Untitled17.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled17.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMhOY92plVC0QjL1coiq6jg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Pradeepjha88/Logistic-regression/blob/main/Untitled17.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_b8a0Vd6YZF",
        "outputId": "66b3ad04-8ebe-4426-bace-c6b17301e203"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "bank = pd.read_csv(\"bank-full.csv\",sep=';')\n",
        "\n",
        "dum_bank = pd.get_dummies(bank, drop_first=True)\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "#from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "X = dum_bank.iloc[:,0:-1]\n",
        "y = dum_bank.iloc[:,-1]\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "logreg = LogisticRegression()\n",
        "kfold = StratifiedKFold(n_splits=5, random_state=2021,\n",
        "                        shuffle=True)\n",
        "results = cross_val_score(logreg, X, y, cv=kfold, \n",
        "                          scoring='roc_auc')\n",
        "AUCs = results\n",
        "print(AUCs)\n",
        "print(\"Mean AUC: %.2f\" % (AUCs.mean()))\n",
        "\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, \n",
        "                                                    random_state=2021,\n",
        "                                                    stratify=y)\n",
        "\n",
        "# Create the classifier: logreg\n",
        "logreg = LogisticRegression()\n",
        "# Fit the classifier to the training data\n",
        "logreg.fit(X_train,y_train)\n",
        "# Predict the labels of the test set: y_pred\n",
        "y_pred = logreg.predict(X_test)\n",
        "\n",
        "# Coeff\n",
        "print(logreg.coef_)\n",
        "print(logreg.intercept_)\n",
        "\n",
        "# Compute and print the confusion matrix and classification report\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(accuracy_score(y_test,y_pred))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[0.85721006 0.85864046 0.85360904 0.84922835 0.8459169 ]\n",
            "Mean AUC: 0.85\n",
            "[[-1.75770849e-02  2.22084315e-05 -1.01540914e-02  3.83755636e-03\n",
            "  -4.04732291e-01  2.59285474e-03 -1.49005248e-01 -2.81823523e-01\n",
            "  -2.60113008e-02 -1.11505758e-02  2.04392359e-02  1.71947306e-01\n",
            "  -2.29826705e-02 -9.71688184e-02  2.67160886e-02 -7.63201213e-02\n",
            "   2.62484930e-03  3.44395016e-03 -1.99371085e-01 -1.40275625e-01\n",
            "  -3.08389503e-01  4.56363652e-02  2.21338922e-02 -2.06979653e-02\n",
            "  -7.28592711e-01 -2.09535086e-01  4.87348518e-02 -4.56365211e-01\n",
            "   2.82180891e-02  3.12785542e-02 -1.01419303e-02 -1.98103449e-02\n",
            "  -9.33959739e-02 -5.25923702e-02  9.40990642e-02 -4.54773762e-01\n",
            "  -6.13440529e-02  9.85000911e-02  7.73342638e-02 -3.45403966e-02\n",
            "   3.38104724e-01 -4.67749404e-01]]\n",
            "[-0.31454048]\n",
            "[[11730   247]\n",
            " [ 1246   341]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.98      0.94     11977\n",
            "           1       0.58      0.21      0.31      1587\n",
            "\n",
            "    accuracy                           0.89     13564\n",
            "   macro avg       0.74      0.60      0.63     13564\n",
            "weighted avg       0.87      0.89      0.87     13564\n",
            "\n",
            "0.8899292244175759\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "09YuslF57jxW",
        "outputId": "3c9f6830-d836-4f25-eac5-9ffa5f8d4967"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "bank = pd.read_csv(\"bank-full.csv\",sep=';')\n",
        "bank"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>37</td>\n",
              "      <td>entrepreneur</td>\n",
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              "<p>45211 rows × 17 columns</p>\n",
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            ],
            "text/plain": [
              "       age           job   marital  education  ... pdays  previous poutcome    y\n",
              "0       58    management   married   tertiary  ...    -1         0  unknown   no\n",
              "1       44    technician    single  secondary  ...    -1         0  unknown   no\n",
              "2       33  entrepreneur   married  secondary  ...    -1         0  unknown   no\n",
              "3       47   blue-collar   married    unknown  ...    -1         0  unknown   no\n",
              "4       33       unknown    single    unknown  ...    -1         0  unknown   no\n",
              "...    ...           ...       ...        ...  ...   ...       ...      ...  ...\n",
              "45206   51    technician   married   tertiary  ...    -1         0  unknown  yes\n",
              "45207   71       retired  divorced    primary  ...    -1         0  unknown  yes\n",
              "45208   72       retired   married  secondary  ...   184         3  success  yes\n",
              "45209   57   blue-collar   married  secondary  ...    -1         0  unknown   no\n",
              "45210   37  entrepreneur   married  secondary  ...   188        11    other   no\n",
              "\n",
              "[45211 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "4hJRzn3LGqHV",
        "outputId": "3a18b2b5-886e-46c0-e3be-3fac91ad1579"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "df = pd.read_csv(\"Image_Segmention.csv\")\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "lbcode = LabelEncoder()\n",
        "\n",
        "X = df.iloc[:,0:-1]\n",
        "y = df.iloc[:,1]\n",
        "\n",
        "y = lbcode.fit_transform(y)\n",
        "\n",
        "# Import the necessary modules\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "\n",
        "################ Multiclass ROC #############################\n",
        "\n",
        "# Import necessary modules\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "\n",
        "#########################K-Fold CV####################################\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "logreg = LogisticRegression()\n",
        "kfold = StratifiedKFold(n_splits=5 random_state=2021,\n",
        "                        shuffle=True)\n",
        "results = cross_val_score(logreg, X, y, cv=kfold, scoring='roc_auc_ovr')\n",
        "AUCs = results\n",
        "print(AUCs)\n",
        "print(\"Mean AUC: %.2f\" % (AUCs.mean()))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-25-7e41113d8efd>\"\u001b[0;36m, line \u001b[0;32m32\u001b[0m\n\u001b[0;31m    kfold = StratifiedKFold(n_splits=5 random_state=2021,\u001b[0m\n\u001b[0m                                                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "BKNatiZyGx22",
        "outputId": "908b67a3-0fba-4c50-9d0d-b8f503a36e57"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "df = pd.read_csv(\"Image_Segmention.csv\")\n",
        "df"
      ],
      "execution_count": 9,
      "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>Class</th>\n",
              "      <th>region.centroid.col</th>\n",
              "      <th>region.centroid.row</th>\n",
              "      <th>region.pixel.count</th>\n",
              "      <th>short.line.density.5</th>\n",
              "      <th>short.line.density.2</th>\n",
              "      <th>vedge.mean</th>\n",
              "      <th>vegde.sd</th>\n",
              "      <th>hedge.mean</th>\n",
              "      <th>hedge.sd</th>\n",
              "      <th>intensity.mean</th>\n",
              "      <th>rawred.mean</th>\n",
              "      <th>rawblue.mean</th>\n",
              "      <th>rawgreen.mean</th>\n",
              "      <th>exred.mean</th>\n",
              "      <th>exblue.mean</th>\n",
              "      <th>exgreen.mean</th>\n",
              "      <th>value.mean</th>\n",
              "      <th>saturation.mean</th>\n",
              "      <th>hue-mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>188</td>\n",
              "      <td>133</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.077778</td>\n",
              "      <td>6.666666</td>\n",
              "      <td>8.333334</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>3.888889</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>-8.333333</td>\n",
              "      <td>8.444445</td>\n",
              "      <td>0.538580</td>\n",
              "      <td>-0.924817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>105</td>\n",
              "      <td>139</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.107407</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.522222</td>\n",
              "      <td>6.111111</td>\n",
              "      <td>7.555555</td>\n",
              "      <td>7.222222</td>\n",
              "      <td>3.555556</td>\n",
              "      <td>4.333334</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>-7.666666</td>\n",
              "      <td>7.555555</td>\n",
              "      <td>0.532628</td>\n",
              "      <td>-0.965946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>34</td>\n",
              "      <td>137</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>1.111111</td>\n",
              "      <td>0.474074</td>\n",
              "      <td>5.851852</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>6.444445</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>5.777778</td>\n",
              "      <td>1.777778</td>\n",
              "      <td>-7.555555</td>\n",
              "      <td>7.777778</td>\n",
              "      <td>0.573633</td>\n",
              "      <td>-0.744272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>39</td>\n",
              "      <td>111</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.374074</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.429629</td>\n",
              "      <td>6.037037</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.666666</td>\n",
              "      <td>3.444444</td>\n",
              "      <td>2.888889</td>\n",
              "      <td>4.888889</td>\n",
              "      <td>-7.777778</td>\n",
              "      <td>7.888889</td>\n",
              "      <td>0.562919</td>\n",
              "      <td>-1.175773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BRICKFACE</td>\n",
              "      <td>16</td>\n",
              "      <td>128</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.077778</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.311111</td>\n",
              "      <td>5.555555</td>\n",
              "      <td>6.888889</td>\n",
              "      <td>6.666666</td>\n",
              "      <td>3.111111</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>-7.333334</td>\n",
              "      <td>7.111111</td>\n",
              "      <td>0.561508</td>\n",
              "      <td>-0.985811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>36</td>\n",
              "      <td>243</td>\n",
              "      <td>9</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.888889</td>\n",
              "      <td>1.851851</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.711110</td>\n",
              "      <td>13.333333</td>\n",
              "      <td>9.888889</td>\n",
              "      <td>12.111111</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>-10.333333</td>\n",
              "      <td>-3.666667</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.452229</td>\n",
              "      <td>2.368310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>186</td>\n",
              "      <td>218</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.744444</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.655555</td>\n",
              "      <td>13.703704</td>\n",
              "      <td>10.666667</td>\n",
              "      <td>12.666667</td>\n",
              "      <td>17.777779</td>\n",
              "      <td>-9.111111</td>\n",
              "      <td>-3.111111</td>\n",
              "      <td>12.222222</td>\n",
              "      <td>17.777779</td>\n",
              "      <td>0.401347</td>\n",
              "      <td>2.382683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>197</td>\n",
              "      <td>236</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.444444</td>\n",
              "      <td>6.829628</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>7.599998</td>\n",
              "      <td>16.074074</td>\n",
              "      <td>13.111111</td>\n",
              "      <td>16.666668</td>\n",
              "      <td>18.444445</td>\n",
              "      <td>-8.888889</td>\n",
              "      <td>1.777778</td>\n",
              "      <td>7.111111</td>\n",
              "      <td>18.555555</td>\n",
              "      <td>0.292729</td>\n",
              "      <td>2.789800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>208</td>\n",
              "      <td>240</td>\n",
              "      <td>9</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.055556</td>\n",
              "      <td>0.862963</td>\n",
              "      <td>2.444444</td>\n",
              "      <td>5.007407</td>\n",
              "      <td>14.148149</td>\n",
              "      <td>10.888889</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>18.555555</td>\n",
              "      <td>-9.777778</td>\n",
              "      <td>-3.444444</td>\n",
              "      <td>13.222222</td>\n",
              "      <td>18.555555</td>\n",
              "      <td>0.421621</td>\n",
              "      <td>2.392487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>GRASS</td>\n",
              "      <td>223</td>\n",
              "      <td>185</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.349603</td>\n",
              "      <td>2.388889</td>\n",
              "      <td>2.080776</td>\n",
              "      <td>12.962963</td>\n",
              "      <td>11.555555</td>\n",
              "      <td>9.777778</td>\n",
              "      <td>17.555555</td>\n",
              "      <td>-4.222222</td>\n",
              "      <td>-9.555555</td>\n",
              "      <td>13.777778</td>\n",
              "      <td>17.555555</td>\n",
              "      <td>0.445418</td>\n",
              "      <td>1.838850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>209 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Class  region.centroid.col  ...  saturation.mean  hue-mean\n",
              "0    BRICKFACE                  188  ...         0.538580 -0.924817\n",
              "1    BRICKFACE                  105  ...         0.532628 -0.965946\n",
              "2    BRICKFACE                   34  ...         0.573633 -0.744272\n",
              "3    BRICKFACE                   39  ...         0.562919 -1.175773\n",
              "4    BRICKFACE                   16  ...         0.561508 -0.985811\n",
              "..         ...                  ...  ...              ...       ...\n",
              "204      GRASS                   36  ...         0.452229  2.368310\n",
              "205      GRASS                  186  ...         0.401347  2.382683\n",
              "206      GRASS                  197  ...         0.292729  2.789800\n",
              "207      GRASS                  208  ...         0.421621  2.392487\n",
              "208      GRASS                  223  ...         0.445418  1.838850\n",
              "\n",
              "[209 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    }
  ]
}