Iris_Flowers_Classification_ML_Project.ipynb

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    "colab": {
      "name": " Iris Flowers Classification ML Project",
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    {
      "cell_type": "markdown",
      "source": [
        "**<h1 align=center>LETS GROW MORE - Virtual Internship Program - Data Science Internship</h1>**"
      ],
      "metadata": {
        "id": "UhjDwbLMFHNE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <h2> TASK 2 - Beginner Level - Iris Flowers Classification ML Project</h2>"
      ],
      "metadata": {
        "id": "3iEx8nQfFOLF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3>Name - Viswanath Bodapati<br> Language - Python</h3>"
      ],
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        "id": "ulorr5XGFSFa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Iris Flowers Classification Project**\n"
      ],
      "metadata": {
        "id": "pGjbID4GGbW8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this project, we will be working on Iris dataset to classify the flowers species, we will follow the whole machine learning project lifecycle. Below is the whole outline that we will be following."
      ],
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        "id": "rpWJPlYIGjvS"
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      "source": [
        "<ol>\n",
        "  <li>Loading the imporatant libraries</li>\n",
        "  <li>Loading and understanding the Iris data</li>\n",
        "  <li>Exploring the data</li>\n",
        "  <li>Visualizaing the data</li>\n",
        "  \n",
        "  <ul>\n",
        "    <li> Checking if our data is skewed or not using countplot.</li>\n",
        "    <li> We will plot the grids for every species.</li>\n",
        "  </ul>\n",
        "  \n",
        "  <li>Checking Null Values</li>\n",
        "  <li>Dividing the data</li>\n",
        "  <li>Model Development</li>\n",
        "  \n",
        "  <ul>\n",
        "    <li>Using Lr, SVM, Decision Tree, Gradient Boosting</li>\n",
        "    <li>Calculating loss</li>\n",
        "    <li>Training on every algorithm</li>\n",
        "    <li>Choosing the best</li>\n",
        "  </ul>\n",
        "\n",
        "  <li>Saving the Model</li>\n",
        "\n",
        "</ol>"
      ],
      "metadata": {
        "id": "PPjgrHARGo3v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "\n",
        "from matplotlib.pyplot import plot as plt \n",
        "import seaborn as sns \n",
        "\n",
        "# Models \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC \n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import precision_score, accuracy_score, recall_score\n",
        "\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')\n",
        "import joblib"
      ],
      "metadata": {
        "id": "7gKDZz-YFU96"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "try :\n",
        "  data = pd.read_csv('/content/drive/MyDrive/Iris_data (1).csv')\n",
        "except:\n",
        "  print('Err')\n",
        "\n",
        "data.head()"
      ],
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          "height": 206
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>species</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
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              "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  species\n",
              "0                5.1               3.5  ...               0.2        0\n",
              "1                4.9               3.0  ...               0.2        0\n",
              "2                4.7               3.2  ...               0.2        0\n",
              "3                4.6               3.1  ...               0.2        0\n",
              "4                5.0               3.6  ...               0.2        0\n",
              "\n",
              "[5 rows x 5 columns]"
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            "text/plain": [
              "sepal length (cm)    0\n",
              "sepal width (cm)     0\n",
              "petal length (cm)    0\n",
              "petal width (cm)     0\n",
              "species              0\n",
              "dtype: int64"
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              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
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              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
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              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.762238</td>\n",
              "      <td>0.819232</td>\n",
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              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
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              "       sepal length (cm)  sepal width (cm)  ...  petal width (cm)     species\n",
              "count         150.000000        150.000000  ...        150.000000  150.000000\n",
              "mean            5.843333          3.057333  ...          1.199333    1.000000\n",
              "std             0.828066          0.435866  ...          0.762238    0.819232\n",
              "min             4.300000          2.000000  ...          0.100000    0.000000\n",
              "25%             5.100000          2.800000  ...          0.300000    0.000000\n",
              "50%             5.800000          3.000000  ...          1.300000    1.000000\n",
              "75%             6.400000          3.300000  ...          1.800000    2.000000\n",
              "max             7.900000          4.400000  ...          2.500000    2.000000\n",
              "\n",
              "[8 rows x 5 columns]"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"species\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmccqCeLhwkL",
        "outputId": "9b115e72-1424-408a-f409-18d8f5765c14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def to_Categorial(y):\n",
        "  ans = []\n",
        "  for i in y:\n",
        "    if i is 'Iris-Setosa':\n",
        "      ans.append(0)\n",
        "    elif i is 'Iris-Versicolour':\n",
        "      ans.append(1)\n",
        "    elif i is 'Iris-Virginica':\n",
        "      ans.append(2)\n",
        "  return ans"
      ],
      "metadata": {
        "id": "j9M8oHXMsrMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ELGrotlcsrI9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p = np.array([data['sepal length (cm)'],data['sepal width (cm)'],data['petal length (cm)'],data['petal width (cm)']])\n",
        "q = np.array(data['species'])"
      ],
      "metadata": {
        "id": "l1GH-CHrqZ4p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p, q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-4dfVCosjbt",
        "outputId": "7f178098-8b81-40c0-b029-55515de766cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,\n",
              "         4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,\n",
              "         5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,\n",
              "         5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,\n",
              "         6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,\n",
              "         6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,\n",
              "         6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,\n",
              "         6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,\n",
              "         6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,\n",
              "         7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,\n",
              "         7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,\n",
              "         6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9],\n",
              "        [3.5, 3. , 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3. ,\n",
              "         3. , 4. , 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3. ,\n",
              "         3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3. ,\n",
              "         3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3. , 3.8, 3.2, 3.7, 3.3, 3.2, 3.2,\n",
              "         3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2. , 3. , 2.2, 2.9, 2.9,\n",
              "         3.1, 3. , 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3. , 2.8, 3. ,\n",
              "         2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3. , 3.4, 3.1, 2.3, 3. , 2.5, 2.6,\n",
              "         3. , 2.6, 2.3, 2.7, 3. , 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3. , 2.9,\n",
              "         3. , 3. , 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3. , 2.5, 2.8, 3.2, 3. ,\n",
              "         3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3. , 2.8, 3. ,\n",
              "         2.8, 3.8, 2.8, 2.8, 2.6, 3. , 3.4, 3.1, 3. , 3.1, 3.1, 3.1, 2.7,\n",
              "         3.2, 3.3, 3. , 2.5, 3. , 3.4, 3. ],\n",
              "        [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4,\n",
              "         1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1. , 1.7, 1.9, 1.6,\n",
              "         1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3,\n",
              "         1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5,\n",
              "         4.9, 4. , 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4. , 4.7, 3.6,\n",
              "         4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4. , 4.9, 4.7, 4.3, 4.4, 4.8, 5. ,\n",
              "         4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4. , 4.4,\n",
              "         4.6, 4. , 3.3, 4.2, 4.2, 4.2, 4.3, 3. , 4.1, 6. , 5.1, 5.9, 5.6,\n",
              "         5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5. , 5.1, 5.3, 5.5,\n",
              "         6.7, 6.9, 5. , 5.7, 4.9, 6.7, 4.9, 5.7, 6. , 4.8, 4.9, 5.6, 5.8,\n",
              "         6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1,\n",
              "         5.9, 5.7, 5.2, 5. , 5.2, 5.4, 5.1],\n",
              "        [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1,\n",
              "         0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2,\n",
              "         0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2,\n",
              "         0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5,\n",
              "         1.5, 1.3, 1.5, 1.3, 1.6, 1. , 1.3, 1.4, 1. , 1.5, 1. , 1.4, 1.3,\n",
              "         1.4, 1.5, 1. , 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7,\n",
              "         1.5, 1. , 1.1, 1. , 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2,\n",
              "         1.4, 1.2, 1. , 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8,\n",
              "         2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2. , 1.9, 2.1, 2. , 2.4, 2.3, 1.8,\n",
              "         2.2, 2.3, 1.5, 2.3, 2. , 2. , 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6,\n",
              "         1.9, 2. , 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9,\n",
              "         2.3, 2.5, 2.3, 1.9, 2. , 2.3, 1.8]]),\n",
              " 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, 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, 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, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plt.plot(p[:, 0][q==0], p[:, 1][q==0], 'r.', label = 'Iris-Setosa')\n",
        "#plt.plot(p[:, 0][q==1], p[:, 1][q==1], 'b.', label = 'Iris-Versicolour')\n",
        "#plt.plot(p[:, 0][q==2], p[:, 1][q==2], 'g.', label = 'Iris-Virginica')\n",
        "\n",
        "#plt.legend()\n",
        "#plt.show()"
      ],
      "metadata": {
        "id": "2_O6C8Iko5K8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val = sns.FacetGrid(data,col = 'species',aspect = 1.6,size =3.2)\n",
        "val.map(sns.countplot,'sepal length (cm)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "H0sCL360h2DF",
        "outputId": "e0192d14-27ab-4760-b821-589818c4d3a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f654032ff10>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABEoAAADfCAYAAADhojYDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAd7ElEQVR4nO3dfbRtdVkv8O8jRzIBBeOUCB2hTG9khXAuiqSReovURItSE8msQd6bpHWjq71crfQ2jMq4WrdMwUivjqGiV8UQA9G6mgIK8ibkC1dRHGi+YS8q+rt/rN/RxWbvteba66y919nn8xljjbPWnPP5zWfO/VvP3uM5c65VrbUAAAAAkNxpsxMAAAAAWBYaJQAAAACdRgkAAABAp1ECAAAA0GmUAAAAAHQaJQAAAACdRglMUVVvqaoDN3ifJ1bV9VX1oap61kbuG2CZbVJNPruqbqmqqzdyvwDLbqNrclV9Z1W9vaquraprquoZG7Vv9i7VWtvsHIAxVbVPkhuS/KckNyW5NMkTW2vXbmpiAHupqnpoki8lObe1dv/Nzgdgb1VVhyQ5pLX2vqo6IMnlSR7r72R2N1eUsMerqv2q6vyqurKqrq6qx/flN1bVH1bVVVX13qq6T1++vapeV1WX9sfxffn+VXVO3/4DVfVTY+Mc3J+f0se6oqr+sqr26Y+X931fVVW/OuchHZvkQ621j7TWvpLk1UlOmnNMgA2xBWtyWmvvTPLZeccB2GhbrSa31m5urb2vP781yXVJDp1nTFjNts1OAHaDE5N8srX2qCSpqruPrftCa+37q+rUJH+a5NFJzkrywtbaP1TVjiRvTfK9SX5n1/Z9nIPGd1JV35vk8UmOb619tar+PMmTklyT5NBd/8u42uWHVfWkJGeskvuHWmsnr1h2aJKPj72+KckDB5wHgGWw1WoywJ5sy9bkqjo8yQOSvGfqWYAZaZSwFVyV5I+r6gVJ3txa+/uxda8a+/eF/fkjkhxZVbu2uVtV7d+XP2HXwtba51bs5+FJjklyaY/91iS3JHlTku+qqhclOT/JhSsTbK29Mskr13uAAHsQNRlgeWzJmtxzel2SZ7bWvjhLLAyhUcIer7V2Q1UdneSRSZ5XVRe11n5v1+rxTfu/d0ryoNbav4+PM/YLYS2V5K9ba8++w4qqH0zyY0meluRnkjx1xfpZOuWfSPKdY68P68sAlt4WrMkAe6ytWJOr6s4ZNUle2Vo7b1pisB4+o4Q9XlXdK8m/ttZekeTMJEePrX782L/v7s8vTHL6WPxR/enbkvzy2PLbXVKY5KIkJ1fVt/f196iqe/f7Mu/UWntdkt9esf8ko055a+2oVR6r/UF+aZLvqaojqmrfjLr3bxxwKgA23RasyQB7rK1Wk2vUsXlZkutaa38y8DTAzFxRwlbw/UnOrKqvJ/lqkv88tu6gqvpAki8neWJf9itJ/qwv35bknRl1uJ/Xl1+d5GtJfjfJN7rUrbVrq+q3k1xYVXfq+/rlJP+W5Jy+LEnu0EmfRWvttqp6ekb3hO6T5OzW2jXzjAmwgbZUTU6SqnpVkhOSHFxVNyV5TmvtZfOOC7ABtlpNPj7Jk5NcVVVX9GW/2Vp7y5zjwu34emC2rKq6McnO1tpnNjsXgL2dmgywPNRkmMytNwAAAACdK0oAAAAAOleUAAAAAHQaJQAAAACdRgkAAABAt1RfD3ziiSe2Cy64YLPTANhKar2BajLAbqcmAyyPNWvyUl1R8pnP+HYqgGWhJgMsDzUZYOMsVaMEAAAAYDNplAAAAAB0C22UVNWvVtU1VXV1Vb2qqu6yyP0BAAAAzGNhjZKqOjTJryTZ2Vq7f5J9kjxhUfsDAAAAmNeib73ZluRbq2pbkrsm+eSC9wcAAACwbgtrlLTWPpHkj5J8LMnNSb7QWrtwUfsDAAAAmNe2RQ1cVQclOSnJEUk+n+Q1VXVKa+0VK7Y7LclpSbJjx45FpcMGO+aMc9cVd/mZp841xng8MDs1GWB5qMkAm2ORt948IslHW2ufbq19Ncl5SR68cqPW2ktaaztbazu3b9++wHQAmEZNBlgeajLA5lhko+RjSR5UVXetqkry8CTXLXB/AAAAAHNZ5GeUvCfJa5O8L8lVfV8vWdT+AAAAAOa1sM8oSZLW2nOSPGeR+wAAAADYXRb99cAAAAAAewyNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBuoY2Sqjqwql5bVR+squuq6rhF7g8AAABgHtsWPP5ZSS5orZ1cVfsmueuC9wcAAACwbgtrlFTV3ZM8NMlTkqS19pUkX1nU/gAAAADmtchbb45I8ukk51TV+6vqpVW13wL3BwAAADCXRd56sy3J0UlOb629p6rOSvKsJL8zvlFVnZbktCTZsWPHAtMBYBo1GWB5qMms5Zgzzp055vIzT11AJrA1LfKKkpuS3NRae09//dqMGie301p7SWttZ2tt5/bt2xeYDgDTqMkAy0NNBtgcC2uUtNY+leTjVXW/vujhSa5d1P4AAAAA5rXob705Pckr+zfefCTJzy94fwAAAADrttBGSWvtiiQ7F7kPAAAAgN1lkZ9RAgAAALBH0SgBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6LZtdgIsxjFnnDtzzOVnnrqATPZufg5AMn8t2B21RD0CYJdl+J2wDDnAWlxRAgAAANBplAAAAAB0GiUAAAAA3aBGSVVdNGQZAAAAwJ5s4oe5VtVdktw1ycFVdVCS6qvuluTQBecGAAAAsKGmfevNLyV5ZpJ7Jbk832yUfDHJixeYFwAAAMCGm9goaa2dleSsqjq9tfaiDcoJAAAAYFNMu6IkSdJae1FVPTjJ4eMxrbXZv/waAAAAYEkNapRU1d8k+e4kVyT5Wl/ckmiUAAAAAFvGoEZJkp1JjmyttUUmAwAAALCZBn09cJKrk9xzkYkAAAAAbLahV5QcnOTaqnpvki/vWthae8xCsgIAAADYBEMbJc9dZBIAAAAAy2Dot968Y9GJAAAAAGy2od96c2tG33KTJPsmuXOSf2mt3W1RiQEAAABstKFXlByw63lVVZKTkjxoSGxV7ZPksiSfaK09ej1JAgAAAGyEod968w1t5A1JfmxgyDOSXDfrfgAAAAA22tBbb35y7OWdkuxM8u8D4g5L8qgkz0/ya+tJEAAAAGCjDP3Wm58Ye35bkhszuv1mmj9N8htJDpi2IQAAAMBmG/oZJT8/68BV9egkt7TWLq+qEyZsd1qS05Jkx44ds+5mKR1zxrkzx1x+5qkLyGT9tsIxALPbijWZrWE9v5eS2/9u8ruNPY2azDJTU9kddsfv90UY9BklVXVYVb2+qm7pj9f122omOT7JY6rqxiSvTvKwqnrFyo1aay9pre1sre3cvn37zAcAwO6jJgMsDzUZYHMM/TDXc5K8Mcm9+uNNfdmaWmvPbq0d1lo7PMkTklzcWjtljlwBAAAAFmpoo2R7a+2c1tpt/fHyJNraAAAAwJYytFHyz1V1SlXt0x+nJPnnoTtprV3SWnv0+lIEAAAA2BhDGyVPTfIzST6V5OYkJyd5yoJyAgAAANgUQ78e+PeS/Fxr7XNJUlX3SPJHGTVQAAAAALaEoVeU/MCuJkmStNY+m+QBi0kJAAAAYHMMbZTcqaoO2vWiX1Ey9GoUAAAAgD3C0GbHHyd5d1W9pr/+6STPX0xKAAAAAJtjUKOktXZuVV2W5GF90U+21q5dXFoAAAAAG2/w7TO9MaI5AgAAAGxZQz+jBAAAAGDL0ygBAAAA6DRKAAAAADqNEgAAAIBOowQAAACg0ygBAAAA6DRKAAAAALptm53AIhxzxrkzx1x+5qkLyITNZB4A7D5q6vzWcw6TrXcezaW9m58/y2Sz5+Nm75+1uaIEAAAAoNMoAQAAAOg0SgAAAAA6jRIAAACATqMEAAAAoNMoAQAAAOg0SgAAAAA6jRIAAACATqMEAAAAoNMoAQAAAOg0SgAAAAA6jRIAAACATqMEAAAAoNMoAQAAAOg0SgAAAAC6hTVKquo7q+rtVXVtVV1TVc9Y1L4AAAAAdodtCxz7tiT/tbX2vqo6IMnlVfW21tq1C9wnAAAAwLot7IqS1trNrbX39ee3JrkuyaGL2h8AAADAvDbkM0qq6vAkD0jyno3YHwAAAMB6LPLWmyRJVe2f5HVJntla++Iq609LclqS7NixY9Hp7BGOOePcdcVdfuapuzkTNtt65sL4PJg3nr3PImqyeUiyNebBMhzDZuew2fvf2/g7eTGW4e8r76X5OYcs0kKvKKmqO2fUJHlla+281bZprb2ktbaztbZz+/bti0wHgCnUZIDloSYDbI5FfutNJXlZkutaa3+yqP0AAAAA7C6LvKLk+CRPTvKwqrqiPx65wP0BAAAAzGVhn1HSWvuHJLWo8QEAAAB2tw351hsAAACAPYFGCQAAAECnUQIAAADQaZQAAAAAdBolAAAAAJ1GCQAAAECnUQIAAADQaZQAAAAAdBolAAAAAJ1GCQAAAECnUQIAAADQaZQAAAAAdBolAAAAAJ1GCQAAAEC3bbMTWOmYM86dOebyM0/dcjmw+cyDEedh77YMP/95c9gdx7AM5wF2h60wl/fmmrAMuS/DHFqGHCDZ/Lm4nv2vzEFNWJ0rSgAAAAA6jRIAAACATqMEAAAAoNMoAQAAAOg0SgAAAAA6jRIAAACATqMEAAAAoNMoAQAAAOg0SgAAAAA6jRIAAACATqMEAAAAoNMoAQAAAOg0SgAAAAA6jRIAAACATqMEAAAAoFtoo6SqTqyq66vqQ1X1rEXuCwAAAGBeC2uUVNU+Sf4syY8nOTLJE6vqyEXtDwAAAGBei7yi5NgkH2qtfaS19pUkr05y0gL3BwAAADCXRTZKDk3y8bHXN/VlAAAAAEupWmuLGbjq5CQnttZ+sb9+cpIHttaevmK705Kc1l/eL8n1E4Y9OMln5khrs+OXIQfHsBw5OIblyGGz4zcih8+01k4cOpiavMfFL0MOjmE5cnAMy5HD3lyTlyGHzY5fhhwcw3Lk4BiWI4f11+TW2kIeSY5L8tax189O8uw5x7xsT45fhhwcw3Lk4BiWI4fNjl+WHPbU3LfC+XcMy5GDY1iOHBzDnn3sy5DDZscvQw6OYTlycAzLkcM88Yu89ebSJN9TVUdU1b5JnpDkjQvcHwAAAMBcti1q4NbabVX19CRvTbJPkrNba9csan8AAAAA81pYoyRJWmtvSfKW3TjkS/bw+GXIwTEsRw6OYTly2Oz4Zclhs/a92fHLkINjWI4cHMNy5OAYNnffzr9zsCw5OIblyGGvPoaFfZgrAAAAwJ5mkZ9RAgAAALBnmfeTbBfxyOgzTd6f5M2rrLt3kouSfCDJJUkOW2WbG5NcleSKrPFJt0lO6OuvSfKOWeKTnNHXXZHk6iRfS3KPGce4e5I3Jbmy5/DzM8YflOT1/Ty8N8n9V6w/MMlrk3wwyXVJjluxvpL8zyQf6mMcPWP8f0jy7iRfTvLra5zjaWM8qe/7qiTvSvKDM8af1OOvSHJZkh+aJX5su/+Y5LYkJ6/jGE5I8oWx+fDfZ4yfOJcGxE+cRwPHWHMuZfRVhFeMPb6Y5JlD58LA+GnzYMgYa86FIfGT5sLQ+EyuKUOOYc25MDB+Yk2Y5xE1eUi8mjylJg8ZY9J7cWAOJ0RNVpOn1xQ1WU2eqyYPHGNiXR4QryYPm0tz1eUB8QuryTOMseZcGBivJq+jJu+Wgr27H0l+Lcn/zuq/AF6T5Of684cl+ZtVtrkxycETxj8wybVJdvTX3z5L/IptfyLJxevI4TeTvKA/357ks0n2nSH+zCTPad98A160Yv1fJ/nF/nzfJAeuWP/IJH+b0S+CByV5z4zx397fLM/P2n+UTxvjwUkO6s9/fB057J9v3j72A0k+OEt8X75Pkosz+iyd1Rol03I4YbV5OksOk+bSgP1PnEcDx5g4l1acq08lufesc2FK/MR5MHCMiXNhWvyQuTBl/xNrytAcJs2FATkM+jmu5xE1eUi8mjzgfThtjLE5vup7cUAOJ6w2T2fZ/6S5NGD/avLAuTApfto8GLB/NbmpyZPO/4D34cSaPHCMie/FAfFq8rC5NFddHhC/ITV5yhiD6vKEeDV5HTV56W69qarDkjwqyUvX2OTIjH5ISfL2jDpks/rZJOe11j6WJK21W9Yxxi5PTPKqdcS1JAdUVWU0eT+bUYduqG+ch9baB5McXlXfkSRVdfckD03ysr7+K621z6+IPynJuW3kH5McWFWHDI1vrd3SWrs0yVdXS27gGO9qrX2uv/zHJIfNGP+l1md7kv0yOqeD47vTk7wuyR3mwAxjrGod8bebSwPjJ86jgWOsOZdWeHiSD7fW/t/tEpgyFwbErzkPZhhjzbkwJL5bcy4MiJ+lpkzKYZdJdWWt+KE/x5moyYOpyVPeh/PWZTX5DtRkNXk1avLIwmry0DEmvRfV5Plr8gxjrDmXlqwmTxpjaF1Wk3djTV66RkmSP03yG0m+vsb6K5P8ZH/+uIwm/ret2KYlubCqLq+q01YZ475JDqqqS/o2p84YnySpqrsmOTGjSbPStDFenOR7k3wyo8uontFa+/oM8d84D1V1bEaXWu560xyR5NNJzqmq91fVS6tqvxXxhyb5+Njrm/qyofHTzDrGL2TUuZ8pvqoeV1UfTHJ+kqfOEl9Vh2Y0h/7XnMdwXFVdWVV/W1Xft55zsMZcGhI/bR4NGWPSXBr3hKzvj51Z4lfOg8FjTJgLU+MHzIVp+59WU4aMsSuXSXVlUvzQn+Os1ORh8Wpypr4P563LavLtqclq8mrU5JFF1uShY0yiJs9fk4eOMWkuLVNNHjrGpLqsJu/OmtymXHKykY8kj07y5/35CVn9ksJ7JTkvo3szz8qocK28ROrQ9s1Lna5M8tAV61+cUTduvyQHJ/mnJPcdGj+23eOTvGmNddNyODnJCzO6pO8+ST6a5G4zxN8tyTkZ3Yf1N0kuTXJUX7czo07pA/vrs5L8/or4N+f296ddlGTn0PixuOdm9csJZxnjRzK6J/Db1hPf1z80yd/NEp/R5akP6s9fnjteTjhkjLsl2b8/f2SSf1rnObjDXBq4/2nzaOgxrDqXxrbZN8lnknzHhJ/BqnNhhvg7zINZx1htLgyJnzYXBsRPrCkznodJdWVSDlN/jrM+oiaryeuIX+t9OPA8rPleHBivJk+ZCzPEq8lrzIWBOajJW7QmDx1j0ntxxng1eY25NDCHNefSDMew0Jo8wxhr1uUh8WvNhWnxk+bBwPg9siYPKswb9UjyBxkV9BszurfoX5O8YsL2+ye5acqYd5iUSZ6V5HfHXr8syU/POqkz+kCYnx1wXKvlcH6Sh4y9vjjJsbPm0NdXP2e73vT3THLj2PqHJDl/RcxfJnni2OvrkxwyNH5abkPHyOg+uQ+vfLPMksPYNh9Jv1914Dn4aD9vNyb5UkaXkj12zhxunCWHSXNp4DFMnEezHsPKuTS2/KQkF846z4fGrzUPZs1htbkwJH7aXBgQP6imDDyPa9aVoedgrZ/jrI+oyWryjPErYm73Phx4HtZ8L64zhxujJq+2Tk0efh7VZDU5GavJQ8eYcnxq8pw1eYZjWHMuzXoMK+fS2PK5avKQMdaaC7PksNZcmBY/aR4MjN8ja/JS3XrTWnt2a+2w1trhGV02c3Fr7ZTxbarq4Kralfezk5y9Yv1+VXXArudJfjSjT8Yd93+S/FBVbeuX7zwwo+7c0Phd97T9cB9r5bohY3wso3uoUqP7o+6X0aQdFF9VB1bVvv3lLyZ5Z2vti0nSWvtUko9X1f36+odn9AE6496Y5NQaeVCSL7TWbp4hfqIhY1TVjoz+1+PJrbUb1hF/n6qq/vzoJN+S5J+HxrfWjmitHd7n22uT/JfW2htmzOGeYzkcm9HtbINz6HGrzqWB8WvOoxmOYc25NGa99xhPjZ80D2YYY825MCR+2lyYFp8JNWWGMSbWlWnxA3+OM1GT1eQZ4ye+D+ety2ry7ajJarKaPCF+kTV5hjHWpCbPX5NnGGPNubRENXniGAPrspq8u2vytI7LZj0ydklhkt9L8pj+/OSMLte5IaMPsvqWFXHfldEleLu+Auq3+vKnJXna2HZnZPRGuDpjXx80Q/xTkrx6jdynjpHRpZEXZnSv3NVJTpkx/rh+Dq7P6I1z0Iocjsro658+kOQNGX0l0nh8JfmzjDqTV2XscsKB8ffM6H81vpjk8/35yu7qtDFemuRz+eZXOV02Y/x/6+fnioy+dmvl1wNPjF+x7cuz+mVk03J4es/hyowuKXvwrDlMmUvT9r/mPJphjGlzab+Miundx5YNngsD4ifOg4FjTJsLE+OnzYUh8Vmjpsw4xqS5MO0cTPw5zvuImqwmz1mTh9bEKe9FNVlNHhQfNVlNXmBNHjjGtPeimjxnTR6Yw8S6PCB+oTV54BjT5oKavICavOtrggAAAAD2ekt16w0AAADAZtIoAQAAAOg0SgAAAAA6jRIAAACATqMEAAAAoNMoYa9QVSdU1ZuHLt8N+3tsVR059vqSqto5IO6Q3ZFPVW2vqgvmHQdgEdRkgOWhJsMdaZTAYjw2yZFTt7qjX0vyV/PuvLX26SQ3V9Xx844FsAWoyQDLQ01m6WmUsBSqar+qOr+qrqyqq6vq8X35MVX1jqq6vKreWlWH9OWXVNVZVXVF3/7YvvzYqnp3Vb2/qt5VVfebMYezq+q9Pf6kvvwpVXVeVV1QVf9UVX84FvMLVXVDj/mrqnpxVT04yWOSnNnz++6++U/37W6oqoeskcZPJbmgj71PVf1RP74PVNXpffmNVfUHfezLqurofm4+XFVPGxvrDUmeNPT4AXZRk79BTQY2nZr8DWoyG2bbZicA3YlJPtlae1SSVNXdq+rOSV6U5KTW2qf7L4XnJ3lqj7lra+2oqnpokrOT3D/JB5M8pLV2W1U9Isn/yKioDvFbSS5urT21qg5M8t6q+ru+7qgkD0jy5STXV9WLknwtye8kOTrJrUkuTnJla+1dVfXGJG9urb22H0+SbGutHVtVj0zynCSPGN95VR2R5HOttS/3RaclOTzJUf147jG2+cf6sb8wycuTHJ/kLkmuTvIXfZvLkjxv4LEDjFOT1WRgeajJajIbTKOEZXFVkj+uqhdkVDj/vqrun1FRf1svoPskuXks5lVJ0lp7Z1XdrRftA5L8dVV9T5KW5M4z5PCjSR5TVb/eX98lyY7+/KLW2heSpKquTXLvJAcneUdr7bN9+WuS3HfC+Of1fy/PqLCvdEiST4+9fkSSv2it3daP87Nj697Y/70qyf6ttVuT3FpVX66qA1trn09yS5J7TT5kgFWpyWoysDzUZDWZDaZRwlJord1QVUcneWSS51XVRUlen+Sa1tpxa4Wt8vr3k7y9tfa4qjo8ySUzpFFJfqq1dv3tFlY9MKMO+S5fy/reO7vGWCv+3zL6pTPLWF9fkdvXx8a+Sx8TYCZqchI1GVgSanISNZkN5jNKWApVda8k/9pae0WSMzO6TO/6JNur6ri+zZ2r6vvGwnbdn/lDSb7QO9l3T/KJvv4pM6bx1iSnV2/LV9UDpmx/aZIfrqqDqmpbbn/p4q0Zde1ncUNu30F/W5Jf6mNnxSWFQ9w3o0sMAWaiJidRk4EloSYnUZPZYBolLIvvz+hexysyui/xea21ryQ5OckLqurKJFckefBYzL9X1fszutfwF/qyP0zyB335rN3s38/oEsQPVNU1/fWaWmufyOjezvcm+b9Jbkzyhb761UnOqNGHXX336iPcYbx/SfLhqrpPX/TSJB/r+VyZ5GdnO5z8SJLzZ4wBSNRkNRlYJmqymswGq9ZWXpUFy6+qLkny6621yzY5j/1ba1/q3ezXJzm7tfb6OcZ7XJJjWmu/vRtye2dGH/D1uXnHAphETR40lpoMbAg1edBYajITuaIE5vPc3t2/OslHM/qqsXXrvzxunDepqtqe5E8Uf2AvoyYDLA81mT2WK0oAAAAAOleUAAAAAHQaJQAAAACdRgkAAABAp1ECAAAA0GmUAAAAAHQaJQAAAADd/wf6v0tEBLrgwAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1105.92x230.4 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val = sns.FacetGrid(data,col = 'species',aspect = 1.6,size =3.2)\n",
        "val.map(sns.countplot,'sepal width (cm)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "xk8IPF9WiINW",
        "outputId": "dafda492-93a2-4a65-cf72-113ea1a7c040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f65303b9850>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1105.92x230.4 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val = sns.FacetGrid(data,col = 'species',aspect = 1.6,size =3.2)\n",
        "val.map(sns.countplot,'petal length (cm)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "4F1BHlKFiqt4",
        "outputId": "a5c9a125-35ac-4692-aeb0-8fb037104e22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f6530060090>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1105.92x230.4 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val = sns.FacetGrid(data,col = 'species',aspect = 1.6,size =3.2)\n",
        "val.map(sns.countplot,'petal width (cm)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "lNZIggq9itt-",
        "outputId": "3146e526-165e-48f0-f219-a3a76d20fefa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f652fd84c50>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1105.92x230.4 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.1, random_state=42) "
      ],
      "metadata": {
        "id": "v1Bj04xJiwbG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train)\n",
        "print(y_train)\n",
        "print(x_test)\n",
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vu0uVWXgjEUI",
        "outputId": "38c27d50-eb18-4efc-9826-e4b630bc0145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.3 3.3 4.7 1.6]\n",
            " [6.5 3.  5.8 2.2]\n",
            " [5.6 2.5 3.9 1.1]\n",
            " [5.7 2.8 4.5 1.3]\n",
            " [6.4 2.8 5.6 2.2]\n",
            " [4.7 3.2 1.6 0.2]\n",
            " [6.1 3.  4.9 1.8]\n",
            " [5.  3.4 1.6 0.4]\n",
            " [6.4 2.8 5.6 2.1]\n",
            " [7.9 3.8 6.4 2. ]\n",
            " [6.7 3.  5.2 2.3]\n",
            " [6.7 2.5 5.8 1.8]\n",
            " [6.8 3.2 5.9 2.3]\n",
            " [4.8 3.  1.4 0.3]\n",
            " [4.8 3.1 1.6 0.2]\n",
            " [4.6 3.6 1.  0.2]\n",
            " [5.7 4.4 1.5 0.4]\n",
            " [6.7 3.1 4.4 1.4]\n",
            " [4.8 3.4 1.6 0.2]\n",
            " [4.4 3.2 1.3 0.2]\n",
            " [6.3 2.5 5.  1.9]\n",
            " [6.4 3.2 4.5 1.5]\n",
            " [5.2 3.5 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]\n",
            " [5.2 4.1 1.5 0.1]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [6.  3.4 4.5 1.6]\n",
            " [6.7 3.1 4.7 1.5]\n",
            " [5.4 3.9 1.3 0.4]\n",
            " [5.4 3.7 1.5 0.2]\n",
            " [5.5 2.4 3.7 1. ]\n",
            " [6.3 2.8 5.1 1.5]\n",
            " [6.4 3.1 5.5 1.8]\n",
            " [6.6 3.  4.4 1.4]\n",
            " [7.2 3.6 6.1 2.5]\n",
            " [5.7 2.9 4.2 1.3]\n",
            " [7.6 3.  6.6 2.1]\n",
            " [5.6 3.  4.5 1.5]\n",
            " [5.1 3.5 1.4 0.2]\n",
            " [7.7 2.8 6.7 2. ]\n",
            " [5.8 2.7 4.1 1. ]\n",
            " [5.2 3.4 1.4 0.2]\n",
            " [5.  3.5 1.3 0.3]\n",
            " [5.1 3.8 1.9 0.4]\n",
            " [5.  2.  3.5 1. ]\n",
            " [6.3 2.7 4.9 1.8]\n",
            " [4.8 3.4 1.9 0.2]\n",
            " [5.  3.  1.6 0.2]\n",
            " [5.1 3.3 1.7 0.5]\n",
            " [5.6 2.7 4.2 1.3]\n",
            " [5.1 3.4 1.5 0.2]\n",
            " [5.7 3.  4.2 1.2]\n",
            " [7.7 3.8 6.7 2.2]\n",
            " [4.6 3.2 1.4 0.2]\n",
            " [6.2 2.9 4.3 1.3]\n",
            " [5.7 2.5 5.  2. ]\n",
            " [5.5 4.2 1.4 0.2]\n",
            " [6.  3.  4.8 1.8]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [6.  2.2 4.  1. ]\n",
            " [5.4 3.  4.5 1.5]\n",
            " [6.2 3.4 5.4 2.3]\n",
            " [5.5 2.3 4.  1.3]\n",
            " [5.4 3.9 1.7 0.4]\n",
            " [5.  2.3 3.3 1. ]\n",
            " [6.4 2.7 5.3 1.9]\n",
            " [5.  3.3 1.4 0.2]\n",
            " [5.  3.2 1.2 0.2]\n",
            " [5.5 2.4 3.8 1.1]\n",
            " [6.7 3.  5.  1.7]\n",
            " [4.9 3.1 1.5 0.2]\n",
            " [5.8 2.8 5.1 2.4]\n",
            " [5.  3.4 1.5 0.2]\n",
            " [5.  3.5 1.6 0.6]\n",
            " [5.9 3.2 4.8 1.8]\n",
            " [5.1 2.5 3.  1.1]\n",
            " [6.9 3.2 5.7 2.3]\n",
            " [6.  2.7 5.1 1.6]\n",
            " [6.1 2.6 5.6 1.4]\n",
            " [7.7 3.  6.1 2.3]\n",
            " [5.5 2.5 4.  1.3]\n",
            " [4.4 2.9 1.4 0.2]\n",
            " [4.3 3.  1.1 0.1]\n",
            " [6.  2.2 5.  1.5]\n",
            " [7.2 3.2 6.  1.8]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.1 3.5 1.4 0.3]\n",
            " [4.4 3.  1.3 0.2]\n",
            " [6.3 2.5 4.9 1.5]\n",
            " [6.3 3.4 5.6 2.4]\n",
            " [4.6 3.4 1.4 0.3]\n",
            " [6.8 3.  5.5 2.1]\n",
            " [6.3 3.3 6.  2.5]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [6.1 2.9 4.7 1.4]\n",
            " [6.5 2.8 4.6 1.5]\n",
            " [6.2 2.8 4.8 1.8]\n",
            " [7.  3.2 4.7 1.4]\n",
            " [6.4 3.2 5.3 2.3]\n",
            " [5.1 3.8 1.6 0.2]\n",
            " [6.9 3.1 5.4 2.1]\n",
            " [5.9 3.  4.2 1.5]\n",
            " [6.5 3.  5.2 2. ]\n",
            " [5.7 2.6 3.5 1. ]\n",
            " [5.2 2.7 3.9 1.4]\n",
            " [6.1 3.  4.6 1.4]\n",
            " [4.5 2.3 1.3 0.3]\n",
            " [6.6 2.9 4.6 1.3]\n",
            " [5.5 2.6 4.4 1.2]\n",
            " [5.3 3.7 1.5 0.2]\n",
            " [5.6 3.  4.1 1.3]\n",
            " [7.3 2.9 6.3 1.8]\n",
            " [6.7 3.3 5.7 2.1]\n",
            " [5.1 3.7 1.5 0.4]\n",
            " [4.9 2.4 3.3 1. ]\n",
            " [6.7 3.3 5.7 2.5]\n",
            " [7.2 3.  5.8 1.6]\n",
            " [4.9 3.6 1.4 0.1]\n",
            " [6.7 3.1 5.6 2.4]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [6.9 3.1 4.9 1.5]\n",
            " [7.4 2.8 6.1 1.9]\n",
            " [6.3 2.9 5.6 1.8]\n",
            " [5.7 2.8 4.1 1.3]\n",
            " [6.5 3.  5.5 1.8]\n",
            " [6.3 2.3 4.4 1.3]\n",
            " [6.4 2.9 4.3 1.3]\n",
            " [5.6 2.8 4.9 2. ]\n",
            " [5.9 3.  5.1 1.8]\n",
            " [5.4 3.4 1.7 0.2]\n",
            " [6.1 2.8 4.  1.3]\n",
            " [4.9 2.5 4.5 1.7]\n",
            " [5.8 4.  1.2 0.2]\n",
            " [5.8 2.6 4.  1.2]\n",
            " [7.1 3.  5.9 2.1]]\n",
            "[1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1 0 0 0 2 1 1 0 0 1 2 2 1 2 1 2\n",
            " 1 0 2 1 0 0 0 1 2 0 0 0 1 0 1 2 0 1 2 0 2 2 1 1 2 1 0 1 2 0 0 1 1 0 2 0 0\n",
            " 1 1 2 1 2 2 1 0 0 2 2 0 0 0 1 2 0 2 2 0 1 1 2 1 2 0 2 1 2 1 1 1 0 1 1 0 1\n",
            " 2 2 0 1 2 2 0 2 0 1 2 2 1 2 1 1 2 2 0 1 2 0 1 2]\n",
            "[[6.1 2.8 4.7 1.2]\n",
            " [5.7 3.8 1.7 0.3]\n",
            " [7.7 2.6 6.9 2.3]\n",
            " [6.  2.9 4.5 1.5]\n",
            " [6.8 2.8 4.8 1.4]\n",
            " [5.4 3.4 1.5 0.4]\n",
            " [5.6 2.9 3.6 1.3]\n",
            " [6.9 3.1 5.1 2.3]\n",
            " [6.2 2.2 4.5 1.5]\n",
            " [5.8 2.7 3.9 1.2]\n",
            " [6.5 3.2 5.1 2. ]\n",
            " [4.8 3.  1.4 0.1]\n",
            " [5.5 3.5 1.3 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.1 3.8 1.5 0.3]]\n",
            "[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = { \n",
        "    'GradientBoostingClassifier' : GradientBoostingClassifier(max_depth=1, n_estimators=3, learning_rate=1.0, random_state=42),\n",
        "    'Logistic_Regression' : LogisticRegression(random_state=42),\n",
        "    'DecisionTreeClassifier': DecisionTreeClassifier(max_depth=1, random_state=42) ,\n",
        "    'SVC' : SVC(kernel='rbf', gamma=0.5, probability=True),\n",
        "} "
      ],
      "metadata": {
        "id": "wvu_jvwWjR9o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stats(a,b,retur=False):\n",
        "  pre = precision_score(a,b,average='macro')\n",
        "  rec = recall_score(a,b,average ='macro')\n",
        "  avg = accuracy_score(a,b)\n",
        "\n",
        "  if retur:\n",
        "    return pre, rec, avg \n",
        "  else:\n",
        "    print(pre)\n",
        "    print(rec)\n",
        "    print(avg)"
      ],
      "metadata": {
        "id": "kVcUAEJklSna"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluating_models(models,x,y):\n",
        "  for name, model in models.items():\n",
        "    print(name,':')\n",
        "    model.fit(x,y)\n",
        "    stats(y,model.predict(x))\n",
        "    print('*'*25)"
      ],
      "metadata": {
        "id": "KXf5awRGmZoK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluating_models(models,x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdSu4pkSm1xy",
        "outputId": "8c8aa653-094b-4326-c8c2-91b91104fc80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GradientBoostingClassifier :\n",
            "0.9716553287981861\n",
            "0.9701805286911669\n",
            "0.9703703703703703\n",
            "*************************\n",
            "Logistic_Regression :\n",
            "0.98\n",
            "0.9772727272727272\n",
            "0.9777777777777777\n",
            "*************************\n",
            "DecisionTreeClassifier :\n",
            "0.5054945054945055\n",
            "0.6666666666666666\n",
            "0.674074074074074\n",
            "*************************\n",
            "SVC :\n",
            "0.98\n",
            "0.9772727272727272\n",
            "0.9777777777777777\n",
            "*************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res = GradientBoostingClassifier(max_depth = 3, n_estimators=10 ,learning_rate =0.1, random_state=42)\n",
        "res.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NTpswGPm6Y7",
        "outputId": "6ce620a8-6d18-4c64-c80f-ddf6eee2501e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(n_estimators=10, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res.predict([[1.2,2.3,1.6,1.0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7c5GGzAnpGo",
        "outputId": "7458fad1-30cd-42a4-e953-d789c9f05789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res.predict([[6.7,3.1,5.6,2.4]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfdAdZt5nxYd",
        "outputId": "ce3ff517-2e46-4982-87c1-fb2456b119cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res.predict([[7,3.2,4.7,1.4]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQ8lqVKSn8QV",
        "outputId": "f3e374b4-cf2a-4239-8f22-c96789b87dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading Process \n",
        "sl = float(input(\"Enter Sepal Length: \"))\n",
        "sw = float(input(\"Enter Sepal Width: \"))\n",
        "pl = float(input(\"Enter Petal Length: \"))\n",
        "pw = float(input(\"Enter Petal Width: \"))\n",
        "\n",
        "ans = res.predict([[sl,sw,pl,pw]])[0]\n",
        "\n",
        "if ans == 0:\n",
        "  print('Iris-Setosa')\n",
        "elif ans == 1:\n",
        "  print('Iris-Versicolour')\n",
        "elif ans ==2 :\n",
        "  print('Iris-Virginica')\n",
        "else:\n",
        "  print(\"Machine Failed !!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HE49peEfoC2Y",
        "outputId": "dff2dce4-a0cb-47e7-b1a9-a15f59c44607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter Sepal Length: 6.1\n",
            "Enter Sepal Width: 5\n",
            "Enter Petal Length: 3.2\n",
            "Enter Petal Width: 4.1\n",
            "Iris-Virginica\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vbJE99TBvnAX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}