AutoML_Predict_Customer_Churn_with_Python_and_Machine_Learning_2.ipynb

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      "execution_count": null,
      "metadata": {
        "id": "_qO1-k5bVm_4"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "!pip install pycaret\n",
        "from pycaret.utils import version\n",
        "version()\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install numpy==1.21.4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1mZceKaUK-E",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: numpy==1.21.4 in /usr/local/lib/python3.7/dist-packages (1.21.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSMNdQ5VWmuc",
        "outputId": "05581383-8ec1-404e-a18c-766bf45c90b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://pycaret.gitbook.io/docs/"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/dataset/churn.csv')\n",
        "df.head(5)"
      ],
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        "colab": {
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        "id": "WtBc_nezWm77",
        "outputId": "d6c6c4f8-8372-485e-8b11-62a816f9e4d7"
      },
      "execution_count": null,
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            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
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              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "MiY0vWkkkjm6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96a2a9cf-2e6f-4536-faa5-90bfdbf4a948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   int64  \n",
            " 1   gender            7043 non-null   int64  \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   int64  \n",
            " 4   Dependents        7043 non-null   int64  \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   int64  \n",
            " 7   MultipleLines     7043 non-null   int64  \n",
            " 8   InternetService   7043 non-null   int64  \n",
            " 9   OnlineSecurity    7043 non-null   int64  \n",
            " 10  OnlineBackup      7043 non-null   int64  \n",
            " 11  DeviceProtection  7043 non-null   int64  \n",
            " 12  TechSupport       7043 non-null   int64  \n",
            " 13  StreamingTV       7043 non-null   int64  \n",
            " 14  StreamingMovies   7043 non-null   int64  \n",
            " 15  Contract          7043 non-null   int64  \n",
            " 16  PaperlessBilling  7043 non-null   int64  \n",
            " 17  PaymentMethod     7043 non-null   int64  \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   int64  \n",
            " 20  Churn             7043 non-null   int64  \n",
            "dtypes: float64(1), int64(20)\n",
            "memory usage: 1.1 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleanDF = df.drop('customerID', axis=1)"
      ],
      "metadata": {
        "id": "uKLKsRE9aYbM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in cleanDF.columns:\n",
        "  if cleanDF[column].dtype == np.number:\n",
        "    continue\n",
        "  cleanDF[column] = LabelEncoder().fit_transform(cleanDF[column])"
      ],
      "metadata": {
        "id": "CFt-EmPsT9Y6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://pycaret.gitbook.io/docs/get-started/preprocessing/data-preparation"
      ],
      "metadata": {
        "id": "m6IvseL9d37W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "\n",
        "s = setup(data = cleanDF, target = 'Churn', session_id=123\n",
        "          , normalize = True\n",
        "          , normalize_method = 'minmax'\n",
        " #         , ignore_features = ['customerID']\n",
        "          , train_size=0.8\n",
        "          , numeric_features = ['tenure','TotalCharges','MonthlyCharges']\n",
        "          , categorical_features = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']\n",
        "          , use_gpu = True\n",
        "          , transformation = True\n",
        "          , ignore_low_variance = True\n",
        "          , remove_multicollinearity = True)\n",
        "          , multicollinearity_threshold = 0.95)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c7ac3f109e2446559ff1405592e12146",
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            "b124760cbe724ed099d315120d4151a3",
            "6c3c50e7fe4c4d23b831dd0584afd12d"
          ]
        },
        "id": "zApR5uskYtQt",
        "outputId": "565600b3-a11a-403f-ce37-9b40ee1f7a92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id               123\n",
              "1                                   Target             Churn\n",
              "2                              Target Type            Binary\n",
              "3                            Label Encoded              None\n",
              "4                            Original Data        (7043, 20)\n",
              "5                           Missing Values             False\n",
              "6                         Numeric Features                 3\n",
              "7                     Categorical Features                16\n",
              "8                         Ordinal Features             False\n",
              "9                High Cardinality Features             False\n",
              "10                 High Cardinality Method              None\n",
              "11                   Transformed Train Set        (5634, 32)\n",
              "12                    Transformed Test Set        (1409, 32)\n",
              "13                      Shuffle Train-Test              True\n",
              "14                     Stratify Train-Test             False\n",
              "15                          Fold Generator   StratifiedKFold\n",
              "16                             Fold Number                10\n",
              "17                                CPU Jobs                -1\n",
              "18                                 Use GPU              True\n",
              "19                          Log Experiment             False\n",
              "20                         Experiment Name  clf-default-name\n",
              "21                                     USI              d1f9\n",
              "22                         Imputation Type            simple\n",
              "23          Iterative Imputation Iteration              None\n",
              "24                         Numeric Imputer              mean\n",
              "25      Iterative Imputation Numeric Model              None\n",
              "26                     Categorical Imputer          constant\n",
              "27  Iterative Imputation Categorical Model              None\n",
              "28           Unknown Categoricals Handling    least_frequent\n",
              "29                               Normalize              True\n",
              "30                        Normalize Method            minmax\n",
              "31                          Transformation              True\n",
              "32                   Transformation Method       yeo-johnson\n",
              "33                                     PCA             False\n",
              "34                              PCA Method              None\n",
              "35                          PCA Components              None\n",
              "36                     Ignore Low Variance              True\n",
              "37                     Combine Rare Levels             False\n",
              "38                    Rare Level Threshold              None\n",
              "39                         Numeric Binning             False\n",
              "40                         Remove Outliers             False\n",
              "41                      Outliers Threshold              None\n",
              "42                Remove Multicollinearity              True\n",
              "43             Multicollinearity Threshold               0.9\n",
              "44             Remove Perfect Collinearity              True\n",
              "45                              Clustering             False\n",
              "46                    Clustering Iteration              None\n",
              "47                     Polynomial Features             False\n",
              "48                       Polynomial Degree              None\n",
              "49                    Trignometry Features             False\n",
              "50                    Polynomial Threshold              None\n",
              "51                          Group Features             False\n",
              "52                       Feature Selection             False\n",
              "53                Feature Selection Method           classic\n",
              "54            Features Selection Threshold              None\n",
              "55                     Feature Interaction             False\n",
              "56                           Feature Ratio             False\n",
              "57                   Interaction Threshold              None\n",
              "58                           Fix Imbalance             False\n",
              "59                    Fix Imbalance Method             SMOTE"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>Churn</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Target Type</td>\n",
              "      <td>Binary</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Label Encoded</td>\n",
              "      <td>None</td>\n",
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              "      <th>4</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(7043, 20)</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
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              "      <td>False</td>\n",
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              "      <th>6</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
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              "      <th>8</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
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              "      <th>9</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(5634, 32)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(1409, 32)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
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              "      <th>17</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>USI</td>\n",
              "      <td>d1f9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>minmax</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>yeo-johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Remove Perfect Collinearity</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Fix Imbalance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Fix Imbalance Method</td>\n",
              "      <td>SMOTE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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        {
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            "INFO:logs:create_model_container: 0\n",
            "INFO:logs:master_model_container: 0\n",
            "INFO:logs:display_container: 1\n",
            "INFO:logs:Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=['gender',\n",
            "                                                            'SeniorCitizen',\n",
            "                                                            'Partner',\n",
            "                                                            'Dependents',\n",
            "                                                            'PhoneService',\n",
            "                                                            'MultipleLines',\n",
            "                                                            'InternetService',\n",
            "                                                            'OnlineSecurity',\n",
            "                                                            'OnlineBackup',\n",
            "                                                            'DeviceProtection',\n",
            "                                                            'TechSupport',\n",
            "                                                            'StreamingTV',\n",
            "                                                            'StreamingMovies',\n",
            "                                                            'Contract',\n",
            "                                                            'PaperlessBilling',\n",
            "                                                            'PaymentMethod'],\n",
            "                                      display_types=True, features_...\n",
            "                ('dummy', Dummify(target='Churn')),\n",
            "                ('fix_perfect', Remove_100(target='Churn')),\n",
            "                ('clean_names', Clean_Colum_Names()),\n",
            "                ('feature_select', 'passthrough'),\n",
            "                ('fix_multi',\n",
            "                 Fix_multicollinearity(correlation_with_target_preference=None,\n",
            "                                       correlation_with_target_threshold=0.0,\n",
            "                                       target_variable='Churn',\n",
            "                                       threshold=0.9)),\n",
            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False)\n",
            "INFO:logs:setup() succesfully completed......................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "4heNg8s8cUEy",
        "outputId": "bca8e359-3008-4ad8-f766-102b1555158b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:gpu_param set to True\n",
            "WARNING:logs:Couldn't import cuml.linear_model.LogisticRegression\n",
            "WARNING:logs:Couldn't import cuml.neighbors.KNeighborsClassifier\n",
            "WARNING:logs:Couldn't import cuml.MBSGDClassifier\n",
            "WARNING:logs:Couldn't import cuml.svm.SVC\n",
            "WARNING:logs:Couldn't import cuml.linear_model\n",
            "WARNING:logs:Couldn't import cuml.ensemble\n",
            "WARNING:logs:Wrong xgboost version. Expected xgboost>=1.1.0, got xgboost==0.90\n",
            "WARNING:logs:Couldn't import catboost.CatBoostClassifier\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     Name  \\\n",
              "ID                                          \n",
              "lr                    Logistic Regression   \n",
              "knn                K Neighbors Classifier   \n",
              "nb                            Naive Bayes   \n",
              "dt               Decision Tree Classifier   \n",
              "svm                   SVM - Linear Kernel   \n",
              "rbfsvm                SVM - Radial Kernel   \n",
              "gpc           Gaussian Process Classifier   \n",
              "mlp                        MLP Classifier   \n",
              "ridge                    Ridge Classifier   \n",
              "rf               Random Forest Classifier   \n",
              "qda       Quadratic Discriminant Analysis   \n",
              "ada                  Ada Boost Classifier   \n",
              "gbc          Gradient Boosting Classifier   \n",
              "lda          Linear Discriminant Analysis   \n",
              "et                 Extra Trees Classifier   \n",
              "lightgbm  Light Gradient Boosting Machine   \n",
              "dummy                    Dummy Classifier   \n",
              "\n",
              "                                                  Reference  Turbo  \n",
              "ID                                                                  \n",
              "lr        sklearn.linear_model._logistic.LogisticRegression   True  \n",
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              "nb                           sklearn.naive_bayes.GaussianNB   True  \n",
              "dt             sklearn.tree._classes.DecisionTreeClassifier   True  \n",
              "svm       sklearn.linear_model._stochastic_gradient.SGDC...   True  \n",
              "rbfsvm                             sklearn.svm._classes.SVC  False  \n",
              "gpc       sklearn.gaussian_process._gpc.GaussianProcessC...  False  \n",
              "mlp       sklearn.neural_network._multilayer_perceptron....  False  \n",
              "ridge           sklearn.linear_model._ridge.RidgeClassifier   True  \n",
              "rf          sklearn.ensemble._forest.RandomForestClassifier   True  \n",
              "qda       sklearn.discriminant_analysis.QuadraticDiscrim...   True  \n",
              "ada       sklearn.ensemble._weight_boosting.AdaBoostClas...   True  \n",
              "gbc         sklearn.ensemble._gb.GradientBoostingClassifier   True  \n",
              "lda       sklearn.discriminant_analysis.LinearDiscrimina...   True  \n",
              "et            sklearn.ensemble._forest.ExtraTreesClassifier   True  \n",
              "lightgbm                    lightgbm.sklearn.LGBMClassifier   True  \n",
              "dummy                         sklearn.dummy.DummyClassifier   True  "
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              "      <td>True</td>\n",
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              "      <th>lda</th>\n",
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              "      <td>True</td>\n",
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        "top3 = compare_models(n_select = 3)"
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            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "lr                    Logistic Regression    0.8044  0.8445  0.5228  0.6599   \n",
              "ridge                    Ridge Classifier    0.8021  0.0000  0.4909  0.6655   \n",
              "gbc          Gradient Boosting Classifier    0.8012  0.8460  0.5105  0.6549   \n",
              "ada                  Ada Boost Classifier    0.8010  0.8455  0.5180  0.6511   \n",
              "lightgbm  Light Gradient Boosting Machine    0.7996  0.8369  0.5126  0.6479   \n",
              "lda          Linear Discriminant Analysis    0.7987  0.8406  0.5377  0.6378   \n",
              "rf               Random Forest Classifier    0.7911  0.8170  0.4739  0.6363   \n",
              "svm                   SVM - Linear Kernel    0.7890  0.0000  0.4404  0.7053   \n",
              "et                 Extra Trees Classifier    0.7717  0.7843  0.4624  0.5811   \n",
              "knn                K Neighbors Classifier    0.7659  0.7740  0.5017  0.5592   \n",
              "nb                            Naive Bayes    0.7464  0.8298  0.7634  0.5112   \n",
              "dummy                    Dummy Classifier    0.7382  0.5000  0.0000  0.0000   \n",
              "dt               Decision Tree Classifier    0.7290  0.6539  0.4922  0.4828   \n",
              "qda       Quadratic Discriminant Analysis    0.5807  0.6104  0.6726  0.3791   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "lr        0.5830  0.4576  0.4631     0.099  \n",
              "ridge     0.5647  0.4405  0.4492     0.048  \n",
              "gbc       0.5733  0.4464  0.4525     0.917  \n",
              "ada       0.5766  0.4488  0.4541     0.319  \n",
              "lightgbm  0.5719  0.4436  0.4490     0.124  \n",
              "lda       0.5829  0.4516  0.4548     0.114  \n",
              "rf        0.5428  0.4112  0.4190     1.210  \n",
              "svm       0.4848  0.3752  0.4013     0.070  \n",
              "et        0.5145  0.3679  0.3723     1.371  \n",
              "knn       0.5285  0.3735  0.3747     0.434  \n",
              "nb        0.6120  0.4346  0.4541     0.015  \n",
              "dummy     0.0000  0.0000  0.0000     0.009  \n",
              "dt        0.4872  0.3031  0.3033     0.040  \n",
              "qda       0.4452  0.1758  0.2120     0.030  "
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
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              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.8044</td>\n",
              "      <td>0.8445</td>\n",
              "      <td>0.5228</td>\n",
              "      <td>0.6599</td>\n",
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              "      <th>ridge</th>\n",
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              "      <td>0.048</td>\n",
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              "      <th>gbc</th>\n",
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              "      <td>0.5733</td>\n",
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              "      <th>ada</th>\n",
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              "      <td>0.8010</td>\n",
              "      <td>0.8455</td>\n",
              "      <td>0.5180</td>\n",
              "      <td>0.6511</td>\n",
              "      <td>0.5766</td>\n",
              "      <td>0.4488</td>\n",
              "      <td>0.4541</td>\n",
              "      <td>0.319</td>\n",
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              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.7996</td>\n",
              "      <td>0.8369</td>\n",
              "      <td>0.5126</td>\n",
              "      <td>0.6479</td>\n",
              "      <td>0.5719</td>\n",
              "      <td>0.4436</td>\n",
              "      <td>0.4490</td>\n",
              "      <td>0.124</td>\n",
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              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.7987</td>\n",
              "      <td>0.8406</td>\n",
              "      <td>0.5377</td>\n",
              "      <td>0.6378</td>\n",
              "      <td>0.5829</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>0.4548</td>\n",
              "      <td>0.114</td>\n",
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              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.7911</td>\n",
              "      <td>0.8170</td>\n",
              "      <td>0.4739</td>\n",
              "      <td>0.6363</td>\n",
              "      <td>0.5428</td>\n",
              "      <td>0.4112</td>\n",
              "      <td>0.4190</td>\n",
              "      <td>1.210</td>\n",
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              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.7890</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4404</td>\n",
              "      <td>0.7053</td>\n",
              "      <td>0.4848</td>\n",
              "      <td>0.3752</td>\n",
              "      <td>0.4013</td>\n",
              "      <td>0.070</td>\n",
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              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.7717</td>\n",
              "      <td>0.7843</td>\n",
              "      <td>0.4624</td>\n",
              "      <td>0.5811</td>\n",
              "      <td>0.5145</td>\n",
              "      <td>0.3679</td>\n",
              "      <td>0.3723</td>\n",
              "      <td>1.371</td>\n",
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              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.7659</td>\n",
              "      <td>0.7740</td>\n",
              "      <td>0.5017</td>\n",
              "      <td>0.5592</td>\n",
              "      <td>0.5285</td>\n",
              "      <td>0.3735</td>\n",
              "      <td>0.3747</td>\n",
              "      <td>0.434</td>\n",
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              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.7464</td>\n",
              "      <td>0.8298</td>\n",
              "      <td>0.7634</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6120</td>\n",
              "      <td>0.4346</td>\n",
              "      <td>0.4541</td>\n",
              "      <td>0.015</td>\n",
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              "      <th>dummy</th>\n",
              "      <td>Dummy Classifier</td>\n",
              "      <td>0.7382</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.009</td>\n",
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              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.7290</td>\n",
              "      <td>0.6539</td>\n",
              "      <td>0.4922</td>\n",
              "      <td>0.4828</td>\n",
              "      <td>0.4872</td>\n",
              "      <td>0.3031</td>\n",
              "      <td>0.3033</td>\n",
              "      <td>0.040</td>\n",
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              "      <th>qda</th>\n",
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              "      <td>0.6104</td>\n",
              "      <td>0.6726</td>\n",
              "      <td>0.3791</td>\n",
              "      <td>0.4452</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>0.2120</td>\n",
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          "text": [
            "INFO:logs:create_model_container: 42\n",
            "INFO:logs:master_model_container: 42\n",
            "INFO:logs:display_container: 4\n",
            "INFO:logs:[LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=123, solver='lbfgs', tol=0.0001, verbose=0,\n",
            "                   warm_start=False), RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
            "                max_iter=None, normalize=False, random_state=123, solver='auto',\n",
            "                tol=0.001), GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
            "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
            "                           max_features=None, max_leaf_nodes=None,\n",
            "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                           min_samples_leaf=1, min_samples_split=2,\n",
            "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                           n_iter_no_change=None, presort='deprecated',\n",
            "                           random_state=123, subsample=1.0, tol=0.0001,\n",
            "                           validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)]\n",
            "INFO:logs:compare_models() succesfully completed......................................\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "lr = create_model('lr')"
      ],
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          "data": {
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "Fold                                                          \n",
              "0       0.7730  0.8184  0.4595  0.5862  0.5152  0.3698  0.3745\n",
              "1       0.7979  0.8356  0.4932  0.6518  0.5615  0.4335  0.4406\n",
              "2       0.8209  0.8555  0.5676  0.6942  0.6245  0.5085  0.5130\n",
              "3       0.8032  0.8530  0.5068  0.6637  0.5747  0.4497  0.4566\n",
              "4       0.8099  0.8533  0.5068  0.6881  0.5837  0.4642  0.4733\n",
              "5       0.7904  0.8265  0.5374  0.6124  0.5725  0.4344  0.4360\n",
              "6       0.8171  0.8585  0.5714  0.6774  0.6199  0.5006  0.5037\n",
              "7       0.8046  0.8516  0.4966  0.6697  0.5703  0.4475  0.4558\n",
              "8       0.8117  0.8371  0.5374  0.6752  0.5985  0.4776  0.4829\n",
              "9       0.8153  0.8556  0.5510  0.6807  0.6090  0.4898  0.4945\n",
              "Mean    0.8044  0.8445  0.5228  0.6599  0.5830  0.4576  0.4631\n",
              "Std     0.0136  0.0133  0.0342  0.0329  0.0307  0.0386  0.0385"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a3023129-a9a7-4501-b5fe-9e639e98f609\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.7730</td>\n",
              "      <td>0.8184</td>\n",
              "      <td>0.4595</td>\n",
              "      <td>0.5862</td>\n",
              "      <td>0.5152</td>\n",
              "      <td>0.3698</td>\n",
              "      <td>0.3745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.7979</td>\n",
              "      <td>0.8356</td>\n",
              "      <td>0.4932</td>\n",
              "      <td>0.6518</td>\n",
              "      <td>0.5615</td>\n",
              "      <td>0.4335</td>\n",
              "      <td>0.4406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8209</td>\n",
              "      <td>0.8555</td>\n",
              "      <td>0.5676</td>\n",
              "      <td>0.6942</td>\n",
              "      <td>0.6245</td>\n",
              "      <td>0.5085</td>\n",
              "      <td>0.5130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8032</td>\n",
              "      <td>0.8530</td>\n",
              "      <td>0.5068</td>\n",
              "      <td>0.6637</td>\n",
              "      <td>0.5747</td>\n",
              "      <td>0.4497</td>\n",
              "      <td>0.4566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8099</td>\n",
              "      <td>0.8533</td>\n",
              "      <td>0.5068</td>\n",
              "      <td>0.6881</td>\n",
              "      <td>0.5837</td>\n",
              "      <td>0.4642</td>\n",
              "      <td>0.4733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.7904</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>0.5374</td>\n",
              "      <td>0.6124</td>\n",
              "      <td>0.5725</td>\n",
              "      <td>0.4344</td>\n",
              "      <td>0.4360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8171</td>\n",
              "      <td>0.8585</td>\n",
              "      <td>0.5714</td>\n",
              "      <td>0.6774</td>\n",
              "      <td>0.6199</td>\n",
              "      <td>0.5006</td>\n",
              "      <td>0.5037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8046</td>\n",
              "      <td>0.8516</td>\n",
              "      <td>0.4966</td>\n",
              "      <td>0.6697</td>\n",
              "      <td>0.5703</td>\n",
              "      <td>0.4475</td>\n",
              "      <td>0.4558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.8117</td>\n",
              "      <td>0.8371</td>\n",
              "      <td>0.5374</td>\n",
              "      <td>0.6752</td>\n",
              "      <td>0.5985</td>\n",
              "      <td>0.4776</td>\n",
              "      <td>0.4829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8153</td>\n",
              "      <td>0.8556</td>\n",
              "      <td>0.5510</td>\n",
              "      <td>0.6807</td>\n",
              "      <td>0.6090</td>\n",
              "      <td>0.4898</td>\n",
              "      <td>0.4945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8044</td>\n",
              "      <td>0.8445</td>\n",
              "      <td>0.5228</td>\n",
              "      <td>0.6599</td>\n",
              "      <td>0.5830</td>\n",
              "      <td>0.4576</td>\n",
              "      <td>0.4631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Std</th>\n",
              "      <td>0.0136</td>\n",
              "      <td>0.0133</td>\n",
              "      <td>0.0342</td>\n",
              "      <td>0.0329</td>\n",
              "      <td>0.0307</td>\n",
              "      <td>0.0386</td>\n",
              "      <td>0.0385</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:create_model_container: 43\n",
            "INFO:logs:master_model_container: 43\n",
            "INFO:logs:display_container: 5\n",
            "INFO:logs:LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=123, solver='lbfgs', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)\n",
            "INFO:logs:create_model() succesfully completed......................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_lr = tune_model(lr, optimize = 'Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622,
          "referenced_widgets": [
            "ba3b783cf19f495a843991642db0287d",
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        "id": "2FUVJmcKhH_S",
        "outputId": "3d186b9b-f2c7-434b-9450-de70879c05e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "Fold                                                          \n",
              "0       0.7730  0.8185  0.4595  0.5862  0.5152  0.3698  0.3745\n",
              "1       0.7979  0.8354  0.4932  0.6518  0.5615  0.4335  0.4406\n",
              "2       0.8209  0.8554  0.5676  0.6942  0.6245  0.5085  0.5130\n",
              "3       0.8014  0.8525  0.5068  0.6579  0.5725  0.4460  0.4525\n",
              "4       0.8099  0.8533  0.5068  0.6881  0.5837  0.4642  0.4733\n",
              "5       0.7904  0.8265  0.5374  0.6124  0.5725  0.4344  0.4360\n",
              "6       0.8188  0.8585  0.5714  0.6829  0.6222  0.5043  0.5078\n",
              "7       0.8028  0.8514  0.4898  0.6667  0.5647  0.4411  0.4499\n",
              "8       0.8099  0.8372  0.5306  0.6724  0.5932  0.4714  0.4770\n",
              "9       0.8171  0.8554  0.5578  0.6833  0.6142  0.4959  0.5003\n",
              "Mean    0.8042  0.8444  0.5221  0.6596  0.5824  0.4569  0.4625\n",
              "Std     0.0139  0.0133  0.0351  0.0332  0.0316  0.0396  0.0394"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d365959-bde1-44d5-a80d-ea9502429d3e\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fold</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.7730</td>\n",
              "      <td>0.8185</td>\n",
              "      <td>0.4595</td>\n",
              "      <td>0.5862</td>\n",
              "      <td>0.5152</td>\n",
              "      <td>0.3698</td>\n",
              "      <td>0.3745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.7979</td>\n",
              "      <td>0.8354</td>\n",
              "      <td>0.4932</td>\n",
              "      <td>0.6518</td>\n",
              "      <td>0.5615</td>\n",
              "      <td>0.4335</td>\n",
              "      <td>0.4406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8209</td>\n",
              "      <td>0.8554</td>\n",
              "      <td>0.5676</td>\n",
              "      <td>0.6942</td>\n",
              "      <td>0.6245</td>\n",
              "      <td>0.5085</td>\n",
              "      <td>0.5130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8014</td>\n",
              "      <td>0.8525</td>\n",
              "      <td>0.5068</td>\n",
              "      <td>0.6579</td>\n",
              "      <td>0.5725</td>\n",
              "      <td>0.4460</td>\n",
              "      <td>0.4525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.8099</td>\n",
              "      <td>0.8533</td>\n",
              "      <td>0.5068</td>\n",
              "      <td>0.6881</td>\n",
              "      <td>0.5837</td>\n",
              "      <td>0.4642</td>\n",
              "      <td>0.4733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.7904</td>\n",
              "      <td>0.8265</td>\n",
              "      <td>0.5374</td>\n",
              "      <td>0.6124</td>\n",
              "      <td>0.5725</td>\n",
              "      <td>0.4344</td>\n",
              "      <td>0.4360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.8188</td>\n",
              "      <td>0.8585</td>\n",
              "      <td>0.5714</td>\n",
              "      <td>0.6829</td>\n",
              "      <td>0.6222</td>\n",
              "      <td>0.5043</td>\n",
              "      <td>0.5078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.8028</td>\n",
              "      <td>0.8514</td>\n",
              "      <td>0.4898</td>\n",
              "      <td>0.6667</td>\n",
              "      <td>0.5647</td>\n",
              "      <td>0.4411</td>\n",
              "      <td>0.4499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.8099</td>\n",
              "      <td>0.8372</td>\n",
              "      <td>0.5306</td>\n",
              "      <td>0.6724</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.4714</td>\n",
              "      <td>0.4770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8171</td>\n",
              "      <td>0.8554</td>\n",
              "      <td>0.5578</td>\n",
              "      <td>0.6833</td>\n",
              "      <td>0.6142</td>\n",
              "      <td>0.4959</td>\n",
              "      <td>0.5003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.8042</td>\n",
              "      <td>0.8444</td>\n",
              "      <td>0.5221</td>\n",
              "      <td>0.6596</td>\n",
              "      <td>0.5824</td>\n",
              "      <td>0.4569</td>\n",
              "      <td>0.4625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Std</th>\n",
              "      <td>0.0139</td>\n",
              "      <td>0.0133</td>\n",
              "      <td>0.0351</td>\n",
              "      <td>0.0332</td>\n",
              "      <td>0.0316</td>\n",
              "      <td>0.0396</td>\n",
              "      <td>0.0394</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d365959-bde1-44d5-a80d-ea9502429d3e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5d365959-bde1-44d5-a80d-ea9502429d3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:create_model_container: 46\n",
            "INFO:logs:master_model_container: 46\n",
            "INFO:logs:display_container: 8\n",
            "INFO:logs:LogisticRegression(C=7.863, class_weight={}, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=123, solver='lbfgs', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)\n",
            "INFO:logs:tune_model() succesfully completed......................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(tuned_lr, 'confusion_matrix')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "Ymg8vD6tiJ4h",
        "outputId": "ab59f629-7ff1-4f6c-d0af-1aa1366b6d1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Visual Rendered Successfully\n",
            "INFO:logs:plot_model() succesfully completed......................................\n"
          ]
        }
      ]
    }
  ]
}