Bank_Customer_Churn_Prediction.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Shaghayegh-Aflatounian/Churn-Prediction/blob/main/Bank_Customer_Churn_Prediction.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": "markdown",
      "source": [
        "For this study, our objectives are:\n",
        "\n",
        "- To identify and visualize the key factors that contribute to customer churn.\n",
        "\n",
        "- To build a predictive model that can: Classify whether a customer will churn or not\n",
        "\n",
        "Ideally, develop a model that can assign a probability of churn for each customer. This would enable customer service to most effectively target their retention efforts at the customers deemed most at-risk of churning."
      ],
      "metadata": {
        "id": "7U4ZGJ_TnDxp"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hAN9ES4NmUvw"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "pd.set_option('display.max_columns', None)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n"
      ],
      "metadata": {
        "id": "X1UOflLWsKGj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Exploration\n"
      ],
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      }
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    {
      "cell_type": "code",
      "source": [
        "# Importing the dataset\n",
        "\n",
        "import os\n",
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "dataset = pd.read_csv('/content/gdrive/My Drive/Github/Churn_Modelling.csv')\n",
        "dataset.head()\n"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "Ybb8LXBdshu-",
        "outputId": "ba34e02e-2c22-4655-eceb-5952fe67fff4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
              "2          3    15619304      Onio          502    France  Female   42   \n",
              "3          4    15701354      Boni          699    France  Female   39   \n",
              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
              "3         93826.63       0  \n",
              "4         79084.10       0  "
            ],
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check columns and missing values\n",
        "\n",
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xpoan85t8rL",
        "outputId": "d5eed705-81d6-42c9-dcfc-fd508a1e60d1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeFUfiBFuTgS",
        "outputId": "adc55255-192b-4cba-a258-29b0eb60f9d8"
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      "execution_count": 8,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "False"
            ]
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          "metadata": {},
          "execution_count": 8
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# Get unique counts for variables\n",
        "\n",
        "dataset.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUvDP1aKugWX",
        "outputId": "5ad77b6e-f2d0-4c0a-81ee-09e085279e97"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber          10000\n",
              "CustomerId         10000\n",
              "Surname             2932\n",
              "CreditScore          460\n",
              "Geography              3\n",
              "Gender                 2\n",
              "Age                   70\n",
              "Tenure                11\n",
              "Balance             6382\n",
              "NumOfProducts          4\n",
              "HasCrCard              2\n",
              "IsActiveMember         2\n",
              "EstimatedSalary     9999\n",
              "Exited                 2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping the columns that do not influence the target\n",
        "\n",
        "dataset = dataset.drop(columns=['RowNumber', 'CustomerId', 'Surname'], axis=1)"
      ],
      "metadata": {
        "id": "LmAINy0nukXR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Review the dataset\n",
        "\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MRKceI0qunxo",
        "outputId": "745bd04a-0be5-401d-c6f2-97988de18878"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0          619    France  Female   42       2       0.00              1   \n",
              "1          608     Spain  Female   41       1   83807.86              1   \n",
              "2          502    France  Female   42       8  159660.80              3   \n",
              "3          699    France  Female   39       1       0.00              2   \n",
              "4          850     Spain  Female   43       2  125510.82              1   \n",
              "\n",
              "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "0          1               1        101348.88       1  \n",
              "1          0               1        112542.58       0  \n",
              "2          1               0        113931.57       1  \n",
              "3          0               0         93826.63       0  \n",
              "4          1               1         79084.10       0  "
            ],
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              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
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              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
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              "      <td>93826.63</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
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              "      <td>125510.82</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-05897130-8b31-4770-9633-3669f43f5da7 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-05897130-8b31-4770-9633-3669f43f5da7');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-394eefd8-b259-44f2-a031-9e74845dcae6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the percentage of exited and stayed customers to the total amount\n",
        "\n",
        "labels = 'Exited', 'Stayed'\n",
        "sizes = [dataset.Exited[dataset['Exited']==1].count(), dataset.Exited[dataset['Exited']==0].count()]\n",
        "fig1, ax1 = plt.subplots(figsize=(10, 6))\n",
        "ax1.pie(sizes, labels=labels, autopct='%1.2f%%',\n",
        "        shadow=False, startangle=0)\n",
        "ax1.axis('equal')\n",
        "plt.title(\"Percentage of Exited and Stayed customers\", size = 16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "DztGOudFurrS",
        "outputId": "915a5eea-c37f-438c-c5cd-e27a6398dd99"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # Checking the relation of target variable with categorical variables\n",
        "\n",
        "fig, axarr = plt.subplots(2, 2, figsize=(16, 8))\n",
        "\n",
        "sns.countplot(x='Geography', hue = 'Exited',data = dataset, ax=axarr[0][0])\n",
        "sns.countplot(x='Gender', hue = 'Exited',data = dataset, ax=axarr[0][1])\n",
        "sns.countplot(x='HasCrCard', hue = 'Exited',data = dataset, ax=axarr[1][0])\n",
        "sns.countplot(x='IsActiveMember', hue = 'Exited',data = dataset, ax=axarr[1][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "o7k-PwgbuvyZ",
        "outputId": "4d969a14-35dd-4ead-e35f-4a89471515d7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='IsActiveMember', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Here we can notice that:\n",
        "#### - German clients were more ready to churn than clients in France and Spain\n",
        "#### - female clients were more ready to churn than male clients\n",
        "#### - there is not significant influence of having or not having a credit card on a churn rate\n",
        "#### - active members were less ready to churn"
      ],
      "metadata": {
        "id": "EEAJs13hu-At"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Relations of target variable with the numerical data\n",
        "\n",
        "fig, axarr = plt.subplots(3, 2, figsize=(16, 12))\n",
        "sns.boxplot(y='CreditScore', x='Exited', hue='Exited', data=dataset, ax=axarr[0][0])\n",
        "sns.boxplot(y='Age', x='Exited', hue='Exited', data=dataset, ax=axarr[0][1])\n",
        "sns.boxplot(y='Tenure', x='Exited', hue='Exited', data=dataset, ax=axarr[1][0])\n",
        "sns.boxplot(y='Balance', x='Exited', hue='Exited', data=dataset, ax=axarr[1][1])\n",
        "sns.boxplot(y='NumOfProducts', x='Exited', hue='Exited', data=dataset, ax=axarr[2][0])\n",
        "sns.boxplot(y='EstimatedSalary', x='Exited', hue='Exited', data=dataset, ax=axarr[2][1])\n",
        "\n",
        "plt.subplots_adjust(hspace=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983
        },
        "id": "yoGZcWWqvN32",
        "outputId": "fee4bf22-4dc5-4d6d-8f56-adb58924c259"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Here we can notice that:\n",
        "##### - there was not big significant influence of Credit Score, Tenure, Number of Products, Estimated Salary on a churn rate\n",
        "##### - older clients are more ready to churn"
      ],
      "metadata": {
        "id": "-I0tLkMMwjO5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Encoding\n",
        "\n",
        "dataset = pd.get_dummies(data=dataset, drop_first=True)\n"
      ],
      "metadata": {
        "id": "uLVzk2qLu8C1"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "NUUQ2o1Zwtxy",
        "outputId": "e4ba7a78-0efd-4274-b401-7e8026a2e298"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619   42       2       0.00              1          1   \n",
              "1          608   41       1   83807.86              1          0   \n",
              "2          502   42       8  159660.80              3          1   \n",
              "3          699   39       1       0.00              2          0   \n",
              "4          850   43       2  125510.82              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  Geography_Germany  \\\n",
              "0               1        101348.88       1                  0   \n",
              "1               1        112542.58       0                  0   \n",
              "2               0        113931.57       1                  0   \n",
              "3               0         93826.63       0                  0   \n",
              "4               1         79084.10       0                  0   \n",
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              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset\n",
        "\n",
        "x = dataset.drop(columns='Exited')\n",
        "y = dataset['Exited']"
      ],
      "metadata": {
        "id": "BtuF2s5bw3ej"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.corrwith(dataset['Exited']).plot.bar(figsize=(16, 8), title = \"Correlated\", rot=45, grid=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "GxN9BRtGw8IR",
        "outputId": "436ab15c-243e-4943-bb92-10064ac2f032"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Correlated'}>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Here we can see the most influential correlation of the churn rate with:\n",
        "####  - Age\n",
        "##### - Active membership\n",
        "#####  - Geography"
      ],
      "metadata": {
        "id": "7OT0nRGPxBx8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Heatmap\n",
        "\n",
        "corr = dataset.corr()\n",
        "plt.figure(figsize=(16,8))\n",
        "sns.heatmap(corr, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "DKwgMSOJxAh_",
        "outputId": "7592d08f-ff42-4cf7-877a-e403cc056473"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset into the test and train set\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=0)\n",
        "\n",
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdZAUdrYxlAQ",
        "outputId": "13f9a0d1-2627-4582-cd44-96182b0304eb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8000, 11), (2000, 11), (8000,), (2000,))"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Scaling the Data\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "metadata": {
        "id": "xSFTLzYIxoFd"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_N_uqnVrxq5y",
        "outputId": "be332438-1542-4b4a-8bce-784f4184cda1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.16958176, -0.46460796,  0.00666099, ..., -0.5698444 ,\n",
              "         1.74309049, -1.09168714],\n",
              "       [-2.30455945,  0.30102557, -1.37744033, ...,  1.75486502,\n",
              "        -0.57369368,  0.91601335],\n",
              "       [-1.19119591, -0.94312892, -1.031415  , ..., -0.5698444 ,\n",
              "        -0.57369368, -1.09168714],\n",
              "       ...,\n",
              "       [ 0.9015152 , -0.36890377,  0.00666099, ..., -0.5698444 ,\n",
              "        -0.57369368,  0.91601335],\n",
              "       [-0.62420521, -0.08179119,  1.39076231, ..., -0.5698444 ,\n",
              "         1.74309049, -1.09168714],\n",
              "       [-0.28401079,  0.87525072, -1.37744033, ...,  1.75486502,\n",
              "        -0.57369368, -1.09168714]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EkRcKYjvxt4e",
        "outputId": "9f44a341-9953-4ecc-c63b-a2ec9a78ac64"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.55204276, -0.36890377,  1.04473698, ...,  1.75486502,\n",
              "        -0.57369368, -1.09168714],\n",
              "       [-1.31490297,  0.10961719, -1.031415  , ..., -0.5698444 ,\n",
              "        -0.57369368, -1.09168714],\n",
              "       [ 0.57162971,  0.30102557,  1.04473698, ..., -0.5698444 ,\n",
              "         1.74309049, -1.09168714],\n",
              "       ...,\n",
              "       [-0.74791227, -0.27319958, -1.37744033, ..., -0.5698444 ,\n",
              "         1.74309049,  0.91601335],\n",
              "       [-0.00566991, -0.46460796, -0.33936434, ...,  1.75486502,\n",
              "        -0.57369368,  0.91601335],\n",
              "       [-0.79945688, -0.84742473,  1.04473698, ...,  1.75486502,\n",
              "        -0.57369368,  0.91601335]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building the model\n",
        "\n"
      ],
      "metadata": {
        "id": "8um_erkRxxkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential()\n"
      ],
      "metadata": {
        "id": "N9gOs0cUxueJ"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tyumnMkx-BH",
        "outputId": "236e1a77-bd3d-405b-aba3-1678e1ac34de"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First input layer\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=6, activation='relu', input_dim=11))"
      ],
      "metadata": {
        "id": "Gd5zY-gLyC5o"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Second hidden layer\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=6, activation='relu'))\n"
      ],
      "metadata": {
        "id": "RurFFGx4yF0R"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Third output layer\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "o5aEb7SjyIdg"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compiling the model\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics='accuracy')"
      ],
      "metadata": {
        "id": "Fh-MJjPbyLxE"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crxticpMyOs2",
        "outputId": "025aab90-18dc-4212-fd3f-cc7982e3769c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 6)                 72        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 42        \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 7         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 121 (484.00 Byte)\n",
            "Trainable params: 121 (484.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the model to the dataset\n",
        "\n",
        "model.fit(x_train, y_train.to_numpy(), batch_size=10, epochs=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5smEJlAyRxb",
        "outputId": "b71d60ce-f06a-4094-92b9-acf253c12a41"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "800/800 [==============================] - 2s 1ms/step - loss: 0.5174 - accuracy: 0.7756\n",
            "Epoch 2/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.4357 - accuracy: 0.8026\n",
            "Epoch 3/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.4098 - accuracy: 0.8219\n",
            "Epoch 4/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3899 - accuracy: 0.8353\n",
            "Epoch 5/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3735 - accuracy: 0.8462\n",
            "Epoch 6/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3629 - accuracy: 0.8526\n",
            "Epoch 7/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3572 - accuracy: 0.8530\n",
            "Epoch 8/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3533 - accuracy: 0.8562\n",
            "Epoch 9/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3500 - accuracy: 0.8584\n",
            "Epoch 10/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3476 - accuracy: 0.8581\n",
            "Epoch 11/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3456 - accuracy: 0.8590\n",
            "Epoch 12/20\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3445 - accuracy: 0.8584\n",
            "Epoch 13/20\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3428 - accuracy: 0.8601\n",
            "Epoch 14/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3420 - accuracy: 0.8616\n",
            "Epoch 15/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3410 - accuracy: 0.8610\n",
            "Epoch 16/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3403 - accuracy: 0.8620\n",
            "Epoch 17/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3398 - accuracy: 0.8625\n",
            "Epoch 18/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3391 - accuracy: 0.8637\n",
            "Epoch 19/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3385 - accuracy: 0.8626\n",
            "Epoch 20/20\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3386 - accuracy: 0.8622\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b9aace59ff0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating the model¶\n"
      ],
      "metadata": {
        "id": "IzOD2t0LyVOh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(x_test, y_test.to_numpy())\n",
        "\n",
        "print('test accuracy:{}'.format(test_accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zRu4x-4ygv1",
        "outputId": "14f7e81f-5cda-42fe-fed5-ee6fd711b425"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 981us/step - loss: 0.3338 - accuracy: 0.8625\n",
            "test accuracy:0.862500011920929\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making a prediction\n",
        "\n",
        "y_pred_prob = model.predict(x_test)\n",
        "y_pred = (y_pred_prob > 0.5).astype(int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0VxFwYQyxjL",
        "outputId": "f5732166-5793-4be8-b379-81d293be235e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 937us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking predictions\n",
        "\n",
        "y_pred[220], y_test[220]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGiBx0g8y1Pg",
        "outputId": "b783d6c8-9b75-476f-b699-e7c27f909380"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0]), 0)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building a confusion matrix\n",
        "\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHPWDD_zy4G7",
        "outputId": "bcd718ad-c8b8-4391-a975-895146ed1ecd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1520   75]\n",
            " [ 200  205]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a heatmap for the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.set(font_scale=1.2)  # Adjust font scale for better readability\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['0 Not Exited', '1 Exited'],\n",
        "            yticklabels=['0 Not Exited', '1 Exited'])\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "toTNnIZly69U",
        "outputId": "03d8e129-e238-4d93-8420-48ca0a660167"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}