "markdown",
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      "source": [
        "# Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.\n",
        "There are many algorithm for getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks depending on what task you’re trying to accomplish and the type and amount of data that you have available.  \n",
        "**"
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      "source": [
        "**There are three types of machine learning** \n",
        "1. Supervised Machine Learning \n",
        "2. Unsupervised Machine Learning \n",
        "3. Reinforcement Machine Learning "
      ]
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      "source": [
        "#  Supervised Machine Learning \n",
        "\n",
        "**It is a type of learning in which both input and desired output data are provided. Input and output data are labeled for classification to provide a learning basis for future data processing.This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.   \n",
        "**"
      ]
    },
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      "source": [
        "# Unsupervised Machine Learning\n",
        "\n",
        "**Unsupervised learning is the training of an algorithm using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.The main idea behind unsupervised learning is to expose the machines to large volumes of varied data and allow it to learn and infer from the data. However, the machines must first be programmed to learn from data. **\n",
        "\n",
        "** Unsupervised learning problems can be further grouped into clustering and association problems.  \n",
        "**\n",
        "1. Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behaviour. \n",
        "2. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. \n",
        "\n",
        "\n"
      ]
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      "source": [
        "# Reinforcement Machine Learning \n",
        "**Reinforcement Learning is a type of Machine Learning which allows machines to automatically determine the ideal behaviour within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behaviour; this is known as the reinforcement signal.It differs from standard supervised learning, in that correct input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is on performance, which involves finding a balance between exploration of uncharted territory and exploitation of current knowledge  \n",
        "**\n"
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      "source": [
        "# Application of Supervised Machine Learning \n",
        "1. Bioinformatics \n",
        "2. Quantitative structure \n",
        "3. Database marketing \n",
        "4. Handwriting recognition \n",
        "5. Information retrieval \n",
        "6. Learning to rank \n",
        "7. Information extraction \n",
        "8. Object recognition in computer vision \n",
        "9. Optical character recognition \n",
        "10. Spam detection \n",
        "11. Pattern recognition \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Application of Unsupervised Machine Learning \n",
        "1. Human Behaviour Analysis \n",
        "2. Social Network Analysis to define groups of friends. \n",
        "3. Market Segmentation of companies by location, industry, vertical. \n",
        "4. Organizing computing clusters based on similar event patterns and processes. \n"
      ]
    },
    {
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      "source": [
        "# Application of Reinforcement Machine Learning \n",
        "1. Resources management in computer clusters \n",
        "2. Traffic Light Control \n",
        "3. Robotics \n",
        "4. Web System Configuration \n",
        "5. Personalized Recommendations \n",
        "6. Deep Learning \n"
      ]
    },
    {
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      "source": [
        "# We can apply machine learning model by following six steps:-\n",
        "1. Problem Definition \n",
        "2. Analyse Data \n",
        "3. Prepare Data \n",
        "4. Evaluate Algorithm \n",
        "5. Improve Results \n",
        "6. Present Results \n"
      ]
    },
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      "source": [
        "# Factors help to choose algorithm \n",
        "1. Type of algorithm \n",
        "2. Parametrization \n",
        "3. Memory size \n",
        "4. Overfitting tendency \n",
        "5. Time of learning \n",
        "6. Time of predicting"
      ]
    },
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      "source": [
        "# Linear Regression \n",
        "**It is a basic and commonly used type of predictive analysis. These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables. \n",
        "Y = a + bX where **\n",
        "* Y – Dependent Variable \n",
        "* a – intercept \n",
        "* X – Independent variable \n",
        "* b – Slope \n",
        "\n",
        "**Example: University GPA' = (0.675)(High School GPA) + 1.097**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "**Library and Data **"
      ]
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        "outputId": "b80836cf-34e3-4e64-99b6-d6eb34514426"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "train = pd.read_csv(\"/content/datasets_1256_2242_train.csv\") \n",
        "test = pd.read_csv(\"/content/datasets_1256_2242_test.csv\") \n",
        "train = train.dropna()\n",
        "test = test.dropna()\n",
        "train.head()\n"
      ],
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              "      <td>24.0</td>\n",
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      "cell_type": "markdown",
      "metadata": {
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        "colab_type": "text"
      },
      "source": [
        "**Model with plots and accuracy**"
      ]
    },
    {
      "cell_type": "code",
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        "id": "WTf-pJ7MqTeN",
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      "source": [
        "X_train = np.array(train.iloc[:, :-1].values)\n",
        "y_train = np.array(train.iloc[:, 1].values)\n",
        "X_test = np.array(test.iloc[:, :-1].values)\n",
        "y_test = np.array(test.iloc[:, 1].values)\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "\n",
        "plt.plot(X_train, model.predict(X_train), color='green')\n",
        "plt.show()\n",
        "print(accuracy)"
      ],
      "execution_count": 3,
      "outputs": [
        {
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          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "0.9888014444327563\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6ySk3bsqTeQ",
        "colab_type": "text"
      },
      "source": [
        "# TheilSen Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "sT_z-jI8qTeQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d4f8d883-c1b8-469d-ab75-1679f7a0088c"
      },
      "source": [
        "from sklearn.linear_model import  TheilSenRegressor\n",
        "model = TheilSenRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(accuracy)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9887120931974983\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kj-8nDH1qTeT",
        "colab_type": "text"
      },
      "source": [
        "# RANSAC Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9ZchNBJpqTeT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "39d83b2e-63ea-4a1d-f98c-e23ffbc60fd2"
      },
      "source": [
        "from sklearn.linear_model import  RANSACRegressor\n",
        "model = RANSACRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(accuracy)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9888014444327563\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuklYRmXqTeX",
        "colab_type": "text"
      },
      "source": [
        "# Huber Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ICt5z8f3qTeX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c2be9a45-5110-4ccd-9861-23869318a94d"
      },
      "source": [
        "from sklearn.linear_model import  HuberRegressor\n",
        "model = HuberRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(accuracy)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9888146261518426\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "eX_QLt0WqTeb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tbo4bUp5qTee",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression \n",
        "**It’s a classification algorithm, that is used where the response variable is categorical. The idea of Logistic Regression is to find a relationship between features and probability of particular outcome.**   \n",
        "* odds= p(x)/(1-p(x)) = probability of event occurrence / probability of not event occurrence \n",
        "\n",
        "**Example- When we have to predict if a student passes or fails in an exam when the number of hours spent studying is given as a feature, the response variable has two values, pass and fail. \n",
        "**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YVDuOLtqTef",
        "colab_type": "text"
      },
      "source": [
        "**Libraries and data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "-blQMUJvqTef",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "740508a5-3bba-45fe-efd1-a0e26e3a50fb"
      },
      "source": [
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import r2_score\n",
        "from statistics import mode\n",
        "\n",
        "\n",
        "train = pd.read_csv(\"/content/train.csv\")\n",
        "test  = pd.read_csv('/content/test.csv')\n",
        "train.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "trusted": true,
        "id": "mPvaLAZdqTei",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ports = pd.get_dummies(train.Embarked , prefix='Embarked')\n",
        "train = train.join(ports)\n",
        "train.drop(['Embarked'], axis=1, inplace=True)\n",
        "train.Sex = train.Sex.map({'male':0, 'female':1})\n",
        "y = train.Survived.copy()\n",
        "X = train.drop(['Survived'], axis=1) \n",
        "X.drop(['Cabin'], axis=1, inplace=True) \n",
        "X.drop(['Ticket'], axis=1, inplace=True) \n",
        "X.drop(['Name'], axis=1, inplace=True) \n",
        "X.drop(['PassengerId'], axis=1, inplace=True)\n",
        "X.Age.fillna(X.Age.median(), inplace=True) \n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_iku_2XqTek",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "WzNuUn5nqTek",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4b680f39-91c2-4e6a-e8a0-950008451271"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=5)\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression(max_iter = 500000)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(accuracy)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8251121076233184\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJSzGPlOqTem",
        "colab_type": "text"
      },
      "source": [
        "**Confusion Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "n81RdugJqTen",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d359423b-7c00-4180-fb8f-6b2fb7f5823a"
      },
      "source": [
        "print(confusion_matrix(y_test,y_pred))\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[125  15]\n",
            " [ 24  59]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHk2U8SBqTep",
        "colab_type": "text"
      },
      "source": [
        "**Report**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "1mNGuw0-qTeq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "4ca6944a-6ff7-4042-cf90-47ca05d8fe2b"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.89      0.87       140\n",
            "           1       0.80      0.71      0.75        83\n",
            "\n",
            "    accuracy                           0.83       223\n",
            "   macro avg       0.82      0.80      0.81       223\n",
            "weighted avg       0.82      0.83      0.82       223\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4h53CBeWqTet",
        "colab_type": "text"
      },
      "source": [
        "# Gaussian Process Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "tTegJ4LCqTet",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8e3f2477-60fc-441a-aeb7-8781f6b4de4c"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=5)\n",
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "model = GaussianProcessClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(accuracy)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7488789237668162\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "1wfFOo2TqTev",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "c5c2743a-e386-4507-e604-5e29fda27d76"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.86      0.81       140\n",
            "           1       0.70      0.57      0.63        83\n",
            "\n",
            "    accuracy                           0.75       223\n",
            "   macro avg       0.74      0.71      0.72       223\n",
            "weighted avg       0.74      0.75      0.74       223\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfgTkCasqTe0",
        "colab_type": "text"
      },
      "source": [
        "# Support Vector Machine \n",
        "**Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms.It is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables \n",
        "**\n",
        "\n",
        "**Example: One class is linearly separable from the others like if we only had two features like Height and Hair length of an individual, we’d first plot these two variables in two dimensional space where each point has two co-ordinates **"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EH3co_hzqTe0",
        "colab_type": "text"
      },
      "source": [
        "**Libraries and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "jbMwop2YqTe1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.svm import SVC\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzRZKjcrqTe3",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nLh4aI6zqTe4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4246c9e8-28e2-4eaf-b3b3-ea1652163d4a"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "y_pred = classifier.predict(X_test)\n",
        "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
        "accuracies.mean()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6542740841248305"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "D9ro8EyhqTe6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "258530f4-f351-4c9e-d0ed-778796193ac0"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.96      0.81       139\n",
            "           1       0.82      0.33      0.47        84\n",
            "\n",
            "    accuracy                           0.72       223\n",
            "   macro avg       0.76      0.65      0.64       223\n",
            "weighted avg       0.75      0.72      0.68       223\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCrVagvLqTe_",
        "colab_type": "text"
      },
      "source": [
        "# Nu Support Vector Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGhutrJUqTe_",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "WtAVtZ4-qTfA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.svm import NuSVC\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Q_015FpqTfC",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "kga194erqTfC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "54a626df-3f91-4381-e635-d322d38ba232"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n",
        "classifier = NuSVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "y_pred = classifier.predict(X_test)\n",
        "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
        "accuracies.mean()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7919945725915875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "1K4gAwYtqTfF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "df714e03-94db-45ef-a06c-5819d88cc52e"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.82      0.82       139\n",
            "           1       0.70      0.70      0.70        84\n",
            "\n",
            "    accuracy                           0.78       223\n",
            "   macro avg       0.76      0.76      0.76       223\n",
            "weighted avg       0.78      0.78      0.78       223\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMTyvfrfqTfK",
        "colab_type": "text"
      },
      "source": [
        "# Naive Bayes Algorithm \n",
        "**A naive Bayes classifier is not a single algorithm, but a family of machine learning algorithms which use probability theory to classify data with an assumption of independence between predictors It is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods    \n",
        "**\n",
        "\n",
        "**Example: Emails are given and we have to find the spam emails from that.A spam filter looks at email messages for certain key words and puts them in a spam folder if they match.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qj8ZDw69qTfK",
        "colab_type": "text"
      },
      "source": [
        "**Libraries and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "CUBg9sHTqTfL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQnawIpsqTfN",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pakik4QbqTfN",
        "colab_type": "text"
      },
      "source": [
        "**Gaussian NB**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "19OOKD7wqTfN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "27731f42-90ac-4207-e657-41dda3224954"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=GaussianNB()\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7988826815642458\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YTWWbmJZqTfR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "caed08e1-b661-41cf-f255-2a748725484e"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.80      0.83       110\n",
            "           1       0.71      0.80      0.75        69\n",
            "\n",
            "    accuracy                           0.80       179\n",
            "   macro avg       0.79      0.80      0.79       179\n",
            "weighted avg       0.81      0.80      0.80       179\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rwx8gtSqTfS",
        "colab_type": "text"
      },
      "source": [
        "**BernoulliNB**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "wnb-OMlYqTfT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d5d9a281-f936-4538-de3b-92a2695f2c49"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=BernoulliNB()\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7932960893854749\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Vm2YAQPCqTfV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "6e519ef4-f15a-4931-f955-399f1325f649"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.85      0.83       110\n",
            "           1       0.74      0.71      0.73        69\n",
            "\n",
            "    accuracy                           0.79       179\n",
            "   macro avg       0.78      0.78      0.78       179\n",
            "weighted avg       0.79      0.79      0.79       179\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evwTKWkUqTfY",
        "colab_type": "text"
      },
      "source": [
        "# KNN \n",
        "**KNN does not learn any model. and stores the entire training data set which it uses as its representation.The output can be calculated as the class with the highest frequency from the K-most similar instances. Each instance in essence votes for their class and the class with the most votes is taken as the prediction \n",
        "**\n",
        "\n",
        "**Example: Should the bank give a loan to an individual? Would an individual default on his or her loan? Is that person closer in characteristics to people who defaulted or did not default on their loans? **\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_7WzRkGqTfY",
        "colab_type": "text"
      },
      "source": [
        "**Libraries and Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCnvVWkWqTfY",
        "colab_type": "text"
      },
      "source": [
        "**As Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "L49lRedeqTfZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neighbors import KNeighborsRegressor\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8FtVgbqqTfc",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "pvuzhZX7qTfd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "44097495-b0ef-46b6-ed21-027f9ed09414"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8268156424581006\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "NoYg1G7mqTff",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ec515aeb-96a8-43a1-8e28-2e127b87be88"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.88      0.86       110\n",
            "           1       0.80      0.74      0.77        69\n",
            "\n",
            "    accuracy                           0.83       179\n",
            "   macro avg       0.82      0.81      0.81       179\n",
            "weighted avg       0.83      0.83      0.83       179\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bs6VmRKbqTfg",
        "colab_type": "text"
      },
      "source": [
        "**As Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AGlLawWqTfh",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ZJnQMVlOqTfh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "train = pd.read_csv(\"/content/datasets_1256_2242_train.csv\") \n",
        "test = pd.read_csv(\"/content/datasets_1256_2242_test.csv\") \n",
        "train = train.dropna()\n",
        "test = test.dropna()\n",
        "X_train = np.array(train.iloc[:, :-1].values)\n",
        "y_train = np.array(train.iloc[:, 1].values)\n",
        "X_test = np.array(test.iloc[:, :-1].values)\n",
        "y_test = np.array(test.iloc[:, 1].values)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzyFDmcEqTfk",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9IRa5_YrqTfk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "87140779-375c-46e6-ff9b-3449b606602b"
      },
      "source": [
        "model = KNeighborsRegressor(n_neighbors=2)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(accuracy)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9854793959456346\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Md2uDPfOqTfn",
        "colab_type": "text"
      },
      "source": [
        "# Perceptron "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrarRvICqTfn",
        "colab_type": "text"
      },
      "source": [
        "** It is single layer neural network and used for classification **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "D9hTkt0RqTfn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "571bee18-af51-447b-8778-83a83532fdc2"
      },
      "source": [
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "p = pd.read_csv(\"/content/datasets_19_420_Iris.csv\")\n",
        "p.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "x7pq8L56qTfp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "63c90d92-dccf-42b1-ab1a-bb400d0fed90"
      },
      "source": [
        "X = p.iloc[:, [1,2,3,4]].values\n",
        "y = p.iloc[:, 5].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=Perceptron()\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "GAGx1LxsqTfr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "a92dde87-e11e-4101-ce71-eb352d23f2c7"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "    Iris-setosa       1.00      1.00      1.00        11\n",
            "Iris-versicolor       0.93      1.00      0.96        13\n",
            " Iris-virginica       1.00      0.83      0.91         6\n",
            "\n",
            "       accuracy                           0.97        30\n",
            "      macro avg       0.98      0.94      0.96        30\n",
            "   weighted avg       0.97      0.97      0.97        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzCcG7mtqTfu",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest \n",
        "**Random forest is collection of tress(forest) and it builds multiple decision trees and merges them together to get a more accurate and stable prediction.It can be used for both classification and regression problems.**\n",
        "\n",
        "**Example: Suppose we have a bowl of 100 unique numbers from 0 to 99. We want to select a random sample of numbers from the bowl. If we put the number back in the bowl, it may be selected more than once. \n",
        "**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5RMIRPKqTfv",
        "colab_type": "text"
      },
      "source": [
        "**Libraries and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Kn4itOJWqTfv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHpB0XFQqTfy",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "6zrl3IjGqTfz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3356548a-04d0-470f-96ca-31626e7d9cbc"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
        "model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=1)\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9210526315789473"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nXngkVYXqTf2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tza1JwHJqTf4",
        "colab_type": "text"
      },
      "source": [
        "# Decision Tree\n",
        "**Decision tree algorithm is classification algorithm under supervised machine learning and it is simple to understand and use in data.The idea of Decision tree is to split the big data(root) into smaller(leaves)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zJX9E_b7qTf4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "ca3470e0-1e73-4bc6-f2f5-d5121a3dc868"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dt = p\n",
        "dt.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "u87m3kI4qTf7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "54872335-a2b7-4c44-e6a4-f09a13c5285f"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=DecisionTreeClassifier(criterion=\"entropy\",random_state=0)\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKtAf0oKqTf9",
        "colab_type": "text"
      },
      "source": [
        "# Extra Tree"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6BWP2tR4qTf9",
        "colab_type": "text"
      },
      "source": [
        "**Library and  Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "lsrvSbpMqTf9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNBVNQNyqTgA",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "C3Ny_764qTgA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9f064ab9-3218-4a72-c0fc-4d64e3b96313"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=ExtraTreesClassifier(criterion=\"entropy\",random_state=0)\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFwmBR9yqTgB",
        "colab_type": "text"
      },
      "source": [
        "# AdaBoost Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-xl8mzSqTgC",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nS1CA1FcqTgC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQ9EydehqTgE",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accutacy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ssqo8sy1qTgF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cc8a3c83-3baf-40b4-a462-53d0542685b9"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=AdaBoostClassifier(random_state=0)\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D42nNA8YqTgG",
        "colab_type": "text"
      },
      "source": [
        "# Passive Aggressive Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "duefCTPNqTgG",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "yMpKQ6UrqTgH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import PassiveAggressiveClassifier\n"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CWGj4muqqTgK",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "oz1fucfrqTgK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2fc765ab-2904-40b9-8082-3075591982c2"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=PassiveAggressiveClassifier(random_state=0)\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zzUPi4gqTgM",
        "colab_type": "text"
      },
      "source": [
        "# Bagging Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_T5U0Q7eqTgM",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "gF8-Y9VOqTgN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import BaggingClassifier\n"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6doRSXEqTgR",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "naSsZ8xKqTgT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "529fd11c-6bca-49c6-ecae-6288ffb2565b"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "classifier=BaggingClassifier(random_state=0)\n",
        "classifier.fit(X_train,y_train)\n",
        "y_pred=classifier.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkVcLMgoqTgU",
        "colab_type": "text"
      },
      "source": [
        "# Gradient Boosting\n",
        "**Gradient boosting is an alogithm under supervised machine learning, boosting means converting weak into strong. In this new tree is boosted over the previous tree**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hksgLK6dqTgV",
        "colab_type": "text"
      },
      "source": [
        "**Libraries and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "qbZQ7aOtqTgV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwG2ZdmLqTgX",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "5veqIfvFqTgX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "19871314-5ca6-4511-81bd-c6cc0d4048b4"
      },
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "gbk = GradientBoostingClassifier()\n",
        "gbk.fit(X_train, y_train)\n",
        "pred = gbk.predict(X_test)\n",
        "acc=accuracy_score(y_test, y_pred)\n",
        "print(acc)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjOb6ReOqTgY",
        "colab_type": "text"
      },
      "source": [
        "# Light GBM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mqVTaG9qTgZ",
        "colab_type": "text"
      },
      "source": [
        "**LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:**\n",
        "\n",
        "1. Faster training speed and higher efficiency.\n",
        "2. Lower memory usage.\n",
        "3. Better accuracy.\n",
        "4. Support of parallel and GPU learning.\n",
        "5. Capable of handling large-scale data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vU1SPzP1qTgZ",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "BHF_Y45xqTgZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "b2e7092c-637c-4a74-9445-21308c5a1230"
      },
      "source": [
        "import lightgbm as lgbm\n",
        "import lightgbm as lgb\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import KFold, GridSearchCV\n",
        "from sklearn import preprocessing\n",
        "\n",
        "\n",
        "train = pd.read_csv(\"/content/train (1).csv\")\n",
        "test = pd.read_csv(\"/content/test (1).csv\")\n",
        "data = pd.concat([train, test], sort=False)\n",
        "data = data.reset_index(drop=True)\n",
        "data.head()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>...</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>706.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>8</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>978.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>162.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>486.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>216.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal  208500.0\n",
              "1   2          20       RL  ...        WD         Normal  181500.0\n",
              "2   3          60       RL  ...        WD         Normal  223500.0\n",
              "3   4          70       RL  ...        WD        Abnorml  140000.0\n",
              "4   5          60       RL  ...        WD         Normal  250000.0\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8KzS9cd2qTgb",
        "colab_type": "text"
      },
      "source": [
        "**Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "_kg_hide-output": true,
        "trusted": true,
        "id": "IbCO9l6oqTgb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d51e4079-9432-4ff1-be72-5ce73109af61"
      },
      "source": [
        "nans=pd.isnull(data).sum()\n",
        "\n",
        "data['MSZoning']  = data['MSZoning'].fillna(data['MSZoning'].mode()[0])\n",
        "data['Utilities'] = data['Utilities'].fillna(data['Utilities'].mode()[0])\n",
        "data['Exterior1st'] = data['Exterior1st'].fillna(data['Exterior1st'].mode()[0])\n",
        "data['Exterior2nd'] = data['Exterior2nd'].fillna(data['Exterior2nd'].mode()[0])\n",
        "\n",
        "data[\"BsmtFinSF1\"]  = data[\"BsmtFinSF1\"].fillna(0)\n",
        "data[\"BsmtFinSF2\"]  = data[\"BsmtFinSF2\"].fillna(0)\n",
        "data[\"BsmtUnfSF\"]   = data[\"BsmtUnfSF\"].fillna(0)\n",
        "data[\"TotalBsmtSF\"] = data[\"TotalBsmtSF\"].fillna(0)\n",
        "data[\"BsmtFullBath\"] = data[\"BsmtFullBath\"].fillna(0)\n",
        "data[\"BsmtHalfBath\"] = data[\"BsmtHalfBath\"].fillna(0)\n",
        "data[\"BsmtQual\"] = data[\"BsmtQual\"].fillna(\"None\")\n",
        "data[\"BsmtCond\"] = data[\"BsmtCond\"].fillna(\"None\")\n",
        "data[\"BsmtExposure\"] = data[\"BsmtExposure\"].fillna(\"None\")\n",
        "data[\"BsmtFinType1\"] = data[\"BsmtFinType1\"].fillna(\"None\")\n",
        "data[\"BsmtFinType2\"] = data[\"BsmtFinType2\"].fillna(\"None\")\n",
        "\n",
        "data['KitchenQual']  = data['KitchenQual'].fillna(data['KitchenQual'].mode()[0])\n",
        "data[\"Functional\"]   = data[\"Functional\"].fillna(\"Typ\")\n",
        "data[\"FireplaceQu\"]  = data[\"FireplaceQu\"].fillna(\"None\")\n",
        "\n",
        "data[\"GarageType\"]   = data[\"GarageType\"].fillna(\"None\")\n",
        "data[\"GarageYrBlt\"]  = data[\"GarageYrBlt\"].fillna(0)\n",
        "data[\"GarageFinish\"] = data[\"GarageFinish\"].fillna(\"None\")\n",
        "data[\"GarageCars\"] = data[\"GarageCars\"].fillna(0)\n",
        "data[\"GarageArea\"] = data[\"GarageArea\"].fillna(0)\n",
        "data[\"GarageQual\"] = data[\"GarageQual\"].fillna(\"None\")\n",
        "data[\"GarageCond\"] = data[\"GarageCond\"].fillna(\"None\")\n",
        "\n",
        "data[\"PoolQC\"] = data[\"PoolQC\"].fillna(\"None\")\n",
        "data[\"Fence\"]  = data[\"Fence\"].fillna(\"None\")\n",
        "data[\"MiscFeature\"] = data[\"MiscFeature\"].fillna(\"None\")\n",
        "data['SaleType']    = data['SaleType'].fillna(data['SaleType'].mode()[0])\n",
        "data['LotFrontage'].interpolate(method='linear',inplace=True)\n",
        "data[\"Electrical\"]  = data.groupby(\"YearBuilt\")['Electrical'].transform(lambda x: x.fillna(x.mode()[0]))\n",
        "data[\"Alley\"] = data[\"Alley\"].fillna(\"None\")\n",
        "\n",
        "data[\"MasVnrType\"] = data[\"MasVnrType\"].fillna(\"None\")\n",
        "data[\"MasVnrArea\"] = data[\"MasVnrArea\"].fillna(0)\n",
        "nans=pd.isnull(data).sum()\n",
        "nans[nans>0]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalePrice    1459\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TM0L0w2uqTgd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_list = []\n",
        "for col in data.columns:\n",
        "    if type(data[col][0]) == type('str'): \n",
        "        _list.append(col)\n",
        "\n",
        "le = preprocessing.LabelEncoder()\n",
        "for li in _list:\n",
        "    le.fit(list(set(data[li])))\n",
        "    data[li] = le.transform(data[li])\n",
        "\n",
        "train, test = data[:len(train)], data[len(train):]\n",
        "\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "\n",
        "test = test.drop(columns=['SalePrice', 'Id'])"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWZFw_HRqTgh",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "aDItInwDqTgj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5f78f060-16b6-4fc4-9205-ba7471b1353c"
      },
      "source": [
        "kfold = KFold(n_splits=5, random_state = 2020, shuffle = True)\n",
        "\n",
        "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
        "                              learning_rate=0.05, n_estimators=720,\n",
        "                              max_bin = 55, bagging_fraction = 0.8,\n",
        "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
        "                              feature_fraction_seed=9, bagging_seed=9,\n",
        "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)\n",
        "model_lgb.fit(X, y)\n",
        "r2_score(model_lgb.predict(X), y)\n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9688435166936553"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbe2Q41qqTgm",
        "colab_type": "text"
      },
      "source": [
        "# **XGBoost**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LORRqk2VqTgm",
        "colab_type": "text"
      },
      "source": [
        "**XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks.It is a perfect combination of software and hardware optimization techniques to yield superior results using less computing resources in the shortest amount of time.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umbtjReFqTgm",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "SZ-A-REdqTgn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "7be9cf89-e2b0-498f-dcdd-ac4c79c17277"
      },
      "source": [
        "import xgboost as xgb\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2yZh5CqpqTgo",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YIryUNUmqTgp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d94b0cdb-c9a3-43f6-a267-2b5ef68b298b"
      },
      "source": [
        "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, \n",
        "                             learning_rate=0.05, max_depth=3, \n",
        "                             min_child_weight=1.7817, n_estimators=2200,\n",
        "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
        "                             subsample=0.5213, silent=1,\n",
        "                             random_state =7, nthread = -1)\n",
        "model_xgb.fit(X, y)\n",
        "r2_score(model_xgb.predict(X), y)\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9980427929339806"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LiLtht6qTgr",
        "colab_type": "text"
      },
      "source": [
        "# Catboost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-JX26FqqTgr",
        "colab_type": "text"
      },
      "source": [
        "**Catboost is a type of gradient boosting algorithms which can  automatically deal with categorical variables without showing the type conversion error, which helps you to focus on tuning your model better rather than sorting out trivial errors.Make sure you handle missing data well before you proceed with the implementation.\n",
        "**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnIivV3-qTgr",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1IgzuF-wpRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "539e0a41-98b7-47e7-a08f-7a9a488b8dfc"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/aa/e61819d04ef2bbee778bf4b3a748db1f3ad23512377e43ecfdc3211437a0/catboost-0.23.2-cp36-none-manylinux1_x86_64.whl (64.8MB)\n",
            "\u001b[K     |████████████████████████████████| 64.8MB 60kB/s \n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.0.5)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.5)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.23.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "l17zCnJNqTgs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "125e46e9-6707-4bb2-fb99-87f377540d66"
      },
      "source": [
        "from catboost import CatBoostRegressor\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kVxEw1BbqTgu",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "jObuTHP2qTgu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "b11f85ef-ed9f-4759-a7e0-8bdd74c541f2"
      },
      "source": [
        "cb_model = CatBoostRegressor(iterations=500,\n",
        "                             learning_rate=0.05,\n",
        "                             depth=10,\n",
        "                             random_seed = 42,\n",
        "                             bagging_temperature = 0.2,\n",
        "                             od_type='Iter',\n",
        "                             metric_period = 50,\n",
        "                             od_wait=20)\n",
        "cb_model.fit(X, y)\n",
        "r2_score(cb_model.predict(X), y)\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0:\tlearn: 76972.0985628\ttotal: 106ms\tremaining: 52.7s\n",
            "50:\tlearn: 26415.2641368\ttotal: 2.41s\tremaining: 21.2s\n",
            "100:\tlearn: 16709.9071671\ttotal: 4.68s\tremaining: 18.5s\n",
            "150:\tlearn: 13122.2560081\ttotal: 7s\tremaining: 16.2s\n",
            "200:\tlearn: 10679.6814896\ttotal: 9.31s\tremaining: 13.9s\n",
            "250:\tlearn: 9032.4332082\ttotal: 11.6s\tremaining: 11.5s\n",
            "300:\tlearn: 7635.2922883\ttotal: 13.9s\tremaining: 9.22s\n",
            "350:\tlearn: 6447.9765142\ttotal: 16.3s\tremaining: 6.9s\n",
            "400:\tlearn: 5348.0870745\ttotal: 18.6s\tremaining: 4.59s\n",
            "450:\tlearn: 4444.7443814\ttotal: 20.9s\tremaining: 2.27s\n",
            "499:\tlearn: 3768.1753183\ttotal: 23.2s\tremaining: 0us\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9977125026795101"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3i9ZxDZaqTgx",
        "colab_type": "text"
      },
      "source": [
        "# Stochastic Gradient Descent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnFTck4ZqTgy",
        "colab_type": "text"
      },
      "source": [
        "**Stochastic means random , so in Stochastic Gradient Descent dataset sample is choosedn random instead of the whole dataset.hough, using the whole dataset is really useful for getting to the minima in a less noisy or less random manner, but the problem arises when our datasets get really huge and for that SGD come in action**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjhdFCs3qTgy",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "fp4Sb-yKqTgy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "73baf1fa-4d87-439e-b112-26cf1ca82b33"
      },
      "source": [
        "from sklearn.linear_model import SGDRegressor\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZqVRZfhqTg3",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "zeuQEQ7aqTg3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2d75efb0-42cc-4d43-9ae6-9ab1cd3cca15"
      },
      "source": [
        "SGD = SGDRegressor(max_iter = 100)\n",
        "SGD.fit(X, y)\n",
        "r2_score(SGD.predict(X), y)\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-2.3163273929324584"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a64b7-gTqTg5",
        "colab_type": "text"
      },
      "source": [
        "# Lasso"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHWDlZd1qTg5",
        "colab_type": "text"
      },
      "source": [
        "**In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. Though originally defined for least squares, lasso regularization is easily extended to a wide variety of statistical models including generalized linear models, generalized estimating equations, proportional hazards models, and M-estimators, in a straightforward fashion**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wng6g7FDqTg5",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "k6OmIvrTqTg6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "15f0bf5d-bcb2-43d8-cc4a-09901625912b"
      },
      "source": [
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwOMYGyrqTg9",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "xK00-CyRqTg9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "6890721f-0442-4507-d4d4-c9057c4b1939"
      },
      "source": [
        "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\n",
        "lasso.fit(X, y)\n",
        "r2_score(lasso.predict(X), y)\n"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 647368140955.3564, tolerance: 920791133.4609975\n",
            "  positive)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8293764275536555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXzcrD4XqThA",
        "colab_type": "text"
      },
      "source": [
        "# Ridge Classifier CV"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzaNg-LCqThA",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "2rh5HJGfqThB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "e15ec5ee-1ca4-464a-d50f-8fe5d784a7be"
      },
      "source": [
        "from sklearn.linear_model import RidgeClassifierCV\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHF3VCL-qThC",
        "colab_type": "text"
      },
      "source": [
        "**Model and  Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "VUWwWu42qThC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "41bccf0f-dbf2-4386-cf24-50d6af854a98"
      },
      "source": [
        "rcc = RidgeClassifierCV()\n",
        "rcc.fit(X, y)\n",
        "r2_score(rcc.predict(X), y)\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6662347422006982"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vySr34glqThD",
        "colab_type": "text"
      },
      "source": [
        "# Kernel Ridge Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2OfED1iqThE",
        "colab_type": "text"
      },
      "source": [
        "**KRR combine Ridge regression and classification with the kernel trick.It is similar to Support vector Regression but relatively very fast.This is suitable for smaller dataset (less than 100 samples)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7gyzMREfqThE",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "EkTEbXklqThE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "43013470-7b21-4132-8c7f-6fd1c0ecddad"
      },
      "source": [
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jv1M6XKqThF",
        "colab_type": "text"
      },
      "source": [
        "**Model and  Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "bSBNIsljqThG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "332cd409-5731-4d98-b1d3-bb8cf904a6a5"
      },
      "source": [
        "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)\n",
        "KRR.fit(X, y)\n",
        "r2_score(KRR.predict(X), y)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=2.70405e-20): result may not be accurate.\n",
            "  overwrite_a=False)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9857117975859181"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGiZCpSzqThK",
        "colab_type": "text"
      },
      "source": [
        "# BayesianRidge"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anyMGH-UqThK",
        "colab_type": "text"
      },
      "source": [
        "** Bayesian regression, is a regression model defined in probabilistic terms, with explicit priors on the parameters. The choice of priors can have the regularizing effect.Bayesian approach is a general way of defining and estimating statistical models that can be applied to different models.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guYSqNYiqThK",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "ZsT3ceZZqThL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "6891b5f8-0bd6-4292-add9-fca6a5c4cba0"
      },
      "source": [
        "from sklearn.linear_model  import BayesianRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qrHZsmEoqThN",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "KCLASaQAqThN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6923d498-3476-4913-d30e-5c484d4fe500"
      },
      "source": [
        "BR = BayesianRidge()\n",
        "BR.fit(X, y)\n",
        "r2_score(BR.predict(X), y)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.816756081496668"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGTIKx3EqThR",
        "colab_type": "text"
      },
      "source": [
        "# Elastic Net Regression \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euU7DIHkqThS",
        "colab_type": "text"
      },
      "source": [
        "**Elastic net is a hybrid of ridge regression and lasso regularization.It combines feature elimination from Lasso and feature coefficient reduction from the Ridge model to improve your model's predictions.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vd2CtvWVqThS",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "D1LP8rSUqThS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "29cb3ffc-fdc2-46d3-e2f9-90e1f60df05d"
      },
      "source": [
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "#Data is used the same as LGB\n",
        "X = train.drop(columns=['SalePrice', 'Id']) \n",
        "y = train['SalePrice']\n",
        "X.head()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   MSSubClass  MSZoning  LotFrontage  ...  YrSold  SaleType  SaleCondition\n",
              "0          60         3         65.0  ...    2008         8              4\n",
              "1          20         3         80.0  ...    2007         8              4\n",
              "2          60         3         68.0  ...    2008         8              4\n",
              "3          70         3         60.0  ...    2006         8              0\n",
              "4          60         3         84.0  ...    2008         8              4\n",
              "\n",
              "[5 rows x 79 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0pW40zUqThV",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "8N3_FZgFqThV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "91f4a916-d205-4391-ac30-56f6582c10e3"
      },
      "source": [
        "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\n",
        "ENet.fit(X, y)\n",
        "r2_score(ENet.predict(X), y)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 671510109304.9265, tolerance: 920791133.4609975\n",
            "  positive)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8293329562200301"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wpcUE_yXqThW",
        "colab_type": "text"
      },
      "source": [
        "# **LDA**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_OGE5HbqThX",
        "colab_type": "text"
      },
      "source": [
        "**A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.The model fits a Gaussian density to each class, assuming that all classes share the same covariance matrix.Itis  used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1R2cgjaqqThX",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "yNTm8-iaqThX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "103d5b8d-df3d-4abe-b812-571b9bf140c1"
      },
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "lda = data\n",
        "lda.head()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>...</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>196.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>706.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>548.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>208500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>284.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>181500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>486.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>223500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>140000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>3</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>350.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>655.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass  MSZoning  ...  SaleType  SaleCondition  SalePrice\n",
              "0   1          60         3  ...         8              4   208500.0\n",
              "1   2          20         3  ...         8              4   181500.0\n",
              "2   3          60         3  ...         8              4   223500.0\n",
              "3   4          70         3  ...         8              0   140000.0\n",
              "4   5          60         3  ...         8              4   250000.0\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgnq7n31qThY",
        "colab_type": "text"
      },
      "source": [
        "**Model and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9JJMBa59qThY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf002e4d-fdc8-4f95-9bf3-737dfd7d7d7e"
      },
      "source": [
        "X = lda.iloc[:, [2,3]].values\n",
        "y = lda.iloc[:, 4].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)\n",
        "Model=LinearDiscriminantAnalysis()\n",
        "Model.fit(X_train,y_train)\n",
        "y_pred=Model.predict(X_test)\n",
        "print('accuracy is ',accuracy_score(y_pred,y_test))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy is  0.0273972602739726\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6K23xSeqThZ",
        "colab_type": "text"
      },
      "source": [
        "# K-Means Algorithm \n",
        "K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data and the goal of this algorithm is to find groups in the data \n",
        "\n",
        "**Steps to use this algorithm:-**\n",
        "* 1-Clusters the data into k groups where k is predefined. \n",
        "* 2-Select k points at random as cluster centers. \n",
        "* 3-Assign objects to their closest cluster center according to the Euclidean distance function. \n",
        "* 4-Calculate the centroid or mean of all objects in each cluster. \n",
        "\n",
        "**Examples: Behavioral segmentation like segment by purchase history or by activities on application, website, or platform Separate valid activity groups from bots  **\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8z2nM-VqThZ",
        "colab_type": "text"
      },
      "source": [
        "**Libraries and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "PxdyuMbnqTha",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "bd8c6492-176b-4937-da57-626311da999a"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "km = pd.read_csv(\"/content/datasets_527325_1332417_Region.csv\")\n",
        "km.head()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code</th>\n",
              "      <th>province</th>\n",
              "      <th>city</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>elementary_school_count</th>\n",
              "      <th>kindergarten_count</th>\n",
              "      <th>university_count</th>\n",
              "      <th>academy_ratio</th>\n",
              "      <th>elderly_population_ratio</th>\n",
              "      <th>elderly_alone_ratio</th>\n",
              "      <th>nursing_home_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000</td>\n",
              "      <td>Seoul</td>\n",
              "      <td>Seoul</td>\n",
              "      <td>37.566953</td>\n",
              "      <td>126.977977</td>\n",
              "      <td>607</td>\n",
              "      <td>830</td>\n",
              "      <td>48</td>\n",
              "      <td>1.44</td>\n",
              "      <td>15.38</td>\n",
              "      <td>5.8</td>\n",
              "      <td>22739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10010</td>\n",
              "      <td>Seoul</td>\n",
              "      <td>Gangnam-gu</td>\n",
              "      <td>37.518421</td>\n",
              "      <td>127.047222</td>\n",
              "      <td>33</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>4.18</td>\n",
              "      <td>13.17</td>\n",
              "      <td>4.3</td>\n",
              "      <td>3088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10020</td>\n",
              "      <td>Seoul</td>\n",
              "      <td>Gangdong-gu</td>\n",
              "      <td>37.530492</td>\n",
              "      <td>127.123837</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>1.54</td>\n",
              "      <td>14.55</td>\n",
              "      <td>5.4</td>\n",
              "      <td>1023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10030</td>\n",
              "      <td>Seoul</td>\n",
              "      <td>Gangbuk-gu</td>\n",
              "      <td>37.639938</td>\n",
              "      <td>127.025508</td>\n",
              "      <td>14</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0.67</td>\n",
              "      <td>19.49</td>\n",
              "      <td>8.5</td>\n",
              "      <td>628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10040</td>\n",
              "      <td>Seoul</td>\n",
              "      <td>Gangseo-gu</td>\n",
              "      <td>37.551166</td>\n",
              "      <td>126.849506</td>\n",
              "      <td>36</td>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1.17</td>\n",
              "      <td>14.39</td>\n",
              "      <td>5.7</td>\n",
              "      <td>1080</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    code province  ... elderly_alone_ratio  nursing_home_count\n",
              "0  10000    Seoul  ...                 5.8               22739\n",
              "1  10010    Seoul  ...                 4.3                3088\n",
              "2  10020    Seoul  ...                 5.4                1023\n",
              "3  10030    Seoul  ...                 8.5                 628\n",
              "4  10040    Seoul  ...                 5.7                1080\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooMlZmXwyOQh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "km = km.loc[:,[\"latitude\",\"longitude\"]]"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6do3c5EuqThb",
        "colab_type": "text"
      },
      "source": [
        "**Checking for number of clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "CEjGVmBVqThb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "c8efb994-da7d-4be0-cbca-8ddce991e630"
      },
      "source": [
        "K_clusters = range(1,8)\n",
        "kmeans = [KMeans(n_clusters=i) for i in K_clusters]\n",
        "Y_axis = km[['latitude']]\n",
        "X_axis = km[['longitude']]\n",
        "score = [kmeans[i].fit(Y_axis).score(Y_axis) for i in range(len(kmeans))]\n",
        "plt.plot(K_clusters, score)\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('Score')\n",
        "plt.show()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iOEVjcSorjOvnjgu7FBGRrI80vgZcARwBcPeXgel5qkkC7k40FueqmU2MHlEVdjkiIlmHRq+7Hz2trSTX3i4kW1uP8/rBE7pqSkQKRraLMMXM7ONAxMxmAXcCz+avLAFYuTGOGdw4X6EhIoUh2yONLwILgG7gp8BR4Mv5KkrSorFWLpt2AeNG1oZdiogIkMWRhplFgF+6+/XA3fkvSQDeOHiCzfuPcfe/mxd2KSIip5zzSMPdk0DKzEYPQz0SiMbiALrUVkQKSrbnNDqAV83sCaCzr9Hd78xLVUI0FmfexFFMa6wLuxQRkVOyDY1/CW4yDA4c72LDG4f50g2zwi5FRKSfrELD3X9kZtXA7KBpq7v35q+s8vbEplbc0doZIlJwsgoNM1sC/AjYDRgw1cz+xN2fzl9p5Ssaa+XCxjrmjB8ZdikiIv1kOzz1LeAmd98KYGazgZ8Bl+WrsHJ19GQvz+1o55NXT8dMy7qKSGHJ9ncaVX2BAeDu2wDNa5EHa7YcoDfpumpKRApStkca683sfuAnweM/BNbnp6TyFo3FGTeyhndOHRN2KSIib5PtkcbngE2kpw+5M7j/uXwVVa66epM8tbWNG+ePp6JCQ1MiUniyDY1K4G/d/RZ3vwX4LjDoFYHM7CNmFjOzlJktzmhvMbOTZvZycLsvY9tlZvaqmW03s+9aCQ74P72tjZO9SV01JSIFK9vQeBIYkfF4BLB6CJ+7EbgFGOjqqx3ufmlwuyOj/V7gM8Cs4LZ8CJ9fkKKxVkbVVvKuixrDLkVEZEDZhkatu3f0PQjuD/qnyu6+OfPE+rmY2URglLuvc3cHfgx8aLCfX4h6kylWb25l6bzxVEW0Cq+IFKZse6dOM1vU9yAYUjqZn5KYbmYvmdlaM3t30DYZ2JvxnL1B24DM7HYzW29m69va2vJUZm49v+sQR0/2cpOumhKRApbt1VNfBn5uZm8GjycCHz3bC8xsNTBQD3i3uz96hpftB6a5+0Ezuwz4VzNbkGWNp7j7CmAFwOLFi4tisahoLE5tVQXXzW4OuxQRkTM6a2iY2eXAHnd/wczmAp8lfS5iJbDrbK9196XnW4y7d5NeswN332BmO0hPXbIPmJLx1ClBW0lIpdLLul43u5kR1YO+vkBEJO/ONTz190BPcP9K4C+B7wOHCb7J55KZNQfrd2BmF5E+4b3T3fcDx8zsXcFVU7cBZzpaKTq/23uE1mPd+kGfiBS8c4VGxN0PBfc/Cqxw94fd/T8CMwf7oWb2YTPbSzqIfmlm0WDTtcArZvYy8AvgjozP/1PgfmA7sAP41WA/v9BEY61UVhg3zNWyriJS2M51TiNiZpXungBuAG4/j9eekbs/AjwyQPvDwMNneM16YOFgP7NQuaeHpq6c0cjoOs3MIiKF7Vwd/8+AtWbWTvpqqV8DmNlM0uuEyxC9dqCDXe2dfOqa6WGXIiJyTmcNDXe/x8yeJH211KrgNxKQHtb6Yr6LKwcrN6aXdb1pvoamRKTwnXOIyd3XDdC2LT/llJ9oLM6iaWMYP6o27FJERM5JPz0O0Z5DJ4i9eUxXTYlI0VBohCgaSw9NKTREpFgoNEK0KtbK3AkjaWmqD7sUEZGsKDRC0na8mxdeP6S5pkSkqCg0QrJ6cyvusFyhISJFRKERkmgsztSxI5g3cWTYpYiIZE2hEYJjXb08u/0gy+ZPoAQXIBSREqbQCMGaLQfoSaa0rKuIFB2FRghWxVppaqhh0bQLwi5FROS8KDSGWVdvkjVbD3Dj/PFUVGhoSkSKi0JjmD3zWjsnepIamhKRoqTQGGbRWJyRtZVceVFj2KWIiJw3hcYwSiRTrN7cyg1zx1Fdqf/0IlJ81HMNo+d3H+LwiV7NNSUiRUuhMYxWxVqpqazgujnNYZciIjIoCo1h0res67Wzm6mrHvRKuSIioVJoDJNX9h5l/9EuDU2JSFFTaAyTaCxOpMJYOm9c2KWIiAyaQmOYrIzFeddFYxlTVx12KSIig6bQGAbbDxxnZ1unhqZEpOgpNIZBNNYKwE3zFRoiUtwUGsNg5cY4l04dw4TRtWGXIiIyJAqNPNt35CSv7juqoSkRKQkKjTyLbowDsGzB+JArEREZOoVGnkVjcWaPb+Ci5oawSxERGbJQQsPMvmlmW8zsFTN7xMzGZGy7y8y2m9lWM1uW0b48aNtuZl8No+7zdbCjmxd2H9LQlIiUjLCONJ4AFrr7xcA24C4AM5sP3AosAJYDf2dmETOLAN8HbgbmAx8LnlvQVm9uJeUoNESkZIQSGu6+yt0TwcN1wJTg/geBB9292913AduBK4Lbdnff6e49wIPBcwtaNNbK5DEjWDBpVNiliIjkRCGc0/gU8Kvg/mRgT8a2vUHbmdoHZGa3m9l6M1vf1taW43Kz09Gd4JnX2lm2YAJmWtZVREpD3qZbNbPVwEDjMne7+6PBc+4GEsADufxsd18BrABYvHix5/K9s7VmywF6kikt6yoiJSVvoeHuS8+23cw+AbwPuMHd+zr2fcDUjKdNCdo4S3tBisbiNNZXc9mFF4RdiohIzoR19dRy4M+BD7j7iYxNjwG3mlmNmU0HZgHPAy8As8xsuplVkz5Z/thw152trt4ka7Yc4Mb544lUaGhKREpHWKsBfQ+oAZ4IxvvXufsd7h4zs4eATaSHrT7v7kkAM/sCEAUiwA/dPRZO6ef27I52OnuSLNPQlIiUmFBCw91nnmXbPcA9A7Q/Djyez7pyJbqxlYaaSq6a0Rh2KSIiOVUIV0+VlGTKeWJzK++ZO46aykjY5YiI5JRCI8de2H2IQ509+kGfiJQkhUaORWNxqisrWDKnOexSRERyTqGRQ+7Oqlgr185qor4mrGsMRETyR6GRQxv3HWPfkZPcpKEpESlRCo0cisbiVBgsnae1M0SkNCk0cmhlLM7vTW9kbH112KWIiOSFQiNHdrR1sP1Ah1boE5GSptDIkWgsvayrzmeISClTaORIdGOcS6aMZtKYEWGXIiKSNwqNHNh/9CS/23tURxkiUvIUGjmwKtYKoLUzRKTkKTRyYOXGODPHNTCjuSHsUkRE8kqhMUSHOnt4fvchXTUlImVBoTFEqze3kkw5yxdMDLsUEZG8U2gM0apYnMljRrBw8qiwSxERyTuFxhB0did4+rV2bpw/nmAFQhGRkqbQGIKntrbRk0jpqikRKRsKjSGIxuKMra/m8paxYZciIjIsFBqD1J1IsmbLAZbOG0ekQkNTIlIeFBqD9OyOgxzvTmhoSkTKikJjkFbF4tRXR7hqRlPYpYiIDBuFxiAkU84Tm1q5fu44aqsiYZcjIjJsFBqDsOH1w7R39LBMExSKSJlRaAxCNBanOlLBkjnNYZciIjKsFBrnyd2JxuJcM6uJkbVVYZcjIjKsFBrnKfbmMfYePqkJCkWkLCk0ztOqWJwKg6XzFBoiUn5CCQ0z+6aZbTGzV8zsETMbE7S3mNlJM3s5uN2X8ZrLzOxVM9tuZt+1kCZ7WhmLc3nLWBobasL4eBGRUIV1pPEEsNDdLwa2AXdlbNvh7pcGtzsy2u8FPgPMCm7Lh63awK72Tra1duiqKREpW6GEhruvcvdE8HAdMOVszzezicAod1/n7g78GPhQnst8m2gsDsBNOp8hImWqEM5pfAr4Vcbj6Wb2kpmtNbN3B22Tgb0Zz9kbtA3IzG43s/Vmtr6trS1nha7cGOcdk0cz5YK6nL2niEgxyVtomNlqM9s4wO2DGc+5G0gADwRN+4Fp7v5O4CvAT83svFc3cvcV7r7Y3Rc3N+fmtxTxo128vOeIrpoSkbJWma83dvelZ9tuZp8A3gfcEAw54e7dQHdwf4OZ7QBmA/voP4Q1JWgbNk9sSg9N6XyGiJSzsK6eWg78OfABdz+R0d5sZpHg/kWkT3jvdPf9wDEze1dw1dRtwKPDWfPKWJyLmuuZOa5hOD9WRKSghHVO43vASOCJ0y6tvRZ4xcxeBn4B3OHuh4JtfwrcD2wHdtD/PEheHTnRw7qdh1i2YIKWdRWRspa34amzcfeZZ2h/GHj4DNvWAwvzWdeZPLn5AMmUs1xDUyJS5grh6qmCtzIWZ+LoWi6eMjrsUkREQqXQOIcTPQme3tbGTfPHa2hKRMqeQuMc1m5tozuRYpmWdRURUWicSzQW54K6Kq5oGRt2KSIioVNonEVPIsWTWw5ww7zxVEb0n0pERD3hWTy38yDHuxK6akpEJKDQOItoLE5ddYRrZjWFXYqISEFQaJxBMuWsirWyZE4ztVWRsMsRESkICo0zeOmNw7R3dGuuKRGRDAqNM4jG4lRFjOvnjgu7FBGRgqHQGIC7E421cvXMJkbVVoVdjohIwQhl7qlC19Wb4sqLGrlqZmPYpYiIFBSFxgBGVEf4+u9fHHYZIiIFR8NTIiKSNYWGiIhkTaEhIiJZU2iIiEjWFBoiIpI1hYaIiGRNoSEiIllTaIiISNbM3cOuIa/MrA14fZAvbwLac1hOmEplX0plP0D7UohKZT9gaPtyobs3D7Sh5ENjKMxsvbsvDruOXCiVfSmV/QDtSyEqlf2A/O2LhqdERCRrCg0REcmaQuPsVoRdQA6Vyr6Uyn6A9qUQlcp+QJ72Rec0REQkazrSEBGRrCk0REQkawqNAZjZD83sgJltDLuWoTCzqWa2xsw2mVnMzL4Udk2DZWa1Zva8mf0u2Jf/EnZNQ2FmETN7ycz+LexahsLMdpvZq2b2spmtD7ueoTCzMWb2CzPbYmabzezKsGsaDDObE/x79N2OmdmXc/b+OqfxdmZ2LdAB/NjdF4Zdz2CZ2URgoru/aGYjgQ3Ah9x9U8ilnTczM6De3TvMrAp4BviSu68LubRBMbOvAIuBUe7+vrDrGSwz2w0sdvei/0Gcmf0I+LW7329m1UCdux8Ju66hMLMIsA/4PXcf7I+c+9GRxgDc/WngUNh1DJW773f3F4P7x4HNwORwqxocT+sIHlYFt6L8xmNmU4D3AveHXYukmdlo4FrgBwDu3lPsgRG4AdiRq8AAhUbZMLMW4J3Ab8OtZPCCIZ2XgQPAE+5erPvyHeDPgVTYheSAA6vMbIOZ3R52MUMwHWgD/jEYNrzfzOrDLioHbgV+lss3VGiUATNrAB4Gvuzux8KuZ7DcPenulwJTgCvMrOiGDs3sfcABd98Qdi05co27LwJuBj4fDO0Wo0pgEXCvu78T6AS+Gm5JQxMMsX0A+Hku31ehUeKC8f+HgQfc/V/CricXgmGDNcDysGsZhKuBDwTnAh4E3mNmPwm3pMFz933B3wPAI8AV4VY0aHuBvRlHr78gHSLF7GbgRXdvzeWbKjRKWHDy+AfAZnf/dtj1DIWZNZvZmOD+COBGYEu4VZ0/d7/L3ae4ewvpoYP/5+5/FHJZg2Jm9cEFFgRDOTcBRXnFobvHgT1mNidougEougtGTvMxcjw0BelDMjmNmf0MWAI0mdle4D+7+w/CrWpQrgb+GHg1OBcA8Jfu/niINQ3WROBHwdUgFcBD7l7Ul6uWgPHAI+nvJlQCP3X3leGWNCRfBB4IhnV2Ap8MuZ5BC0L8RuCzOX9vXXIrIiLZ0vCUiIhkTaEhIiJZU2iIiEjWFBoiIpI1hYaIiGRNoSFFzczczL6V8fjPzOxrOXrvfzKz38/Fe53jcz4SzKq6ZoBts83scTN7zcxeNLOHzGy8mS0Z7Ay5ZvZlM6sbeuVSjhQaUuy6gVvMrCnsQjKZ2fn8BurTwGfc/frT3qMW+CXpqS1mBdN1/B3QPMTyvgycV2gEv48RUWhI0UuQXgv535++4fQjBTPrCP4uMbO1Zvaome00s/9pZn8YrNfxqpnNyHibpWa23sy2BfNG9U2c+E0ze8HMXjGzz2a876/N7DEG+DWxmX0seP+NZvb1oO0/AdcAPzCzb572ko8Dz7n7/+1rcPen3L3fr67N7Gtm9mcZjzeaWUvwi+1fBmuQbDSzj5rZncAkYE3fkY2Z3WRmzwVHMj8P5irrWyvj62b2IvARM7vT0muzvGJmD57j30VKlH4RLqXg+8ArZvaN83jNJcA80lPg7wTud/crLL1Q1RdJfxsHaCE9n9IM0h3tTOA24Ki7X25mNcBvzGxV8PxFwEJ335X5YWY2Cfg6cEdN/jwAAAJ6SURBVBlwmPTMsB9y9782s/cAf+bupy9itJD0GiiDtRx4093fG9Qw2t2PBmt5XO/u7cER2l8BS92908z+AvgK8NfBexwMjnAwszeB6e7e3Teli5QfHWlI0Qtm7v0xcOd5vOyFYL2RbmAH0Nfpv0o6KPo85O4pd3+NdLjMJT3H0m3B1Cy/BRqBWcHznz89MAKXA0+5e5u7J4AHSK/fkE+vAjcGRwvvdvejAzznXcB80sH3MvAnwIUZ2/854/4rpKfZ+CPSR3hShhQaUiq+Q/rcQOYaCAmC/42bWQVQnbGtO+N+KuNxiv5H4KfPs+OAAV9090uD23R37wudziHtRX8x0kcm53JqPwO1AO6+jfSRz6vAfwuGwk5npNcm6duX+e7+6YztmfvzXtJHdYuAF87zvI2UCIWGlAR3PwQ8RDo4+uzmrU73A6RX+ztfHzGziuA8x0XAViAKfC6Ydr7vCqdzLdjzPHCdmTUFJ5U/Bqw9x2t+ClxlZu/tazCza+3t64jsJpjG28wWkV5QqG9I7IS7/wT4Jm9N9X0cGBncXwdcHQy79c1cO/v0QoLQnerua4C/AEYDDeeoX0qQvilIKfkW8IWMx/8APGpmvwNWMrijgDdId/ijgDvcvcvM7ic9hPWipad4bQM+dLY3cff9ZvZV0uuAGPBLd3/0HK85GZx8/46ZfQfoJT1E9CUg82qxh0kPl8VID5dtC9rfAXzTzFLBaz8XtK8AVprZm+5+vZl9AvhZcH4G0uc4ttFfBPiJpZdFNeC7JbIcqpwnzXIrIiJZ0/CUiIhkTaEhIiJZU2iIiEjWFBoiIpI1hYaIiGRNoSEiIllTaIiISNb+P5MUGDfAeKVtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4XFiGF_qThc",
        "colab_type": "text"
      },
      "source": [
        "**Fitting Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weTkI0xtymFV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "ec81900a-5926-4352-c506-98b55b106488"
      },
      "source": [
        "km.head()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37.566953</td>\n",
              "      <td>126.977977</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37.518421</td>\n",
              "      <td>127.047222</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37.530492</td>\n",
              "      <td>127.123837</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.639938</td>\n",
              "      <td>127.025508</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>37.551166</td>\n",
              "      <td>126.849506</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    latitude   longitude  cluster_label\n",
              "0  37.566953  126.977977              0\n",
              "1  37.518421  127.047222              0\n",
              "2  37.530492  127.123837              0\n",
              "3  37.639938  127.025508              0\n",
              "4  37.551166  126.849506              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "wkSCL3xhqThc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "165375fd-4feb-4b58-ce94-c6eadee6fef0"
      },
      "source": [
        "kmeans = KMeans(n_clusters = 3, init ='k-means++')\n",
        "kmeans.fit(km[km.columns[0:1]])\n",
        "km['cluster_label'] = kmeans.fit_predict(km[km.columns[0:1]])\n",
        "centers = kmeans.cluster_centers_\n",
        "labels = kmeans.predict(km[km.columns[0:1]])\n",
        "km.cluster_label.unique()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXiTy138qThe",
        "colab_type": "text"
      },
      "source": [
        "**Plotting Clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "oKbyWKLXqThe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3c0ba82e-113b-42ef-8a60-5c47764e86e1"
      },
      "source": [
        "km.plot.scatter(x = 'latitude', y = 'longitude', c=labels, s=50, cmap='viridis')\n",
        "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=100, alpha=0.5)\n"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-85-023e6002acef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mkm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'latitude'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'longitude'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcenters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 1 with size 1"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_h3J2K3eqThg",
        "colab_type": "text"
      },
      "source": [
        "# CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRMFRSd2qThi",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "wmK-vumnqThi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "c2ed3648-a7d2-47a9-a1b8-590868253d41"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
        "import tensorflow as tf\n",
        "train_data = pd.read_csv(\"/train.csv\")\n",
        "test_data = pd.read_csv(\"/test.csv\")\n",
        "train_data.head()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>pixel0</th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
              "      <th>pixel7</th>\n",
              "      <th>pixel8</th>\n",
              "      <th>pixel9</th>\n",
              "      <th>pixel10</th>\n",
              "      <th>pixel11</th>\n",
              "      <th>pixel12</th>\n",
              "      <th>pixel13</th>\n",
              "      <th>pixel14</th>\n",
              "      <th>pixel15</th>\n",
              "      <th>pixel16</th>\n",
              "      <th>pixel17</th>\n",
              "      <th>pixel18</th>\n",
              "      <th>pixel19</th>\n",
              "      <th>pixel20</th>\n",
              "      <th>pixel21</th>\n",
              "      <th>pixel22</th>\n",
              "      <th>pixel23</th>\n",
              "      <th>pixel24</th>\n",
              "      <th>pixel25</th>\n",
              "      <th>pixel26</th>\n",
              "      <th>pixel27</th>\n",
              "      <th>pixel28</th>\n",
              "      <th>pixel29</th>\n",
              "      <th>pixel30</th>\n",
              "      <th>pixel31</th>\n",
              "      <th>pixel32</th>\n",
              "      <th>pixel33</th>\n",
              "      <th>pixel34</th>\n",
              "      <th>pixel35</th>\n",
              "      <th>pixel36</th>\n",
              "      <th>pixel37</th>\n",
              "      <th>pixel38</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel744</th>\n",
              "      <th>pixel745</th>\n",
              "      <th>pixel746</th>\n",
              "      <th>pixel747</th>\n",
              "      <th>pixel748</th>\n",
              "      <th>pixel749</th>\n",
              "      <th>pixel750</th>\n",
              "      <th>pixel751</th>\n",
              "      <th>pixel752</th>\n",
              "      <th>pixel753</th>\n",
              "      <th>pixel754</th>\n",
              "      <th>pixel755</th>\n",
              "      <th>pixel756</th>\n",
              "      <th>pixel757</th>\n",
              "      <th>pixel758</th>\n",
              "      <th>pixel759</th>\n",
              "      <th>pixel760</th>\n",
              "      <th>pixel761</th>\n",
              "      <th>pixel762</th>\n",
              "      <th>pixel763</th>\n",
              "      <th>pixel764</th>\n",
              "      <th>pixel765</th>\n",
              "      <th>pixel766</th>\n",
              "      <th>pixel767</th>\n",
              "      <th>pixel768</th>\n",
              "      <th>pixel769</th>\n",
              "      <th>pixel770</th>\n",
              "      <th>pixel771</th>\n",
              "      <th>pixel772</th>\n",
              "      <th>pixel773</th>\n",
              "      <th>pixel774</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel0  pixel1  pixel2  ...  pixel780  pixel781  pixel782  pixel783\n",
              "0      1       0       0       0  ...         0         0         0         0\n",
              "1      0       0       0       0  ...         0         0         0         0\n",
              "2      1       0       0       0  ...         0         0         0         0\n",
              "3      4       0       0       0  ...         0         0         0         0\n",
              "4      0       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ROZ9sEaqThk",
        "colab_type": "text"
      },
      "source": [
        "**Preprocessing and Data Split**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nO_3QT_lqThk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "591cbeb7-a487-4501-ba97-da51a92fc791"
      },
      "source": [
        "X = np.array(train_data.drop(\"label\", axis=1)).astype('float32')\n",
        "y = np.array(train_data['label']).astype('float32')\n",
        "for i in range(9):\n",
        "    plt.subplot(3,3,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(X[i].reshape(28, 28), cmap=plt.cm.binary)\n",
        "    plt.xlabel(y[i])\n",
        "plt.show()\n",
        "\n",
        "X = X / 255.0\n",
        "X = X.reshape(-1, 28, 28, 1)\n",
        "y = to_categorical(y)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)\n",
        "X_test = np.array(test_data).astype('float32')\n",
        "X_test = X_test / 255.0\n",
        "X_test = X_test.reshape(-1, 28, 28, 1)\n",
        "plt.figure(figsize=(10,10))\n"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A6NGTyVmqThm",
        "colab_type": "text"
      },
      "source": [
        "**Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Tbd2RF5QqThm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fc903fd0-e63a-4c5e-c284-2f69859bdec6"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
        "                 activation ='relu', input_shape = (28,28,1)))\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
        "                 activation ='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation = \"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "model.summary()\n",
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, to_file='model1.png')\n"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 32)        832       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 32)        25632     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               803072    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 887,530\n",
            "Trainable params: 887,530\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QDnOWAsSqThn",
        "colab_type": "text"
      },
      "source": [
        "**Compiling model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "XP-jWUk8qThn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "52038038-a16a-43c3-f5e5-bf9b8d7c5895"
      },
      "source": [
        "#increse to epochs to 30 for better accuracy\n",
        "model.compile(optimizer='adam', loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "history = model.fit(X_train, y_train, epochs=10, batch_size=85, validation_data=(X_val, y_val))"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 33600 samples, validate on 8400 samples\n",
            "Epoch 1/1\n",
            "33600/33600 [==============================] - 225s 7ms/step - loss: 0.2068 - accuracy: 0.9363 - val_loss: 0.0591 - val_accuracy: 0.9831\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "rbgJQR0OqTho",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "da56aa9f-0925-430a-a99e-cc0e44b519c2"
      },
      "source": [
        "accuracy = history.history['accuracy']\n",
        "val_accuracy = history.history['val_accuracy']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.show()\n",
        "\n",
        "print(model.evaluate(X_val, y_val))\n"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ar0y6lmPgg8CfVtXz3Qn+qmLQz1NVv3ykuSTfSvL6qvpmktcD315g2QHg0qH9aWAH8Dagl+TfGfy7vy7Jjqq6lAlbwZ4P2Qo8WVUfH0O5K+EAcO7Q/nQ3ttCa/d0Prp8A/nPEY49Hy+mZJNPAZ4HfrKqnVr7csVhOz78AvCfJ7cDpwA+SfK+qPrnyZY/BpG8SrKYH8FEOvzF5+wJrzmRwHe+M7vF14Mx5a9azem7GLqtnBvcj/ho4YdK9vEKPaxjcQD6fH96ke/O8Nb/H4Tfp7u2238zhN2P3sTpuxi6n59O79Rsn3cex6nnemg+yym7GTryA1fRgcH1yO/Ak8IWhMOsBfzG07rcZ3JSbBX5rgddZTUF/1D0zOGMqYA/wWPf4nUn3dIQ+fxV4gsG7Mm7pxrYAV3bbr2XwbotZ4F+BNwwde0t33F6Ow3cVjbtn4I+A/xn6nj4GvG7S/az093noNVZd0PtfIEhS43zXjSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjft/2yPsKJirb0cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "8400/8400 [==============================] - 13s 2ms/step\n",
            "[0.05909540975954206, 0.9830952286720276]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "trusted": true,
        "id": "x4I8q6iIqThq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = model.predict_classes(X_test)\n"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MC0nkZS8qThr",
        "colab_type": "text"
      },
      "source": [
        "# LSTM "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCtwyZFpqThr",
        "colab_type": "text"
      },
      "source": [
        "**LSTM  blocks are part of a recurrent neural network structure. Recurrent neural networks are made to utilize certain types of artificial memory processes that can help these artificial intelligence programs to more effectively imitate human thought.It is  capable of learning order dependence \n",
        "LSTM can be used for machine translation, speech recognition, and more.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8Z5fk9rqThr",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "lQH7NKs9qThr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "597f3792-6e6d-4990-fd68-38fe08dba812"
      },
      "source": [
        "import math\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "lstm = pd.read_csv(\"/prices.csv\")\n",
        "lstm = lstm[lstm['symbol']==\"NFLX\"]\n",
        "lstm['date'] = pd.to_datetime(lstm['date'])\n",
        "lstm.set_index('date',inplace=True)\n",
        "lstm = lstm.reset_index()\n",
        "lstm.head()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>symbol</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "      <th>low</th>\n",
              "      <th>high</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-01-04</td>\n",
              "      <td>NFLX</td>\n",
              "      <td>55.519999</td>\n",
              "      <td>53.479999</td>\n",
              "      <td>52.960001</td>\n",
              "      <td>55.730000</td>\n",
              "      <td>17239600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-05</td>\n",
              "      <td>NFLX</td>\n",
              "      <td>53.570001</td>\n",
              "      <td>51.510001</td>\n",
              "      <td>50.810001</td>\n",
              "      <td>53.599998</td>\n",
              "      <td>23753100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-01-06</td>\n",
              "      <td>NFLX</td>\n",
              "      <td>51.530001</td>\n",
              "      <td>53.319999</td>\n",
              "      <td>50.380002</td>\n",
              "      <td>53.710001</td>\n",
              "      <td>23290400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-01-07</td>\n",
              "      <td>NFLX</td>\n",
              "      <td>54.120000</td>\n",
              "      <td>52.400001</td>\n",
              "      <td>52.240001</td>\n",
              "      <td>54.300001</td>\n",
              "      <td>9955400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-01-08</td>\n",
              "      <td>NFLX</td>\n",
              "      <td>52.490000</td>\n",
              "      <td>53.300002</td>\n",
              "      <td>52.260001</td>\n",
              "      <td>54.199999</td>\n",
              "      <td>8180900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date symbol       open      close        low       high      volume\n",
              "0 2010-01-04   NFLX  55.519999  53.479999  52.960001  55.730000  17239600.0\n",
              "1 2010-01-05   NFLX  53.570001  51.510001  50.810001  53.599998  23753100.0\n",
              "2 2010-01-06   NFLX  51.530001  53.319999  50.380002  53.710001  23290400.0\n",
              "3 2010-01-07   NFLX  54.120000  52.400001  52.240001  54.300001   9955400.0\n",
              "4 2010-01-08   NFLX  52.490000  53.300002  52.260001  54.199999   8180900.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOihOE0-qThs",
        "colab_type": "text"
      },
      "source": [
        "**Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "IKxcuU-gqThs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = lstm.filter(['close'])\n",
        "dataset = data.values \n",
        "training_data_len = math.ceil(len(dataset)*.75)  \n",
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data = scaler.fit_transform(dataset)\n",
        "train_data = scaled_data[0:training_data_len, :]\n",
        "x_train = []\n",
        "y_train = []\n",
        "for i in range(60,len(train_data)):\n",
        "    x_train.append(train_data[i-60:i, 0])\n",
        "    y_train.append(train_data[i,0])\n",
        "x_train,y_train = np.array(x_train), np.array(y_train)\n",
        "x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMCuZ0wcqTht",
        "colab_type": "text"
      },
      "source": [
        "**Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Bcm8Ud1AqThu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "outputId": "f410d1c2-6a53-4215-88fe-cabab537560e"
      },
      "source": [
        "model =Sequential()\n",
        "model.add(LSTM(64,return_sequences=True, input_shape=(x_train.shape[1],1)))\n",
        "model.add(LSTM(64, return_sequences= False))\n",
        "model.add(Dense(32))\n",
        "model.add(Dense(1))\n",
        "model.summary()\n",
        "from tensorflow.keras.utils import plot_model \n",
        "plot_model(model, to_file='model1.png')\n"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 60, 64)            16896     \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 64)                33024     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 52,033\n",
            "Trainable params: 52,033\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1djPH6iTqThv",
        "colab_type": "text"
      },
      "source": [
        "**Compiling Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-output": true,
        "trusted": true,
        "id": "INbCn4ZRqThv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "4b42597d-dc31-4161-f70d-5b0694fdd55a"
      },
      "source": [
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(x_train,y_train, batch_size=85, epochs=10)\n"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1262/1262 [==============================] - 2s 2ms/step - loss: 0.0198\n",
            "Epoch 2/10\n",
            "1262/1262 [==============================] - 2s 1ms/step - loss: 0.0027\n",
            "Epoch 3/10\n",
            "1262/1262 [==============================] - 2s 1ms/step - loss: 0.0013\n",
            "Epoch 4/10\n",
            "1262/1262 [==============================] - 2s 1ms/step - loss: 9.3033e-04\n",
            "Epoch 5/10\n",
            "1262/1262 [==============================] - 2s 1ms/step - loss: 8.3774e-04\n",
            "Epoch 6/10\n",
            "1262/1262 [==============================] - 2s 1ms/step - loss: 8.0103e-04\n",
            "Epoch 7/10\n",
            "1262/1262 [==============================] - 2s 2ms/step - loss: 7.8102e-04\n",
            "Epoch 8/10\n",
            "1262/1262 [==============================] - 2s 2ms/step - loss: 7.5857e-04\n",
            "Epoch 9/10\n",
            "1262/1262 [==============================] - 2s 1ms/step - loss: 7.4795e-04\n",
            "Epoch 10/10\n",
            "1262/1262 [==============================] - 2s 2ms/step - loss: 7.6345e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f163c50d0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-z2b8NlAqThw",
        "colab_type": "text"
      },
      "source": [
        "**Prediction and Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "xBVwGSFUqThx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "43d7a55f-cc73-4667-ef69-ab351d8346ab"
      },
      "source": [
        "test_data= scaled_data[training_data_len-60:, :]\n",
        "x_test = []\n",
        "y_test = dataset[training_data_len:,:]\n",
        "for i in range(60,len(test_data)):\n",
        "    x_test.append(test_data[i-60:i,0])\n",
        "x_test = np.array(x_test)\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1],1))\n",
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "rmse = np.sqrt(np.mean(predictions - y_test)**2)\n",
        "rmse"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.407256397124689"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lFcCkZUqThy",
        "colab_type": "text"
      },
      "source": [
        "# Principle Component Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbzl5fjvqThy",
        "colab_type": "text"
      },
      "source": [
        "**It's an important method for dimension reduction.It extracts low dimensional set of features from a high dimensional data set with a motive to capture as much information as possible and to visualise high-dimensional data, it also reduces noise and finally makes other algorithms to work better because we are injecting fewer inputs.**\n",
        "* Example: When we have to bring out strong patterns in a data set or to make data easy to explore and visualize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TVVdyQwQqThy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "643e2f68-265c-4a8c-f8a3-71cf98f40e77"
      },
      "source": [
        "from sklearn.datasets import make_blobs\n",
        "from sklearn import datasets\n",
        "class PCA:\n",
        "  def __init__(self, n_components):\n",
        "    self.n_components = n_components\n",
        "    self.components = None\n",
        "    self.mean = None\n",
        "\n",
        "  def fit(self, X):\n",
        "    self.mean = np.mean(X, axis=0)\n",
        "    X = X - self.mean\n",
        "    cov = np.cov(X.T)\n",
        "\n",
        "    evalue, evector = np.linalg.eig(cov)\n",
        "\n",
        "    eigenvectors = evector.T\n",
        "    idxs = np.argsort(evalue)[::-1]\n",
        "    \n",
        "    evalue = evalue[idxs]\n",
        "    evector = evector[idxs]\n",
        "    self.components = evector[0:self.n_components]\n",
        "\n",
        "  def transform(self, X):\n",
        "    #project data\n",
        "    X = X - self.mean\n",
        "    return(np.dot(X, self.components.T))\n",
        "\n",
        "data = datasets.load_iris()\n",
        "X = data.data\n",
        "y = data.target\n",
        "\n",
        "pca = PCA(2)\n",
        "pca.fit(X)\n",
        "X_projected = pca.transform(X)\n",
        "\n",
        "\n",
        "\n",
        "x1 = X_projected[:,0]\n",
        "x2 = X_projected[:,1]\n",
        "\n",
        "plt.scatter(x1,x2,c=y,edgecolor='none',alpha=0.8,cmap=plt.cm.get_cmap('viridis',3))\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SxlaJXTqTh0",
        "colab_type": "text"
      },
      "source": [
        "# Apriori"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-z3SLT1QqTh0",
        "colab_type": "text"
      },
      "source": [
        "**It is a categorisation algorithm attempts to operate on database records, particularly transactional records, or records including certain numbers of fields or items.It is mainly used for sorting large amounts of data. Sorting data often occurs because of association rules. **\n",
        "* Example: To analyse data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "yB1mEC6JqTh1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "ec7fdfde-dd8d-4385-d7ef-1e08faf5b95d"
      },
      "source": [
        "df = pd.read_csv('/datasets_344_727_GroceryStoreDataSet.csv',names=['products'],header=None)\n",
        "data = list(df[\"products\"].apply(lambda x:x.split(',')))\n",
        "data"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['MILK', 'BREAD', 'BISCUIT'],\n",
              " ['BREAD', 'MILK', 'BISCUIT', 'CORNFLAKES'],\n",
              " ['BREAD', 'TEA', 'BOURNVITA'],\n",
              " ['JAM', 'MAGGI', 'BREAD', 'MILK'],\n",
              " ['MAGGI', 'TEA', 'BISCUIT'],\n",
              " ['BREAD', 'TEA', 'BOURNVITA'],\n",
              " ['MAGGI', 'TEA', 'CORNFLAKES'],\n",
              " ['MAGGI', 'BREAD', 'TEA', 'BISCUIT'],\n",
              " ['JAM', 'MAGGI', 'BREAD', 'TEA'],\n",
              " ['BREAD', 'MILK'],\n",
              " ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],\n",
              " ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],\n",
              " ['COFFEE', 'SUGER', 'BOURNVITA'],\n",
              " ['BREAD', 'COFFEE', 'COCK'],\n",
              " ['BREAD', 'SUGER', 'BISCUIT'],\n",
              " ['COFFEE', 'SUGER', 'CORNFLAKES'],\n",
              " ['BREAD', 'SUGER', 'BOURNVITA'],\n",
              " ['BREAD', 'COFFEE', 'SUGER'],\n",
              " ['BREAD', 'COFFEE', 'SUGER'],\n",
              " ['TEA', 'MILK', 'COFFEE', 'CORNFLAKES']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "lD6L-SOwqTh3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "333e21ad-3269-43e3-baae-3d2a702f5901"
      },
      "source": [
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "te = TransactionEncoder()\n",
        "te_data = te.fit(data).transform(data)\n",
        "df = pd.DataFrame(te_data,columns=te.columns_)\n",
        "df1 = apriori(df,min_support=0.01,use_colnames=True)\n",
        "df1.head()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.35</td>\n",
              "      <td>(BISCUIT)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.20</td>\n",
              "      <td>(BOURNVITA)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.65</td>\n",
              "      <td>(BREAD)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.15</td>\n",
              "      <td>(COCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.40</td>\n",
              "      <td>(COFFEE)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   support     itemsets\n",
              "0     0.35    (BISCUIT)\n",
              "1     0.20  (BOURNVITA)\n",
              "2     0.65      (BREAD)\n",
              "3     0.15       (COCK)\n",
              "4     0.40     (COFFEE)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f49FCpVlqTh6",
        "colab_type": "text"
      },
      "source": [
        "# Prophet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UkEhWsELqTh6",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Prophet is an extremely easy tool for analysts to produce reliable forecasts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "trusted": true,
        "id": "aexmJOeNqTh6",
        "colab_type": "text"
      },
      "source": [
        "1. Prophet only takes data as a dataframe with a ds (datestamp) and y (value we want to forecast) column. So first, let’s convert the dataframe to the appropriate format.\n",
        "1. Create an instance of the Prophet class and then fit our dataframe to it.\n",
        "2. Create a dataframe with the dates for which we want a prediction to be made with make_future_dataframe(). Then specify the number of days to forecast using the periods parameter.\n",
        "3. Call predict to make a prediction and store it in the forecast dataframe. What’s neat here is that you can inspect the dataframe and see the predictions as well as the lower and upper boundaries of the uncertainty interval.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNc1gzwlqTh6",
        "colab_type": "text"
      },
      "source": [
        "**Library and Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "PcpKlnqZqTh7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "af002ac4-820b-40d3-ed79-9dfca7baf142"
      },
      "source": [
        "import plotly.offline as py\n",
        "import plotly.express as px\n",
        "from fbprophet import Prophet\n",
        "from fbprophet.plot import plot_plotly, add_changepoints_to_plot\n",
        "\n",
        "pred = pd.read_csv(\"/covid-19-all.csv\")\n",
        "pred = pred.fillna(0)\n",
        "predgrp = pred.groupby(\"Date\")[[\"Confirmed\",\"Recovered\",\"Deaths\"]].sum().reset_index()\n",
        "pred_cnfrm = predgrp.loc[:,[\"Date\",\"Confirmed\"]]\n",
        "pr_data = pred_cnfrm\n",
        "pr_data.columns = ['ds','y']\n",
        "pr_data.head()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>941.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>1438.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>2118.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ds       y\n",
              "0  2020-01-22   555.0\n",
              "1  2020-01-23   653.0\n",
              "2  2020-01-24   941.0\n",
              "3  2020-01-25  1438.0\n",
              "4  2020-01-26  2118.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pAQj5jbqTh_",
        "colab_type": "text"
      },
      "source": [
        "**Model and Forecast**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "haT8sW_bqTiA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "outputId": "7006149a-6bab-43a4-a041-ff5fb7abccbd"
      },
      "source": [
        "m=Prophet()\n",
        "m.fit(pr_data)\n",
        "future=m.make_future_dataframe(periods=15)\n",
        "forecast=m.predict(future)\n",
        "forecast\n"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>weekly</th>\n",
              "      <th>weekly_lower</th>\n",
              "      <th>weekly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>-8.255279e+03</td>\n",
              "      <td>-8.562225e+04</td>\n",
              "      <td>6.370752e+04</td>\n",
              "      <td>-8.255279e+03</td>\n",
              "      <td>-8.255279e+03</td>\n",
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              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>-5.601910e+03</td>\n",
              "      <td>-7.935931e+04</td>\n",
              "      <td>6.527202e+04</td>\n",
              "      <td>-5.601910e+03</td>\n",
              "      <td>-5.601910e+03</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3.705993e+03</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>-2.948541e+03</td>\n",
              "      <td>-5.921735e+04</td>\n",
              "      <td>7.522503e+04</td>\n",
              "      <td>-2.948541e+03</td>\n",
              "      <td>-2.948541e+03</td>\n",
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              "      <td>10863.443289</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.914902e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>-2.951717e+02</td>\n",
              "      <td>-6.719935e+04</td>\n",
              "      <td>7.469072e+04</td>\n",
              "      <td>-2.951717e+02</td>\n",
              "      <td>-2.951717e+02</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.033428e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>2.358198e+03</td>\n",
              "      <td>-6.887634e+04</td>\n",
              "      <td>7.162951e+04</td>\n",
              "      <td>2.358198e+03</td>\n",
              "      <td>2.358198e+03</td>\n",
              "      <td>1102.853869</td>\n",
              "      <td>1102.853869</td>\n",
              "      <td>1102.853869</td>\n",
              "      <td>1102.853869</td>\n",
              "      <td>1102.853869</td>\n",
              "      <td>1102.853869</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.461051e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>2020-07-14</td>\n",
              "      <td>1.239808e+07</td>\n",
              "      <td>1.229562e+07</td>\n",
              "      <td>1.249376e+07</td>\n",
              "      <td>1.233536e+07</td>\n",
              "      <td>1.246098e+07</td>\n",
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              "      <td>-7374.619313</td>\n",
              "      <td>-7374.619313</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.239071e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>2020-07-15</td>\n",
              "      <td>1.254329e+07</td>\n",
              "      <td>1.243036e+07</td>\n",
              "      <td>1.263916e+07</td>\n",
              "      <td>1.246830e+07</td>\n",
              "      <td>1.261643e+07</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>-3400.455622</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.253989e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>2020-07-16</td>\n",
              "      <td>1.268849e+07</td>\n",
              "      <td>1.258171e+07</td>\n",
              "      <td>1.280419e+07</td>\n",
              "      <td>1.260110e+07</td>\n",
              "      <td>1.276987e+07</td>\n",
              "      <td>1895.917476</td>\n",
              "      <td>1895.917476</td>\n",
              "      <td>1895.917476</td>\n",
              "      <td>1895.917476</td>\n",
              "      <td>1895.917476</td>\n",
              "      <td>1895.917476</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.269039e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>2020-07-17</td>\n",
              "      <td>1.283370e+07</td>\n",
              "      <td>1.273127e+07</td>\n",
              "      <td>1.296058e+07</td>\n",
              "      <td>1.273338e+07</td>\n",
              "      <td>1.292308e+07</td>\n",
              "      <td>10863.443289</td>\n",
              "      <td>10863.443289</td>\n",
              "      <td>10863.443289</td>\n",
              "      <td>10863.443289</td>\n",
              "      <td>10863.443289</td>\n",
              "      <td>10863.443289</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.284456e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>2020-07-18</td>\n",
              "      <td>1.297890e+07</td>\n",
              "      <td>1.284873e+07</td>\n",
              "      <td>1.311128e+07</td>\n",
              "      <td>1.286436e+07</td>\n",
              "      <td>1.307973e+07</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>2328.599685</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.298123e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>179 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds         trend  ...  multiplicative_terms_upper          yhat\n",
              "0   2020-01-22 -8.255279e+03  ...                         0.0 -1.165574e+04\n",
              "1   2020-01-23 -5.601910e+03  ...                         0.0 -3.705993e+03\n",
              "2   2020-01-24 -2.948541e+03  ...                         0.0  7.914902e+03\n",
              "3   2020-01-25 -2.951717e+02  ...                         0.0  2.033428e+03\n",
              "4   2020-01-26  2.358198e+03  ...                         0.0  3.461051e+03\n",
              "..         ...           ...  ...                         ...           ...\n",
              "174 2020-07-14  1.239808e+07  ...                         0.0  1.239071e+07\n",
              "175 2020-07-15  1.254329e+07  ...                         0.0  1.253989e+07\n",
              "176 2020-07-16  1.268849e+07  ...                         0.0  1.269039e+07\n",
              "177 2020-07-17  1.283370e+07  ...                         0.0  1.284456e+07\n",
              "178 2020-07-18  1.297890e+07  ...                         0.0  1.298123e+07\n",
              "\n",
              "[179 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "BHCHr5kVqTiB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "56a93d87-00f8-49f6-c175-74388f0fe5b6"
      },
      "source": [
        "fig = plot_plotly(m, forecast)\n",
        "py.iplot(fig) \n",
        "\n",
        "fig = m.plot(forecast,xlabel='Date',ylabel='Confirmed Count')"
      ],
      "execution_count": 124,
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdJ2E3HBqTiF",
        "colab_type": "text"
      },
      "source": [
        "# **Evaluate Algorithms** \n",
        "**The evaluation of algorithm consist three following steps:- **\n",
        "1. Test Harness  \n",
        "2. Explore and select algorithms \n",
        "3. Interpret and report results \n",
        "\n"
      ]
    }
  ]
}