Churn_modeling.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOiV0pRAnpF8/gD57Q3PXnV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shubdas9902/AI-ML_projects/blob/main/Churn_modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UnfO21w5frT",
        "outputId": "4d24d587-85e0-4778-9a71-61c4917e00c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
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            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)\n",
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            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Qjdctb06iXX",
        "outputId": "ae7b98df-0670-4349-ff4d-e005799baf06"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.17.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "Awtnxjzk6vS-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('/content/Churn_Modelling.csv')\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "xqeIsp8y69Z0",
        "outputId": "36927a94-4eee-4442-c319-69caf548cc20"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
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              "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",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-af0a2b0c-712d-4cfa-9e53-0b1a4c6e1259 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"RowNumber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2886,\n        \"min\": 1,\n        \"max\": 10000,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          6253,\n          4685,\n          1732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CustomerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71936,\n        \"min\": 15565701,\n        \"max\": 15815690,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          15687492,\n          15736963,\n          15721730\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Surname\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2932,\n        \"samples\": [\n          \"McGuirk\",\n          \"Torkelson\",\n          \"Rapuluchukwu\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CreditScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96,\n        \"min\": 350,\n        \"max\": 850,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          754,\n          533,\n          744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geography\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"France\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 92,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          61,\n          42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62397.405202385955,\n        \"min\": 0.0,\n        \"max\": 250898.09,\n        \"num_unique_values\": 6382,\n        \"samples\": [\n          117707.18,\n          133050.97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumOfProducts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HasCrCard\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsActiveMember\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EstimatedSalary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57510.49281769816,\n        \"min\": 11.58,\n        \"max\": 199992.48,\n        \"num_unique_values\": 9999,\n        \"samples\": [\n          100809.99,\n          95273.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exited\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaLaIRIe7dlH",
        "outputId": "a25cab0d-4fe2-4bd9-ae35-270982afdc8f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=dataset.iloc[:,3:13]\n",
        "y=dataset.iloc[:,13]"
      ],
      "metadata": {
        "id": "q9SQG3U97o3J"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "geography=pd.get_dummies(x['Geography'],drop_first=True)\n",
        "gender=pd.get_dummies(x['Gender'],drop_first=True)\n",
        "\n",
        "print(x['Geography'].dtype)\n",
        "print(x['Gender'].dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9McaY1tM7-At",
        "outputId": "df15330a-1f47-4106-b113-a7d049e383a0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "object\n",
            "object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.drop(['Geography','Gender'],axis=1,inplace=True)\n"
      ],
      "metadata": {
        "id": "SpkB0o978pAn"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=pd.concat([x,geography,gender],axis=1)\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IVLb6ReQ8-O9",
        "outputId": "c6144d59-c521-4e64-ed39-bc8bfd859c4c"
      },
      "execution_count": 23,
      "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  Germany  Spain   Male  \n",
              "0               1        101348.88    False  False  False  \n",
              "1               1        112542.58    False   True  False  \n",
              "2               0        113931.57    False  False  False  \n",
              "3               0         93826.63    False  False  False  \n",
              "4               1         79084.10    False   True  False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3890516b-0e26-49c4-b404-6657cb6370bb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
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              "      <td>79084.10</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3890516b-0e26-49c4-b404-6657cb6370bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3890516b-0e26-49c4-b404-6657cb6370bb 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-3890516b-0e26-49c4-b404-6657cb6370bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2af2e2f9-5e52-4889-859d-873e64be2089\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2af2e2f9-5e52-4889-859d-873e64be2089')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\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",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2af2e2f9-5e52-4889-859d-873e64be2089 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"CreditScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96,\n        \"min\": 350,\n        \"max\": 850,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          754,\n          533,\n          744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 92,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          61,\n          42,\n          59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          2,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62397.405202385955,\n        \"min\": 0.0,\n        \"max\": 250898.09,\n        \"num_unique_values\": 6382,\n        \"samples\": [\n          117707.18,\n          133050.97,\n          124271.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumOfProducts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HasCrCard\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IsActiveMember\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EstimatedSalary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57510.49281769816,\n        \"min\": 11.58,\n        \"max\": 199992.48,\n        \"num_unique_values\": 9999,\n        \"samples\": [\n          100809.99,\n          95273.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Germany\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spain\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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"
      ],
      "metadata": {
        "id": "kGWS4qOo-YtV"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "x_train=sc.fit_transform(x_train)\n",
        "x_test=sc.transform(x_test)"
      ],
      "metadata": {
        "id": "6jHBb6Zd_bDv"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8-ETiNf_18d",
        "outputId": "6c4fefd7-3512-4bc3-cabb-639b5d912acd"
      },
      "execution_count": 26,
      "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": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating the ANN"
      ],
      "metadata": {
        "id": "08lM6xdp__Gj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LeakyReLU,PReLU,ELU,ReLU\n",
        "from tensorflow.keras.layers import Dropout"
      ],
      "metadata": {
        "id": "CuPFvP_xAZIj"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "initialize the ANN"
      ],
      "metadata": {
        "id": "k5S_wai_Gx-b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier=Sequential()"
      ],
      "metadata": {
        "id": "uto7hnAaG08N"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.add(Dense(units=11,activation='relu'))\n",
        "#hidden layers\n",
        "classifier.add(Dense(units=7,activation='relu'))\n",
        "classifier.add(Dense(units=6,activation='relu'))\n",
        "#output layer\n",
        "classifier.add(Dense(units=1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "CdTfPZ_bG_Ff"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "bMrSQ2BLHo5P"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#early stopping\n",
        "early_stopping=tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0.0001,\n",
        "    patience=20,\n",
        "    verbose=1,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=False,\n",
        ")"
      ],
      "metadata": {
        "id": "9BSbPCvdI-PK"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_history=classifier.fit(x_train,y_train,validation_split=0.33,batch_size=10,epochs=1000,callbacks=early_stopping)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbJPgipQIEyM",
        "outputId": "5a3ee737-e06c-4f5d-91a6-ae964579bba6"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8769 - loss: 0.3056 - val_accuracy: 0.8527 - val_loss: 0.3612\n",
            "Epoch 2/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8792 - loss: 0.2932 - val_accuracy: 0.8546 - val_loss: 0.3584\n",
            "Epoch 3/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.8739 - loss: 0.3079 - val_accuracy: 0.8546 - val_loss: 0.3614\n",
            "Epoch 4/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8704 - loss: 0.3172 - val_accuracy: 0.8535 - val_loss: 0.3656\n",
            "Epoch 5/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8681 - loss: 0.3219 - val_accuracy: 0.8538 - val_loss: 0.3641\n",
            "Epoch 6/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8764 - loss: 0.3009 - val_accuracy: 0.8554 - val_loss: 0.3610\n",
            "Epoch 7/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8787 - loss: 0.3019 - val_accuracy: 0.8527 - val_loss: 0.3624\n",
            "Epoch 8/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8703 - loss: 0.3090 - val_accuracy: 0.8523 - val_loss: 0.3640\n",
            "Epoch 9/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8741 - loss: 0.3058 - val_accuracy: 0.8538 - val_loss: 0.3635\n",
            "Epoch 10/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8717 - loss: 0.3118 - val_accuracy: 0.8538 - val_loss: 0.3638\n",
            "Epoch 11/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8771 - loss: 0.3045 - val_accuracy: 0.8557 - val_loss: 0.3675\n",
            "Epoch 12/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8785 - loss: 0.3030 - val_accuracy: 0.8516 - val_loss: 0.3647\n",
            "Epoch 13/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8765 - loss: 0.3072 - val_accuracy: 0.8542 - val_loss: 0.3641\n",
            "Epoch 14/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8792 - loss: 0.2936 - val_accuracy: 0.8554 - val_loss: 0.3645\n",
            "Epoch 15/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8714 - loss: 0.3138 - val_accuracy: 0.8512 - val_loss: 0.3705\n",
            "Epoch 16/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8698 - loss: 0.3189 - val_accuracy: 0.8527 - val_loss: 0.3662\n",
            "Epoch 17/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8750 - loss: 0.3021 - val_accuracy: 0.8519 - val_loss: 0.3694\n",
            "Epoch 18/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8750 - loss: 0.3018 - val_accuracy: 0.8542 - val_loss: 0.3613\n",
            "Epoch 19/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8650 - loss: 0.3141 - val_accuracy: 0.8519 - val_loss: 0.3652\n",
            "Epoch 20/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8698 - loss: 0.3142 - val_accuracy: 0.8523 - val_loss: 0.3661\n",
            "Epoch 21/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.8726 - loss: 0.3179 - val_accuracy: 0.8523 - val_loss: 0.3690\n",
            "Epoch 22/1000\n",
            "\u001b[1m536/536\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8728 - loss: 0.3075 - val_accuracy: 0.8512 - val_loss: 0.3650\n",
            "Epoch 22: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history.history.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdDTWP1AIuWH",
        "outputId": "0ea0514b-cd9d-45a8-ea0d-f4d13802a39d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train','test'],loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MgIK6Zc4J_nS",
        "outputId": "d22115fe-6f59-453f-805c-27c5863487e7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=classifier.predict(x_test)\n",
        "y_pred=(y_pred>=0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bH1AZpK6NH0Q",
        "outputId": "47b51a19-7360-4977-8144-f66d3fcba28b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test,y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2pVWxBbNZsm",
        "outputId": "3d83ba0c-168b-4c29-d5e4-752ddcec81d7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1512,   83],\n",
              "       [ 198,  207]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "score=accuracy_score(y_pred,y_test)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--UE8SMcNgsp",
        "outputId": "7b24733e-2eac-4fee-812a-c04082ebe97d"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8595"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    }
  ]
}