log_reg_model.ipynb

{
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  "metadata": {
    "colab": {
      "name": "log_reg_model.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "f713jgjdYJd-"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/ChurnAnalysis')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oMxzgA0u24p"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9iot8Dnv7tL"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/ChurnAnalysis/dummy_data_csv.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "yw6_PC6OwJ-t",
        "outputId": "44a1b6a2-f4e7-4c9a-cfe5-3d3aeec1c6f4"
      },
      "source": [
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Unnamed: 0</th>\n",
              "      <th>SeniorCitizen</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7030</th>\n",
              "      <td>7041</td>\n",
              "      <td>1</td>\n",
              "      <td>74.40</td>\n",
              "      <td>306.60</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7031</th>\n",
              "      <td>7042</td>\n",
              "      <td>0</td>\n",
              "      <td>105.65</td>\n",
              "      <td>6844.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7032 rows × 52 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0  SeniorCitizen  ...  tenure_group_49 - 60  tenure_group_61 - 72\n",
              "0              0              0  ...                     0                     0\n",
              "1              1              0  ...                     0                     0\n",
              "2              2              0  ...                     0                     0\n",
              "3              3              0  ...                     0                     0\n",
              "4              4              0  ...                     0                     0\n",
              "...          ...            ...  ...                   ...                   ...\n",
              "7027        7038              0  ...                     0                     0\n",
              "7028        7039              0  ...                     0                     1\n",
              "7029        7040              0  ...                     0                     0\n",
              "7030        7041              1  ...                     0                     0\n",
              "7031        7042              0  ...                     0                     1\n",
              "\n",
              "[7032 rows x 52 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQDp2bV7wLFO"
      },
      "source": [
        "df.drop(columns=['Unnamed: 0'],axis = 1, inplace=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiGebB_zwS9K"
      },
      "source": [
        "import sklearn\n",
        "from sklearn.metrics import recall_score, f1_score, confusion_matrix, classification_report, roc_auc_score\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqXWsd7Ey4zZ"
      },
      "source": [
        "X = df.drop(columns=['Churn'])\n",
        "Y = df['Churn']"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vH7GLn8j2wkA"
      },
      "source": [
        "from imblearn.combine import SMOTEENN\n",
        "sm = SMOTEENN()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAQ17Pz22-hT"
      },
      "source": [
        "X_re , Y_re = sm.fit_resample(X,Y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEEWVtlbzO3i"
      },
      "source": [
        "##minmaxscaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVXEEOw2zFYi"
      },
      "source": [
        "minmax = MinMaxScaler()\n",
        "X_mm = minmax.fit_transform(X_re)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnSjofEl1Dgz"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X_mm, Y_re, test_size = 0.33)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hl-NDB542VWX",
        "outputId": "0a73f25b-4205-4fec-e871-2fa478a521a0"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3950, 50)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2I_3zHn2ZAE",
        "outputId": "227cf751-adf7-473b-ee86-d5a724afd4e9"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1946,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWUGVVD12cfY"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3F3JxUbr33OV"
      },
      "source": [
        "log_reg = LogisticRegression(fit_intercept=False, C=1e12, solver='liblinear')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptMEKtcs4Qj8",
        "outputId": "449914a6-fcac-42ca-89b3-459bd7f33cb3"
      },
      "source": [
        "log_reg.fit(x_train, y_train)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1000000000000.0, class_weight=None, dual=False,\n",
              "                   fit_intercept=False, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d82ta8X4my7"
      },
      "source": [
        "pred = log_reg.predict(x_test)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfe_Kqul4uqh",
        "outputId": "b6fac499-7be2-4012-bf74-8266b364a4c9"
      },
      "source": [
        "score = log_reg.score(x_test, y_test)\n",
        "print(score)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9075025693730729\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-TPC4jp5KSD"
      },
      "source": [
        "Achieved 90% accuracy!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3ZglmK65Gw0"
      },
      "source": [
        "class_report = classification_report(y_test, pred)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yenEwrxc5ssa",
        "outputId": "4bb46db5-b732-4590-af71-57e67270bed2"
      },
      "source": [
        "print(class_report)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.87      0.89       867\n",
            "           1       0.90      0.94      0.92      1079\n",
            "\n",
            "    accuracy                           0.91      1946\n",
            "   macro avg       0.91      0.90      0.91      1946\n",
            "weighted avg       0.91      0.91      0.91      1946\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJPdmnln5usx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}