Assignment_trial.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/RKNahid/ML_815/blob/main/Assignment_trial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P8ZR5Gul1rv0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2KPUxs8RHlbP"
      },
      "outputs": [],
      "source": [
        "!pip install imbalanced-learn\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "def balance(x_temp,y_temp):\n",
        "  smote = SMOTE()\n",
        "\n",
        "  x_temp, y_temp = smote.fit_resample(x_temp, y_temp)\n",
        "\n",
        "  return x_temp, y_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H0GjOamL10qz"
      },
      "outputs": [],
      "source": [
        "a1 = pd.read_excel(\"/content/drive/MyDrive/Project/Assignment1/customer_churn_prediction.xlsx\")\n",
        "target = \"Churn\"\n",
        "\n",
        "a2 = a1.copy()\n",
        "a3 = a1.copy()\n",
        "a4 = a1.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AkY6OhM615CH"
      },
      "source": [
        "**Show first five row**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "aV9MPmO113P8",
        "outputId": "c0ecb73e-ecc3-413d-f1b1-cf072c93a430"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>...</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <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>1889.50</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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            "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       1889.50     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]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a1.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RN-NCbE12AjO"
      },
      "source": [
        "**Show shape of dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dExHEXoE19yT",
        "outputId": "abfbd5a1-051c-4460-e4dd-5e7b68136c1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a1.shape\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qsBokLbl2Iw-"
      },
      "source": [
        "**Display whether there is null value or not and if so handle those**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "727p2Ljw2Cxl",
        "outputId": "de5ad5f4-e1e4-42cc-bd2e-dba7eda5ec4d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7032 non-null   float64\n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(2), int64(2), object(17)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ],
      "source": [
        "a1.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OA5OBdf02Pgv"
      },
      "source": [
        "There are null Values in TotalCharges column. Lets handle this column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NI4Of8a2M0z",
        "outputId": "29a246e3-cf52-42e7-e189-5d366286fe77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   float64\n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(2), int64(2), object(17)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ],
      "source": [
        "a1['TotalCharges'] = a1['TotalCharges'].fillna(a1['TotalCharges'].mean())\n",
        "a1.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyANk-7H2fTw"
      },
      "source": [
        "**Count target values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HvDMjN72XLL",
        "outputId": "9df00cc5-bd2c-426c-ffa1-8051f7e98547"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['No', 'Yes']"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sorted(list(set(a1[target])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGRnAi-J3ejV",
        "outputId": "b26b1653-68ed-49d5-d9a5-f5e641affd2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No     5174\n",
            "Yes    1869\n",
            "Name: Churn, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "a1_c = a1['Churn'].value_counts()\n",
        "print(a1_c)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0M2_ZjIP6gf6"
      },
      "source": [
        "**Plot target values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mUTxwgBf4BMi"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3dczh4M-62_k",
        "outputId": "ac602e83-7c51-42bf-ac9a-e531c4307c3f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.bar(a1_c.index, a1_c.values)\n",
        "\n",
        "plt.xlabel('Churn Values')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Ploting Target values')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ygjjRhPO8m8m"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "en = LabelEncoder()\n",
        "\n",
        "a1[target] = en.fit_transform(a1[target])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrirWrqaA0RG",
        "outputId": "b2eaac5b-6f6d-4fc1-f8af-57e6e8a1cadb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0, 1]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sorted(list(set(a1[target])))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6ADWOtzA395",
        "outputId": "1399fa88-aeb6-4d9c-cbe7-6ac1cffcef66"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   float64\n",
            " 20  Churn             7043 non-null   int64  \n",
            "dtypes: float64(2), int64(3), object(16)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ],
      "source": [
        "a1.info()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A8S2sEuwDqza"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "ob_col = a1.select_dtypes(include=['object']).columns\n",
        "\n",
        "en5 = LabelEncoder()\n",
        "\n",
        "a1[ob_col] = a1[ob_col].apply(lambda col: en5.fit_transform(col))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0k1smqEE3Es",
        "outputId": "6a062f4b-a7d6-4216-adf4-64b13886c1e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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   float64\n",
            " 20  Churn             7043 non-null   int64  \n",
            "dtypes: float64(2), int64(19)\n",
            "memory usage: 1.1 MB\n"
          ]
        }
      ],
      "source": [
        "a1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "RbjKTf8s1Jzl",
        "outputId": "44d568c0-55d8-47dc-cf6d-c005e01ffb99"
      },
      "outputs": [
        {
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-105aa4f5-cebf-471c-a514-df60b108b660');\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
              "0        5375       0              0        1           0       1   \n",
              "1        3962       1              0        0           0      34   \n",
              "2        2564       1              0        0           0       2   \n",
              "3        5535       1              0        0           0      45   \n",
              "4        6511       0              0        0           0       2   \n",
              "\n",
              "   PhoneService  MultipleLines  InternetService  OnlineSecurity  ...  \\\n",
              "0             0              1                0               0  ...   \n",
              "1             1              0                0               2  ...   \n",
              "2             1              0                0               2  ...   \n",
              "3             0              1                0               2  ...   \n",
              "4             1              0                1               0  ...   \n",
              "\n",
              "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
              "0                 0            0            0                0         0   \n",
              "1                 2            0            0                0         1   \n",
              "2                 0            0            0                0         0   \n",
              "3                 2            2            0                0         1   \n",
              "4                 0            0            0                0         0   \n",
              "\n",
              "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
              "0                 1              2           29.85         29.85      0  \n",
              "1                 0              3           56.95       1889.50      0  \n",
              "2                 1              3           53.85        108.15      1  \n",
              "3                 0              0           42.30       1840.75      0  \n",
              "4                 1              2           70.70        151.65      1  \n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a1.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDkzfTSyBMox"
      },
      "source": [
        "**Apply hold out cross validation and show the training and test score (Use Logistic Regression Algorithm only)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Trtkf_wYBstD"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_H1_sPkbA58q"
      },
      "outputs": [],
      "source": [
        "x = a1.loc[:,a1.columns != target]\n",
        "y = a1[target]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_1AWz6QCMbb"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vEruDq0F48t",
        "outputId": "f4f03d7c-2bb3-4504-ff48-570109650a70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    5174\n",
              "1    1869\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a1[target].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hE5DM-EFHZM0",
        "outputId": "e873dbe0-d0e7-45b4-da9a-2c5cc1aa6739"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    4138\n",
              "1    1496\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ntMSbGeVHaDc"
      },
      "outputs": [],
      "source": [
        "x_train, y_train = balance(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNvPgkj0HZwy",
        "outputId": "d3125356-906e-4995-c6f2-1ef1de2b6603"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    4138\n",
              "1    4138\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yd1Tof8CHxYa",
        "outputId": "0fa5b39d-01da-4bd0-d191-fb3fc1677f1c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    5174\n",
              "1    1869\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a1[target].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VktBCmH9Cwqa"
      },
      "source": [
        "**Training Phase**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "VqDRZSWWCX-E",
        "outputId": "d0370f25-72fe-4fe6-bc49-9958b1f324d6"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/linear_model/_logistic.py:458: 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",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "data": {
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              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "trin = LogisticRegression()\n",
        "\n",
        "trin.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLLkxMJ1Cyuu",
        "outputId": "b7298cff-31c4-4b45-b1ef-f7eaef4a5fa4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.80521991300145"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trin.score(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2M63p6QdS_lN"
      },
      "outputs": [],
      "source": [
        "y_pred = trin.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGEsud-KTTpg",
        "outputId": "44d2e777-f6d5-4c11-ed8d-e2b604aa963f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[787 249]\n",
            " [ 84 289]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.76      0.83      1036\n",
            "           1       0.54      0.77      0.63       373\n",
            "\n",
            "    accuracy                           0.76      1409\n",
            "   macro avg       0.72      0.77      0.73      1409\n",
            "weighted avg       0.81      0.76      0.77      1409\n",
            "\n",
            "Accuracy:  0.7636621717530163\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(\"Accuracy: \",accuracy_score(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXj_iONFUD3P"
      },
      "source": [
        "**K fold**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RMf_B6yKVERA"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import cross_val_score, KFold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x_SuMgt8NYrb"
      },
      "outputs": [],
      "source": [
        "x = a1.loc[:,a1.columns != target]\n",
        "y = a1[target]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MV0p5znVOku",
        "outputId": "77db796c-4530-49ac-d7a1-f80550405a8a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.80411639 0.80482612 0.78708304 0.8125     0.80397727]\n"
          ]
        }
      ],
      "source": [
        "sco = cross_val_score(LogisticRegression(max_iter=1000), x, y, cv=5)\n",
        "print(sco)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SuTbMz7pV7Vo",
        "outputId": "276f2704-632c-46a8-ce73-1892be2975e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Min Score 78.708303761533 %\n",
            "Max Score 81.25 %\n",
            "Avg Score 80.25005645525518 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Min Score\", np.min(sco)*100, \"%\")\n",
        "print(\"Max Score\", np.max(sco)*100, \"%\")\n",
        "print(\"Avg Score\", np.mean(sco)*100, \"%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OoDZGp3ja7_E"
      },
      "source": [
        "**Apply stratified k fold cross validation (Use Logistic Regression Algorithm only)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-AgypFzwWd6T"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IpeL1DLHbTua"
      },
      "outputs": [],
      "source": [
        "x = a1.loc[:,a1.columns != target]\n",
        "y = a1[target]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2XfyHEPnRtW",
        "outputId": "e3785a70-3250-4146-8850-c6ff7bbb83a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.79543198 0.80417495 0.81411531 0.79324056 0.81212724 0.79920477\n",
            " 0.81312127]\n"
          ]
        }
      ],
      "source": [
        "scok = cross_val_score(LogisticRegression(max_iter=1000), x, y, cv=7)\n",
        "print(scok)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kzo9rMXKoRvC",
        "outputId": "a51a200d-5673-4371-d2f6-ca5d033d2f6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Min Score 79.32405566600397 %\n",
            "Max Score 81.41153081510934 %\n",
            "Avg Score 80.44880102277526 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Min Score\", np.min(scok)*100, \"%\")\n",
        "print(\"Max Score\", np.max(scok)*100, \"%\")\n",
        "print(\"Avg Score\", np.mean(scok)*100, \"%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwCQV0h3og7E"
      },
      "source": [
        "**Apply leave one out cross validation (Use Logistic Regression Algorithm only)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jxrx0ZCloTZk"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import LeaveOneOut, cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B5UtVEClox9d"
      },
      "outputs": [],
      "source": [
        "x = a1.loc[:,a1.columns != target]\n",
        "y = a1[target]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "6iPpo03Vo4pN",
        "outputId": "5f651eaf-4888-43e6-da09-65fc73a89ca0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0. 1. 0. ... 1. 1. 1.]\n"
          ]
        }
      ],
      "source": [
        "lo = LeaveOneOut()\n",
        "scol = cross_val_score(LogisticRegression(max_iter=1000, n_jobs=-1), x, y, cv=lo)\n",
        "print(scol)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "_LD4Qtaso5sL",
        "outputId": "edc0ae36-ba6f-42a5-e386-6b27b9a4c0ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg Score 80.06531307681387 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Avg Score\", np.mean(scol)*100, \"%\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1Q9uiRngamjgQDv-YFecfuO_s_ZtuuIGZ",
      "authorship_tag": "ABX9TyN78PkNJT000LPLTJeA+f7D",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}