kNN_wine_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "kNN_wine_dataset.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "YO_9N1JcSf8O"
      },
      "source": [
        "#Import scikit-learn dataset library\n",
        "from sklearn import datasets\n",
        "\n",
        "#Load dataset\n",
        "wine = datasets.load_wine()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KK4vam-1V-hG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "879a5a61-9019-479a-f2a2-df4311d1c3d5"
      },
      "source": [
        "# print the names of the features\n",
        "print(wine.feature_names)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28AZCny6WDW4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2971a038-a599-4a13-cb07-43ab2593353e"
      },
      "source": [
        "# print the label species(class_0, class_1, class_2)\n",
        "print(wine.target_names)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['class_0' 'class_1' 'class_2']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiutvoeFWSpz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0b6a8ca-b169-44d9-c8b2-aed2e8406397"
      },
      "source": [
        "# print the wine data (top 5 records)\n",
        "print(wine.data[0:5])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.423e+01 1.710e+00 2.430e+00 1.560e+01 1.270e+02 2.800e+00 3.060e+00\n",
            "  2.800e-01 2.290e+00 5.640e+00 1.040e+00 3.920e+00 1.065e+03]\n",
            " [1.320e+01 1.780e+00 2.140e+00 1.120e+01 1.000e+02 2.650e+00 2.760e+00\n",
            "  2.600e-01 1.280e+00 4.380e+00 1.050e+00 3.400e+00 1.050e+03]\n",
            " [1.316e+01 2.360e+00 2.670e+00 1.860e+01 1.010e+02 2.800e+00 3.240e+00\n",
            "  3.000e-01 2.810e+00 5.680e+00 1.030e+00 3.170e+00 1.185e+03]\n",
            " [1.437e+01 1.950e+00 2.500e+00 1.680e+01 1.130e+02 3.850e+00 3.490e+00\n",
            "  2.400e-01 2.180e+00 7.800e+00 8.600e-01 3.450e+00 1.480e+03]\n",
            " [1.324e+01 2.590e+00 2.870e+00 2.100e+01 1.180e+02 2.800e+00 2.690e+00\n",
            "  3.900e-01 1.820e+00 4.320e+00 1.040e+00 2.930e+00 7.350e+02]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GriV4mhOWXL_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43347429-4968-4b03-b8a4-c709990647ba"
      },
      "source": [
        "# print the wine labels (0:Class_0, 1:Class_1, 2:Class_3)\n",
        "print(wine.target)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GR-a6j1JWm-l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac2ff8a0-66ca-4349-ecb5-949f5f8ad6f4"
      },
      "source": [
        "# print data(feature)shape\n",
        "print(wine.data.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(178, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "0Mu-ZLTX7X9Z",
        "outputId": "657d2078-8874-42bf-ee83-ac0210d6c90f"
      },
      "source": [
        "display(wine)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n',\n",
              " 'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
              "         1.065e+03],\n",
              "        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
              "         1.050e+03],\n",
              "        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
              "         1.185e+03],\n",
              "        ...,\n",
              "        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
              "         8.350e+02],\n",
              "        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
              "         8.400e+02],\n",
              "        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
              "         5.600e+02]]),\n",
              " 'feature_names': ['alcohol',\n",
              "  'malic_acid',\n",
              "  'ash',\n",
              "  'alcalinity_of_ash',\n",
              "  'magnesium',\n",
              "  'total_phenols',\n",
              "  'flavanoids',\n",
              "  'nonflavanoid_phenols',\n",
              "  'proanthocyanins',\n",
              "  'color_intensity',\n",
              "  'hue',\n",
              "  'od280/od315_of_diluted_wines',\n",
              "  'proline'],\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2]),\n",
              " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7')}"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QY4_v_cYWqcm"
      },
      "source": [
        "# Import train_test_split function\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset into training set and test set w/ 70% train & 30% test\n",
        "X_train, X_test, y_train, y_test = train_test_split(wine.data, \n",
        "                                                    wine.target, \n",
        "                                                    test_size=0.3)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXvkiyIiXmdG"
      },
      "source": [
        "#Import knearest neighbors Classifier model\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaJe9R0tXvSe"
      },
      "source": [
        "#Create KNN Classifier -- FILL IN THIS LINE\n",
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csH6rL8XYNVs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d32513e-40a6-45d1-d360-9bd3242183cf"
      },
      "source": [
        "#Train the model using the training sets -- FILL IN THIS LINE\n",
        "knn.fit(X_train, y_train)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOJkhaQ5YPb7"
      },
      "source": [
        "#Predict the response for test dataset -- FILL IN THIS LINE\n",
        "y_pred=knn.predict(X_test)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24b27fp7YQji",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8926080-366e-4c41-af3a-84e6a412d854"
      },
      "source": [
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7962962962962963\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxhqW1t7hUxD"
      },
      "source": [
        "Visualize your features with boxplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBdhDPvQh2ZZ"
      },
      "source": [
        "# import pandas for dataframes\n",
        "import pandas as pd\n",
        "\n",
        "# import some visualization tools\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ONK2WethV28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "d412e638-90ee-4b8d-c28c-836542629f72"
      },
      "source": [
        "df = pd.DataFrame(wine.data, columns=wine.feature_names)\n",
        "\n",
        "plt.figure(figsize=(28, 6))\n",
        "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(df))\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2016x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OEGOO6H5ZIMn"
      },
      "source": [
        "NORMALIZE your dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BepL0YGhYdjJ"
      },
      "source": [
        "# import scikit-learn method to Normalize\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3_kOIwnZKxA"
      },
      "source": [
        "min_max_scaler = preprocessing.MinMaxScaler()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlTGUgE4ZKz9"
      },
      "source": [
        "# Do minmax scaling on your original dataset -- FILL THIS IN \n",
        "\n",
        "D_minmax = min_max_scaler.fit_transform(wine.data)\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IMlmd_SZK2n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "243084fe-772d-4e21-f232-a79c785574b5"
      },
      "source": [
        "# put your data into a pandas dataframe -- FILL THIS IN\n",
        "wine_minmax = pd.DataFrame(D_minmax, columns=wine.feature_names)\n",
        "wine_minmax.head()"
      ],
      "execution_count": 34,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.191700</td>\n",
              "      <td>0.572193</td>\n",
              "      <td>0.257732</td>\n",
              "      <td>0.619565</td>\n",
              "      <td>0.627586</td>\n",
              "      <td>0.573840</td>\n",
              "      <td>0.283019</td>\n",
              "      <td>0.593060</td>\n",
              "      <td>0.372014</td>\n",
              "      <td>0.455285</td>\n",
              "      <td>0.970696</td>\n",
              "      <td>0.561341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.571053</td>\n",
              "      <td>0.205534</td>\n",
              "      <td>0.417112</td>\n",
              "      <td>0.030928</td>\n",
              "      <td>0.326087</td>\n",
              "      <td>0.575862</td>\n",
              "      <td>0.510549</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>0.274448</td>\n",
              "      <td>0.264505</td>\n",
              "      <td>0.463415</td>\n",
              "      <td>0.780220</td>\n",
              "      <td>0.550642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.560526</td>\n",
              "      <td>0.320158</td>\n",
              "      <td>0.700535</td>\n",
              "      <td>0.412371</td>\n",
              "      <td>0.336957</td>\n",
              "      <td>0.627586</td>\n",
              "      <td>0.611814</td>\n",
              "      <td>0.320755</td>\n",
              "      <td>0.757098</td>\n",
              "      <td>0.375427</td>\n",
              "      <td>0.447154</td>\n",
              "      <td>0.695971</td>\n",
              "      <td>0.646933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.878947</td>\n",
              "      <td>0.239130</td>\n",
              "      <td>0.609626</td>\n",
              "      <td>0.319588</td>\n",
              "      <td>0.467391</td>\n",
              "      <td>0.989655</td>\n",
              "      <td>0.664557</td>\n",
              "      <td>0.207547</td>\n",
              "      <td>0.558360</td>\n",
              "      <td>0.556314</td>\n",
              "      <td>0.308943</td>\n",
              "      <td>0.798535</td>\n",
              "      <td>0.857347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.581579</td>\n",
              "      <td>0.365613</td>\n",
              "      <td>0.807487</td>\n",
              "      <td>0.536082</td>\n",
              "      <td>0.521739</td>\n",
              "      <td>0.627586</td>\n",
              "      <td>0.495781</td>\n",
              "      <td>0.490566</td>\n",
              "      <td>0.444795</td>\n",
              "      <td>0.259386</td>\n",
              "      <td>0.455285</td>\n",
              "      <td>0.608059</td>\n",
              "      <td>0.325963</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    alcohol  malic_acid  ...  od280/od315_of_diluted_wines   proline\n",
              "0  0.842105    0.191700  ...                      0.970696  0.561341\n",
              "1  0.571053    0.205534  ...                      0.780220  0.550642\n",
              "2  0.560526    0.320158  ...                      0.695971  0.646933\n",
              "3  0.878947    0.239130  ...                      0.798535  0.857347\n",
              "4  0.581579    0.365613  ...                      0.608059  0.325963\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAPCC5OthOVj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "0b964949-f84a-4952-92c5-67d6180834af"
      },
      "source": [
        "# plot boxplot for normalized data \n",
        "plt.figure(figsize=(28, 6))\n",
        "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(wine_minmax))\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2016x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQmE23SqZLTa"
      },
      "source": [
        "STANDARDIZE your dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zW2iMUo-ZNt6"
      },
      "source": [
        "standard_scaler = preprocessing.StandardScaler()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIW2ONf-ZQ3v"
      },
      "source": [
        "# Standardize your original dataset -- FILL THIS IN\n",
        "standard_scaler = preprocessing.StandardScaler()\n",
        "D_standard = standard_scaler.fit_transform(wine.data)\n"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4KsDLj2ZQ7C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "b954112e-27fe-4cac-81f7-8a402fc4dc4f"
      },
      "source": [
        "# put your data into a pandas dataframe -- FILL THIS IN\n",
        "wine_standard = pd.DataFrame(D_standard, columns=wine.feature_names)\n",
        "wine_standard.head()"
      ],
      "execution_count": 39,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.518613</td>\n",
              "      <td>-0.562250</td>\n",
              "      <td>0.232053</td>\n",
              "      <td>-1.169593</td>\n",
              "      <td>1.913905</td>\n",
              "      <td>0.808997</td>\n",
              "      <td>1.034819</td>\n",
              "      <td>-0.659563</td>\n",
              "      <td>1.224884</td>\n",
              "      <td>0.251717</td>\n",
              "      <td>0.362177</td>\n",
              "      <td>1.847920</td>\n",
              "      <td>1.013009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.246290</td>\n",
              "      <td>-0.499413</td>\n",
              "      <td>-0.827996</td>\n",
              "      <td>-2.490847</td>\n",
              "      <td>0.018145</td>\n",
              "      <td>0.568648</td>\n",
              "      <td>0.733629</td>\n",
              "      <td>-0.820719</td>\n",
              "      <td>-0.544721</td>\n",
              "      <td>-0.293321</td>\n",
              "      <td>0.406051</td>\n",
              "      <td>1.113449</td>\n",
              "      <td>0.965242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.196879</td>\n",
              "      <td>0.021231</td>\n",
              "      <td>1.109334</td>\n",
              "      <td>-0.268738</td>\n",
              "      <td>0.088358</td>\n",
              "      <td>0.808997</td>\n",
              "      <td>1.215533</td>\n",
              "      <td>-0.498407</td>\n",
              "      <td>2.135968</td>\n",
              "      <td>0.269020</td>\n",
              "      <td>0.318304</td>\n",
              "      <td>0.788587</td>\n",
              "      <td>1.395148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.691550</td>\n",
              "      <td>-0.346811</td>\n",
              "      <td>0.487926</td>\n",
              "      <td>-0.809251</td>\n",
              "      <td>0.930918</td>\n",
              "      <td>2.491446</td>\n",
              "      <td>1.466525</td>\n",
              "      <td>-0.981875</td>\n",
              "      <td>1.032155</td>\n",
              "      <td>1.186068</td>\n",
              "      <td>-0.427544</td>\n",
              "      <td>1.184071</td>\n",
              "      <td>2.334574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.295700</td>\n",
              "      <td>0.227694</td>\n",
              "      <td>1.840403</td>\n",
              "      <td>0.451946</td>\n",
              "      <td>1.281985</td>\n",
              "      <td>0.808997</td>\n",
              "      <td>0.663351</td>\n",
              "      <td>0.226796</td>\n",
              "      <td>0.401404</td>\n",
              "      <td>-0.319276</td>\n",
              "      <td>0.362177</td>\n",
              "      <td>0.449601</td>\n",
              "      <td>-0.037874</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    alcohol  malic_acid  ...  od280/od315_of_diluted_wines   proline\n",
              "0  1.518613   -0.562250  ...                      1.847920  1.013009\n",
              "1  0.246290   -0.499413  ...                      1.113449  0.965242\n",
              "2  0.196879    0.021231  ...                      0.788587  1.395148\n",
              "3  1.691550   -0.346811  ...                      1.184071  2.334574\n",
              "4  0.295700    0.227694  ...                      0.449601 -0.037874\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqxWET16isMd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "bbafedeb-42e0-4d51-be1a-093be74b3b90"
      },
      "source": [
        "# plot boxplot for normalized data\n",
        "plt.figure(figsize=(28, 6))\n",
        "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(wine_standard))\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2016x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNaGBOAemJ4b"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}