Banka_Müşteri_Kaybı_Analizi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "GsZwakuv-Gd6",
        "8mFX3Rks-DaF",
        "MsfjYb4U8uyl",
        "5Sy0GBh88oR9",
        "gela2eZh6-xa"
      ],
      "authorship_tag": "ABX9TyNp3wv1fQvQ0JeRqyMLrLMS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oguzhansarigol/Machine-Learning-Oguzhan-Sarigol/blob/main/Banka_M%C3%BC%C5%9Fteri_Kayb%C4%B1_Analizi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gerekli Kütüphaneler"
      ],
      "metadata": {
        "id": "GsZwakuv-Gd6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "5ujYmnhj0EYz"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Database"
      ],
      "metadata": {
        "id": "8mFX3Rks-DaF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bu veri seti, bir bankanın müşterilerine ait çeşitli özellikleri ve bu müşterilerin bankayı terk edip etmediğini (\"Exited\" sütunu) içermektedir.\n",
        "Veri setindeki temel amaç, müşterilerin bankadan ayrılmasını etkileyen faktörleri incelemek ve bu durumu tahmin edebilmektir.\n",
        "\n",
        "Veri setinde bulunan bazı sütunlar şunlardır:\n",
        "- CreditScore: Müşterinin kredi puanı\n",
        "- Geography: Müşterinin yaşadığı ülke (France, Spain, Germany)\n",
        "- Gender: Müşterinin cinsiyeti (Male, Female)\n",
        "- Age: Müşterinin yaşı\n",
        "- Balance: Müşterinin bankadaki hesap bakiyesi\n",
        "- NumOfProducts: Müşterinin sahip olduğu ürün sayısı\n",
        "- HasCrCard: Müşterinin kredi kartı sahip olup olmadığı (1: Evet, 0: Hayır)\n",
        "- IsActiveMember: Müşterinin aktif bir banka üyesi olup olmadığı (1: Evet, 0: Hayır)\n",
        "- EstimatedSalary: Müşterinin tahmini maaşı\n",
        "- Exited: Müşterinin bankayı terk edip etmediği (1: Evet, 0: Hayır) [Hedef değişken]\n",
        "\n",
        "Bu görselleştirmeler, müşterilerin demografik ve finansal özelliklerinin dağılımını ve bu özelliklerin müşteri kaybıyla olan ilişkisini anlamaya yöneliktir.\n"
      ],
      "metadata": {
        "id": "4Mz22GQd-E4f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Database: https://www.kaggle.com/datasets/saurabhbadole/bank-customer-churn-prediction-dataset"
      ],
      "metadata": {
        "id": "MpJIKoTjjtYE"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"churn.csv\")"
      ],
      "metadata": {
        "id": "ABgbJHjckOgA"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ],
      "metadata": {
        "id": "MsfjYb4U8uyl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Kategorik değişkenlerin dağılımı (Cinsiyet ve Coğrafya)\n",
        "plt.figure(figsize=(14, 6))\n",
        "\n",
        "# Cinsiyet dağılımı\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.countplot(x='Gender', data=df)\n",
        "plt.title('Gender Distribution')\n",
        "\n",
        "# Coğrafi bölge dağılımı\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.countplot(x='Geography', data=df)\n",
        "plt.title('Geography Distribution')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# 2. Sayısal değişkenlerin dağılımı (Kredi Puanı, Yaş, Maaş)\n",
        "plt.figure(figsize=(18, 6))\n",
        "\n",
        "# Kredi puanı dağılımı\n",
        "plt.subplot(1, 3, 1)\n",
        "sns.histplot(df['CreditScore'], kde=True, bins=30)\n",
        "plt.title('Credit Score Distribution')\n",
        "\n",
        "# Yaş dağılımı\n",
        "plt.subplot(1, 3, 2)\n",
        "sns.histplot(df['Age'], kde=True, bins=30)\n",
        "plt.title('Age Distribution')\n",
        "\n",
        "# Maaş dağılımı\n",
        "plt.subplot(1, 3, 3)\n",
        "sns.histplot(df['EstimatedSalary'], kde=True, bins=30)\n",
        "plt.title('Estimated Salary Distribution')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# 3. Korelasyon matrisi\n",
        "plt.figure(figsize=(10, 8))\n",
        "corr_matrix = df.select_dtypes(include=np.number).corr()\n",
        "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', square=True)\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()\n",
        "\n",
        "# 4. Müşteri kaybı analizi - Cinsiyete göre Exited dağılımı\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='Gender', hue='Exited', data=df)\n",
        "plt.title('Exited Distribution by Gender')\n",
        "plt.show()\n",
        "\n",
        "# 5. Müşteri kaybı analizi - Yaşın Exited üzerindeki etkisi\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.histplot(data=df, x='Age', hue='Exited', kde=True, bins=30)\n",
        "plt.title('Age Distribution by Exited Status')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Sv_E6kuB81OL",
        "outputId": "1b0d4e15-5824-42db-d8be-4d7ab0e15b05"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Machine Learning Process**"
      ],
      "metadata": {
        "id": "5Sy0GBh88oR9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "id": "lyKgoSGFkU1j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "04728425-4258-40a0-a131-478d036376c7"
      },
      "execution_count": 100,
      "outputs": [
        {
          "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",
              "\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",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5301482d-a839-45c1-ac62-9ab8a30008d4\" 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>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>15634602</td>\n",
              "      <td>Hargrave</td>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>15647311</td>\n",
              "      <td>Hill</td>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>15619304</td>\n",
              "      <td>Onio</td>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </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-5301482d-a839-45c1-ac62-9ab8a30008d4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-5301482d-a839-45c1-ac62-9ab8a30008d4 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-5301482d-a839-45c1-ac62-9ab8a30008d4');\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-b22ce1ba-e7d8-4afd-8f40-6cf226a9a9c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b22ce1ba-e7d8-4afd-8f40-6cf226a9a9c2')\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-b22ce1ba-e7d8-4afd-8f40-6cf226a9a9c2 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": "df",
              "summary": "{\n  \"name\": \"df\",\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": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop([\"RowNumber\",\"CustomerId\",\"Surname\"],axis=1)"
      ],
      "metadata": {
        "id": "IndrZVuRV7I_"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zvyVHvccWHZI",
        "outputId": "0dca2948-1868-4ac1-92b1-fca5ebe6b640"
      },
      "execution_count": 102,
      "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",
              "\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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-48578935-2534-4a95-9e32-a24429ff4e4a\" 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>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </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-48578935-2534-4a95-9e32-a24429ff4e4a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-48578935-2534-4a95-9e32-a24429ff4e4a 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-48578935-2534-4a95-9e32-a24429ff4e4a');\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-942d526e-9d01-416c-9086-2574db5e5f35\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-942d526e-9d01-416c-9086-2574db5e5f35')\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-942d526e-9d01-416c-9086-2574db5e5f35 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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"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": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjQEcz22WIrw",
        "outputId": "16b53d95-b32a-4ac7-d5b1-6b76328495f8"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   CreditScore      10000 non-null  int64  \n",
            " 1   Geography        10000 non-null  object \n",
            " 2   Gender           10000 non-null  object \n",
            " 3   Age              10000 non-null  int64  \n",
            " 4   Tenure           10000 non-null  int64  \n",
            " 5   Balance          10000 non-null  float64\n",
            " 6   NumOfProducts    10000 non-null  int64  \n",
            " 7   HasCrCard        10000 non-null  int64  \n",
            " 8   IsActiveMember   10000 non-null  int64  \n",
            " 9   EstimatedSalary  10000 non-null  float64\n",
            " 10  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(7), object(2)\n",
            "memory usage: 859.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.get_dummies(df,columns=[\"Geography\",\"Gender\"],drop_first=True)"
      ],
      "metadata": {
        "id": "yTIp2i0BWWJD"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df[\"Exited\"]\n",
        "x=df.drop([\"Exited\"],axis=1)"
      ],
      "metadata": {
        "id": "AR46Uc3jWjSX"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.7,random_state=16)"
      ],
      "metadata": {
        "id": "5W7h2sB3Wo6O"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf=RandomForestClassifier()\n",
        "model=rf.fit(x_train,y_train)\n",
        "model.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oc-BrY9AXCTX",
        "outputId": "d628f959-5e5f-4166-9cca-6bf317d4ecbc"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8583333333333333"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[712,22,3,0,5,1,1,200000,False,False,True]])"
      ],
      "metadata": {
        "id": "GHH83-HqXI11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fdb1644-0d89-4031-e78e-8c2ceefa0fda"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Hot Encoder"
      ],
      "metadata": {
        "id": "gela2eZh6-xa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "metadata": {
        "id": "vqGgpiR76-jb"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('churn.csv')\n",
        "df=df.drop(columns=[\"RowNumber\",\"CustomerId\",\"Surname\"])"
      ],
      "metadata": {
        "id": "4ojMh3us7F4g"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ScfJm4QU5BlW",
        "outputId": "0e5ab1a5-7fec-44ab-a229-c5d481347fea"
      },
      "execution_count": 111,
      "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",
              "\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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6d6321f-d6e3-4c9d-927e-b0eff568aae9\" 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>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </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-b6d6321f-d6e3-4c9d-927e-b0eff568aae9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-b6d6321f-d6e3-4c9d-927e-b0eff568aae9 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-b6d6321f-d6e3-4c9d-927e-b0eff568aae9');\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-e2a7918d-0e48-452b-bb6b-34a99fe2fb23\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e2a7918d-0e48-452b-bb6b-34a99fe2fb23')\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-e2a7918d-0e48-452b-bb6b-34a99fe2fb23 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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"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": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ohe=OneHotEncoder()"
      ],
      "metadata": {
        "id": "QP5K3Eq862De"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xd=ohe.fit_transform(df[['Geography',\"Gender\"]]).toarray()"
      ],
      "metadata": {
        "id": "OSYw2cK27Kbs"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ohe.get_feature_names_out()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FRym7lkn7MI9",
        "outputId": "7880d797-3144-4865-9f73-53986bebf350"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Geography_France', 'Geography_Germany', 'Geography_Spain',\n",
              "       'Gender_Female', 'Gender_Male'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xd=pd.DataFrame(xd)"
      ],
      "metadata": {
        "id": "6hdjKgca7NKP"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xd.columns=ohe.get_feature_names_out()"
      ],
      "metadata": {
        "id": "eM9iAcxr7OER"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VnrqF8nh7Qoa",
        "outputId": "d7fa5220-edf1-462e-9257-0dac019e2c72"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Geography_France  Geography_Germany  Geography_Spain  Gender_Female  \\\n",
              "0                  1.0                0.0              0.0            1.0   \n",
              "1                  0.0                0.0              1.0            1.0   \n",
              "2                  1.0                0.0              0.0            1.0   \n",
              "3                  1.0                0.0              0.0            1.0   \n",
              "4                  0.0                0.0              1.0            1.0   \n",
              "...                ...                ...              ...            ...   \n",
              "9995               1.0                0.0              0.0            0.0   \n",
              "9996               1.0                0.0              0.0            0.0   \n",
              "9997               1.0                0.0              0.0            1.0   \n",
              "9998               0.0                1.0              0.0            0.0   \n",
              "9999               1.0                0.0              0.0            1.0   \n",
              "\n",
              "      Gender_Male  \n",
              "0             0.0  \n",
              "1             0.0  \n",
              "2             0.0  \n",
              "3             0.0  \n",
              "4             0.0  \n",
              "...           ...  \n",
              "9995          1.0  \n",
              "9996          1.0  \n",
              "9997          0.0  \n",
              "9998          1.0  \n",
              "9999          0.0  \n",
              "\n",
              "[10000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d19d03d4-cd0d-4ea0-ae50-cf28bfbb371e\" 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>Geography_France</th>\n",
              "      <th>Geography_Germany</th>\n",
              "      <th>Geography_Spain</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d19d03d4-cd0d-4ea0-ae50-cf28bfbb371e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-d19d03d4-cd0d-4ea0-ae50-cf28bfbb371e 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-d19d03d4-cd0d-4ea0-ae50-cf28bfbb371e');\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-840bdd9a-2f53-4b8a-a6dd-34280be89ca6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-840bdd9a-2f53-4b8a-a6dd-34280be89ca6')\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-840bdd9a-2f53-4b8a-a6dd-34280be89ca6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b6e1bbc8-5402-412b-8401-e0596b3a4930\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('xd')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b6e1bbc8-5402-412b-8401-e0596b3a4930 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('xd');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "xd",
              "summary": "{\n  \"name\": \"xd\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"Geography_France\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5000230417733071,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geography_Germany\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43355274973027197,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geography_Spain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43169821210497666,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_Female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4979320271185246,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_Male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4979320271185245,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "mIfdFMn27Rzo",
        "outputId": "452d9385-df51-4cd7-a6d1-d57e6756c874"
      },
      "execution_count": 118,
      "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",
              "\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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2405f918-17a3-4bf9-bdc7-96756bb51ec4\" 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>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </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-2405f918-17a3-4bf9-bdc7-96756bb51ec4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-2405f918-17a3-4bf9-bdc7-96756bb51ec4 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-2405f918-17a3-4bf9-bdc7-96756bb51ec4');\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-c3e53a52-0cca-4a0d-bdb3-d7f4e8fdfe2b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3e53a52-0cca-4a0d-bdb3-d7f4e8fdfe2b')\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      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-c3e53a52-0cca-4a0d-bdb3-d7f4e8fdfe2b 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": "df",
              "summary": "{\n  \"name\": \"df\",\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\": \"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": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(columns=[\"Geography\",\"Gender\"])"
      ],
      "metadata": {
        "id": "zUZaIly_7vIM"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[xd.columns]=xd"
      ],
      "metadata": {
        "id": "ihLl0f-u7UDP"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['Exited']\n",
        "x=df.drop(\"Exited\",axis=1)"
      ],
      "metadata": {
        "id": "66ChrgUY7VO6"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "35dhEpqa7V-W"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree=DecisionTreeClassifier()\n",
        "model=tree.fit(x,y)\n",
        "model.score(x,y)\n",
        "\n",
        "# Overfiting Oldu :("
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TsCkJyp7XHE",
        "outputId": "12d08cdb-6d61-4d1a-e149-1add87925386"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ohe.transform([[\"France\",\"Female\"]]).toarray()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mt6ceMkE7YAl",
        "outputId": "8025a854-20c4-4a95-d46d-e633c0173e1e"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but OneHotEncoder was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7lGgoUwt7aIg"
      },
      "execution_count": 124,
      "outputs": []
    }
  ]
}