(Bank_Turnover)_Customer_Churn_Prediction_using_ANN.ipynb

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      "cell_type": "markdown",
      "source": [
        "# (Bank Turnover) Customer Churn Prediction using ANN\n",
        "- link of dataset: https://www.kaggle.com/datasets/barelydedicated/bank-customer-churn-modeling"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "### Importing libraries"
      ],
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "nBV6f24Wuorq"
      },
      "execution_count": 54,
      "outputs": []
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      "source": [
        "path = '/content/drive/MyDrive/datasets/Churn_Modelling.csv'\n",
        "\n",
        "df = pd.read_csv(path)\n",
        "df.head()"
      ],
      "metadata": {
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        "outputId": "be25bfe6-25a5-470d-a4cc-bba503c322e6"
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      "outputs": [
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              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
              "2          3    15619304      Onio          502    France  Female   42   \n",
              "3          4    15701354      Boni          699    France  Female   39   \n",
              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
              "3         93826.63       0  \n",
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      "source": [
        "### Data Cleaning"
      ],
      "metadata": {
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      }
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      "cell_type": "code",
      "source": [
        "df.info()"
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        "id": "ajp9LcJCvytW",
        "outputId": "7eaf4607-2a72-4d09-baec-1b388cf8866f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLY1WTGjv1np",
        "outputId": "fabdd0eb-8012-47aa-a5e0-6728358ae632"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber          0\n",
              "CustomerId         0\n",
              "Surname            0\n",
              "CreditScore        0\n",
              "Geography          0\n",
              "Gender             0\n",
              "Age                0\n",
              "Tenure             0\n",
              "Balance            0\n",
              "NumOfProducts      0\n",
              "HasCrCard          0\n",
              "IsActiveMember     0\n",
              "EstimatedSalary    0\n",
              "Exited             0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 57
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
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        "id": "GxUd6nSRjqzR",
        "outputId": "b89659fd-5595-4f86-a9a1-03da3abee571"
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      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Geography'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hy-BaJk2jZuR",
        "outputId": "18b176c9-8be5-464d-fb3f-9044c31e1653"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "France     5014\n",
              "Germany    2509\n",
              "Spain      2477\n",
              "Name: Geography, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
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      "cell_type": "code",
      "source": [
        "df.drop(['RowNumber', 'CustomerId', 'Surname', 'Geography'], axis=1, inplace=True)"
      ],
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      "cell_type": "markdown",
      "source": [
        "removing the rownumber, customerid, & surname column for they are personal details and may serve as a noise to our model.\n",
        "\n",
        "removing the geography for it may introduce bias in terms of just location"
      ],
      "metadata": {
        "id": "ZHVItyDjkQcU"
      }
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      },
      "execution_count": 61,
      "outputs": [
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            "text/plain": [
              "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619  Female   42       2       0.00              1          1   \n",
              "1          608  Female   41       1   83807.86              1          0   \n",
              "2          502  Female   42       8  159660.80              3          1   \n",
              "3          699  Female   39       1       0.00              2          0   \n",
              "4          850  Female   43       2  125510.82              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  \n",
              "0               1        101348.88       1  \n",
              "1               1        112542.58       0  \n",
              "2               0        113931.57       1  \n",
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              "        displayQuickchartButton(document);\n",
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              "      fill: #174EA6;\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d0ba2562-a0cd-4ecd-a678-f28deab726f1');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory Data Analysis(EDA)"
      ],
      "metadata": {
        "id": "aMeyo-8vvajV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tenure_churn_no = df[df.Exited==0].Tenure\n",
        "tenure_churn_yes = df[df.Exited==1].Tenure\n",
        "\n",
        "plt.xlabel(\"Tenure\")\n",
        "plt.ylabel(\"# of Customers\")\n",
        "plt.title(\"Customer Churn Prediction Visualization\")\n",
        "\n",
        "plt.hist([tenure_churn_yes, tenure_churn_no], color=['green', 'red'], label=['Churn=Yes', 'Churn=No'])\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "id": "jNgA07WMu3vK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "cddc34d5-37fd-4cfd-ce84-9d9ec7c62daa"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fec8ab66350>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['CreditScore'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "iKx3KVzomt20",
        "outputId": "8e7839f8-58a2-4b57-f33a-a37cd95e7cb4"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  19.,  166.,  447.,  958., 1444., 1866., 1952., 1525.,  968.,\n",
              "         655.]),\n",
              " array([350., 400., 450., 500., 550., 600., 650., 700., 750., 800., 850.]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "creditscore_churn_no = df[df.Exited == 0].CreditScore\n",
        "creditscore_churn_yes = df[df.Exited == 1].CreditScore\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.histplot(creditscore_churn_no, bins=30, color='blue', label='Churn No', kde=True)\n",
        "sns.histplot(creditscore_churn_yes, bins=30, color='orange', label='Churn Yes', kde=True)\n",
        "\n",
        "plt.xlabel('CreditScore')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('CreditScore Distribution for Churn No and Churn Yes')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Gcoq4t0emsuH",
        "outputId": "c59954ee-c82f-41a4-9703-96b5d9c72841"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ES_churn_no = df[df.Exited == 0].EstimatedSalary\n",
        "ES_churn_yes = df[df.Exited == 1].EstimatedSalary\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.histplot(ES_churn_no, bins=30, color='blue', label='Churn No')\n",
        "sns.histplot(ES_churn_yes, bins=30, color='orange', label='Churn Yes')\n",
        "\n",
        "plt.xlabel('Estimated Salary')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Estimated Salary\tDistribution for Churn No and Churn Yes')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "RXlIHxkSnpFT",
        "outputId": "e516a266-3e00-4630-ac0e-0767623381a5"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 9 (\t) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F_churn_no = df[(df.Exited == 0) & (df.Gender == 'Female')]\n",
        "F_churn_yes = df[(df.Exited == 1) & (df.Gender == 'Female')]\n",
        "\n",
        "M_churn_no = df[(df.Exited == 0) & (df.Gender == 'Male')]\n",
        "M_churn_yes = df[(df.Exited == 1) & (df.Gender == 'Male')]\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.countplot(x='Gender', hue='Exited', data=pd.concat([F_churn_no, F_churn_yes]), palette='Set1')\n",
        "plt.title('Churn Distribution by Gender - Female')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.countplot(x='Gender', hue='Exited', data=pd.concat([M_churn_no, M_churn_yes]), palette='Set2')\n",
        "plt.title('Churn Distribution by Gender - Male')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "eCQHd-F6ocAK",
        "outputId": "b7eaf8ec-f67c-4942-87dd-2350689e3835"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Processing"
      ],
      "metadata": {
        "id": "jvW0JsA8oWzy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8w4mYZOlp6Tg",
        "outputId": "93d0b4ca-18e1-4f52-c966-25ee90d46726"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 10 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   CreditScore      10000 non-null  int64  \n",
            " 1   Gender           10000 non-null  object \n",
            " 2   Age              10000 non-null  int64  \n",
            " 3   Tenure           10000 non-null  int64  \n",
            " 4   Balance          10000 non-null  float64\n",
            " 5   NumOfProducts    10000 non-null  int64  \n",
            " 6   HasCrCard        10000 non-null  int64  \n",
            " 7   IsActiveMember   10000 non-null  int64  \n",
            " 8   EstimatedSalary  10000 non-null  float64\n",
            " 9   Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(7), object(1)\n",
            "memory usage: 781.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MKKJhNsZoWgy",
        "outputId": "022915c4-5562-4de8-9637-a472e4a805c0"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619  Female   42       2       0.00              1          1   \n",
              "1          608  Female   41       1   83807.86              1          0   \n",
              "2          502  Female   42       8  159660.80              3          1   \n",
              "3          699  Female   39       1       0.00              2          0   \n",
              "4          850  Female   43       2  125510.82              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  \n",
              "0               1        101348.88       1  \n",
              "1               1        112542.58       0  \n",
              "2               0        113931.57       1  \n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-93bff5af-098f-4c3a-bcbe-800bdddc620e 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-93bff5af-098f-4c3a-bcbe-800bdddc620e');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "df['Gender'] = label_encoder.fit_transform(df['Gender'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Eq-wVVwzoapg",
        "outputId": "f4e1c209-3a9c-4d8d-9753-718d2d0a1f0b"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619       0   42       2       0.00              1          1   \n",
              "1          608       0   41       1   83807.86              1          0   \n",
              "2          502       0   42       8  159660.80              3          1   \n",
              "3          699       0   39       1       0.00              2          0   \n",
              "4          850       0   43       2  125510.82              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  \n",
              "0               1        101348.88       1  \n",
              "1               1        112542.58       0  \n",
              "2               0        113931.57       1  \n",
              "3               0         93826.63       0  \n",
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              "          const element = document.querySelector('#df-70dbb6ea-0e0d-472c-b47a-47ec40646f4b');\n",
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              "            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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scaling values\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "cols_to_scale = ['CreditScore', 'Age', 'Tenure', 'Balance', 'EstimatedSalary']\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "df2 = df.copy()\n",
        "df2[cols_to_scale] = scaler.fit_transform(df2[cols_to_scale])\n",
        "\n",
        "df2.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Dxq4tbLVq2NO",
        "outputId": "81a069a8-f8fd-46f7-bbe4-4f7dbbf94cee"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Gender       Age  Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
              "0        0.538       0  0.324324     0.2  0.000000              1          1   \n",
              "1        0.516       0  0.310811     0.1  0.334031              1          0   \n",
              "2        0.304       0  0.324324     0.8  0.636357              3          1   \n",
              "3        0.698       0  0.283784     0.1  0.000000              2          0   \n",
              "4        1.000       0  0.337838     0.2  0.500246              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Exited  \n",
              "0               1         0.506735       1  \n",
              "1               1         0.562709       0  \n",
              "2               0         0.569654       1  \n",
              "3               0         0.469120       0  \n",
              "4               1         0.395400       0  "
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-dd9d11ac-af91-4c89-b591-7c8858fe8854 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-dd9d11ac-af91-4c89-b591-7c8858fe8854');\n",
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              "                                                     [key], {});\n",
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              "\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",
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            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.get_dummies(data=df2, columns=['NumOfProducts'])\n",
        "df2.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I85E4n-Ht4NZ",
        "outputId": "dae36fd2-42fc-45b2-a602-f8d4445a8dff"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CreditScore', 'Gender', 'Age', 'Tenure', 'Balance', 'HasCrCard',\n",
              "       'IsActiveMember', 'EstimatedSalary', 'Exited', 'NumOfProducts_1',\n",
              "       'NumOfProducts_2', 'NumOfProducts_3', 'NumOfProducts_4'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for cols in df2:\n",
        "  print(f\"{cols}: {df2[cols].unique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cofz4nnIsiX2",
        "outputId": "569d44a6-f932-47eb-872b-55e8d60da659"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CreditScore: [0.538 0.516 0.304 0.698 1.    0.59  0.944 0.052 0.302 0.668 0.356 0.294\n",
            " 0.252 0.398 0.57  0.532 0.606 0.474 0.752 0.764 0.572 0.32  0.638 0.992\n",
            " 0.454 0.812 0.442 0.448 0.122 0.482 0.366 0.406 0.34  0.744 0.25  0.28\n",
            " 0.908 0.464 0.244 0.23  0.412 0.968 0.62  0.852 0.958 0.574 0.4   0.696\n",
            " 0.47  0.876 0.61  0.502 0.612 0.75  0.322 0.528 0.784 0.674 0.41  0.506\n",
            " 0.802 0.462 0.77  0.622 0.65  0.776 0.926 0.614 0.508 0.338 0.628 0.656\n",
            " 0.814 0.132 0.63  0.854 0.386 0.312 0.286 0.604 0.8   0.758 0.592 0.594\n",
            " 0.916 0.348 0.838 0.76  0.33  0.846 0.928 0.72  0.126 0.546 0.64  0.544\n",
            " 0.87  0.51  0.258 0.67  0.376 0.424 0.742 0.556 0.636 0.956 0.648 0.55\n",
            " 0.164 0.84  0.816 0.89  0.672 0.878 0.478 0.222 0.468 0.458 0.626 0.664\n",
            " 0.886 0.682 0.27  0.6   0.808 0.37  0.732 0.378 0.712 0.472 0.562 0.734\n",
            " 0.9   0.666 0.708 0.53  0.634 0.268 0.26  0.456 0.324 0.512 0.494 0.856\n",
            " 0.328 0.35  0.73  0.46  0.914 0.342 0.818 0.332 0.722 0.536 0.586 0.642\n",
            " 0.678 0.54  0.652 0.444 0.69  0.484 0.434 0.688 0.394 0.488 0.646 0.52\n",
            " 0.834 0.826 0.724 0.706 0.624 0.618 0.346 0.844 0.39  0.568 0.778 0.842\n",
            " 0.662 0.388 0.692 0.832 0.754 0.686 0.414 0.362 0.296 0.602 0.882 0.766\n",
            " 0.922 0.714 0.728 0.864 0.85  0.898 0.504 0.788 0.476 0.794 0.466 0.554\n",
            " 0.762 0.558 0.176 0.584 0.912 0.248 0.418 0.158 0.66  0.798 0.768 0.588\n",
            " 0.552 0.598 0.91  0.736 0.98  0.56  0.608 0.824 0.436 0.526 0.344 0.774\n",
            " 0.596 0.186 0.58  0.38  0.22  0.486 0.902 0.522 0.904 0.79  0.266 0.68\n",
            " 0.284 0.718 0.71  0.42  0.804 0.702 0.374 0.274 0.492 0.704 0.272 0.748\n",
            " 0.396 0.228 0.88  0.368 0.796 0.288 0.48  0.236 0.318 0.936 0.932 0.372\n",
            " 0.806 0.848 0.542 0.438 0.616 0.896 0.582 0.384 0.684 0.578 0.83  0.44\n",
            " 0.576 0.498 0.564 0.858 0.354 0.428 0.966 0.308 0.984 0.316 0.134 0.496\n",
            " 0.782 0.514 0.822 0.996 0.392 0.178 0.81  0.82  0.352 0.726 0.7   0.632\n",
            " 0.432 0.29  0.676 0.524 0.254 0.154 0.978 0.938 0.74  0.218 0.306 0.548\n",
            " 0.358 0.426 0.264 0.892 0.19  0.792 0.872 0.408 0.644 0.874 0.298 0.988\n",
            " 0.2   0.93  0.976 0.906 0.772 0.566 0.5   0.658 0.334 0.884 0.786 0.276\n",
            " 0.142 0.982 0.716 0.314 0.31  0.212 0.17  0.422 0.336 0.43  0.756 0.868\n",
            " 0.404 0.518 0.828 0.694 0.746 0.402 0.188 0.738 0.292 0.382 0.96  0.924\n",
            " 0.654 0.14  0.49  0.534 0.918 0.3   0.952 0.168 0.326 0.256 0.894 0.026\n",
            " 0.098 0.226 0.86  0.204 0.45  0.974 0.888 0.948 0.156 0.946 0.862 0.998\n",
            " 0.278 0.162 0.214 0.836 0.962 0.018 0.94  0.446 0.452 0.416 0.934 0.198\n",
            " 0.18  0.13  0.942 0.36  0.    0.192 0.15  0.78  0.262 0.866 0.016 0.99\n",
            " 0.202 0.216 0.238 0.146 0.108 0.972 0.246 0.97  0.232 0.282 0.002 0.954\n",
            " 0.986 0.03  0.364 0.128 0.206 0.242 0.102 0.92  0.964 0.24  0.194 0.144\n",
            " 0.95  0.16  0.172 0.152 0.116 0.994 0.136 0.174 0.12  0.208 0.114 0.21\n",
            " 0.224 0.072 0.11  0.066 0.09  0.234 0.166 0.184 0.148 0.196 0.182 0.034\n",
            " 0.124 0.064 0.046 0.138]\n",
            "Gender: [0 1]\n",
            "Age: [0.32432432 0.31081081 0.28378378 0.33783784 0.35135135 0.43243243\n",
            " 0.14864865 0.12162162 0.17567568 0.08108108 0.21621622 0.09459459\n",
            " 0.22972973 0.36486486 0.54054054 0.18918919 0.27027027 0.37837838\n",
            " 0.24324324 0.2027027  0.2972973  0.44594595 0.58108108 0.41891892\n",
            " 0.25675676 0.01351351 0.64864865 0.51351351 0.10810811 0.04054054\n",
            " 0.5        0.77027027 0.05405405 0.16216216 0.13513514 0.63513514\n",
            " 0.40540541 0.45945946 0.52702703 0.74324324 0.39189189 0.48648649\n",
            " 0.72972973 0.02702703 0.66216216 0.82432432 0.59459459 0.47297297\n",
            " 0.83783784 0.55405405 0.67567568 0.06756757 0.56756757 0.7027027\n",
            " 0.60810811 0.62162162 0.         0.86486486 0.68918919 0.75675676\n",
            " 0.71621622 0.78378378 0.7972973  0.94594595 0.90540541 0.89189189\n",
            " 0.81081081 0.85135135 1.         0.87837838]\n",
            "Tenure: [0.2 0.1 0.8 0.7 0.4 0.6 0.3 1.  0.5 0.9 0. ]\n",
            "Balance: [0.         0.33403148 0.63635718 ... 0.22865702 0.29922631 0.51870777]\n",
            "HasCrCard: [1 0]\n",
            "IsActiveMember: [1 0]\n",
            "EstimatedSalary: [0.50673489 0.56270874 0.56965435 ... 0.21039009 0.46442905 0.19091423]\n",
            "Exited: [1 0]\n",
            "NumOfProducts_1: [1 0]\n",
            "NumOfProducts_2: [0 1]\n",
            "NumOfProducts_3: [0 1]\n",
            "NumOfProducts_4: [0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wO1HNjxDs8Gz",
        "outputId": "52d0d8a5-4cf2-4105-b4e6-10499798560f"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df2.drop('Exited', axis=1)\n",
        "y = df2['Exited']"
      ],
      "metadata": {
        "id": "ukB3mreTssCH"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Uceb6jJ4s3gL",
        "outputId": "dec19502-b604-4fc8-f433-ff54d4692a3f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Gender       Age  Tenure   Balance  HasCrCard  IsActiveMember  \\\n",
              "0        0.538       0  0.324324     0.2  0.000000          1               1   \n",
              "1        0.516       0  0.310811     0.1  0.334031          0               1   \n",
              "2        0.304       0  0.324324     0.8  0.636357          1               0   \n",
              "3        0.698       0  0.283784     0.1  0.000000          0               0   \n",
              "4        1.000       0  0.337838     0.2  0.500246          1               1   \n",
              "\n",
              "   EstimatedSalary  NumOfProducts_1  NumOfProducts_2  NumOfProducts_3  \\\n",
              "0         0.506735                1                0                0   \n",
              "1         0.562709                1                0                0   \n",
              "2         0.569654                0                0                1   \n",
              "3         0.469120                0                1                0   \n",
              "4         0.395400                1                0                0   \n",
              "\n",
              "   NumOfProducts_4  \n",
              "0                0  \n",
              "1                0  \n",
              "2                0  \n",
              "3                0  \n",
              "4                0  "
            ],
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              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
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              "      <td>0.304</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-378affa4-b841-402f-a00b-25a434f73281')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-378affa4-b841-402f-a00b-25a434f73281 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\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-276479d8-6b06-4f36-ab85-ad88afb507f6 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-276479d8-6b06-4f36-ab85-ad88afb507f6');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1WjFI5Ss2Tc",
        "outputId": "3896b0e8-571f-4939-afd4-b4946e3284e5"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    0\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "KUp41TYqs5Wc"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGeMADh5teos",
        "outputId": "90eed235-2455-492d-f9ce-cccf6b4c0464"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4OitubqtiPo",
        "outputId": "05065dfe-8bca-4a5d-e32d-f2c730e2a48d"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4eqfNnNutZR",
        "outputId": "d7d6f2fb-acc5-4cdb-cbd7-6e24a9ebb1f1"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Training"
      ],
      "metadata": {
        "id": "a28AdrMLuTCL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(10, input_shape=(12,), activation='relu'),\n",
        "    keras.layers.Dense(5, activation='relu'),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.fit(X_train, y_train, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRujoW0Ftjes",
        "outputId": "f403bc3d-2606-4106-e128-1dd2619d7dfa"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "250/250 [==============================] - 2s 2ms/step - loss: 0.6627 - accuracy: 0.7456\n",
            "Epoch 2/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.6090 - accuracy: 0.7945\n",
            "Epoch 3/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.5722 - accuracy: 0.7945\n",
            "Epoch 4/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.5288 - accuracy: 0.8144\n",
            "Epoch 5/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.4933 - accuracy: 0.8239\n",
            "Epoch 6/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.4689 - accuracy: 0.8313\n",
            "Epoch 7/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.4502 - accuracy: 0.8364\n",
            "Epoch 8/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.4360 - accuracy: 0.8409\n",
            "Epoch 9/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.4248 - accuracy: 0.8436\n",
            "Epoch 10/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.4164 - accuracy: 0.8476\n",
            "Epoch 11/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.4102 - accuracy: 0.8493\n",
            "Epoch 12/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.4057 - accuracy: 0.8529\n",
            "Epoch 13/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.4022 - accuracy: 0.8520\n",
            "Epoch 14/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3996 - accuracy: 0.8521\n",
            "Epoch 15/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3974 - accuracy: 0.8531\n",
            "Epoch 16/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3958 - accuracy: 0.8546\n",
            "Epoch 17/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3953 - accuracy: 0.8549\n",
            "Epoch 18/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3945 - accuracy: 0.8550\n",
            "Epoch 19/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3932 - accuracy: 0.8546\n",
            "Epoch 20/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3932 - accuracy: 0.8550\n",
            "Epoch 21/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3922 - accuracy: 0.8545\n",
            "Epoch 22/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3918 - accuracy: 0.8553\n",
            "Epoch 23/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3915 - accuracy: 0.8547\n",
            "Epoch 24/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3912 - accuracy: 0.8547\n",
            "Epoch 25/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3916 - accuracy: 0.8549\n",
            "Epoch 26/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3905 - accuracy: 0.8565\n",
            "Epoch 27/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3901 - accuracy: 0.8550\n",
            "Epoch 28/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3899 - accuracy: 0.8537\n",
            "Epoch 29/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3899 - accuracy: 0.8536\n",
            "Epoch 30/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3898 - accuracy: 0.8553\n",
            "Epoch 31/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3892 - accuracy: 0.8554\n",
            "Epoch 32/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3886 - accuracy: 0.8554\n",
            "Epoch 33/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3885 - accuracy: 0.8549\n",
            "Epoch 34/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3885 - accuracy: 0.8549\n",
            "Epoch 35/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3878 - accuracy: 0.8551\n",
            "Epoch 36/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3882 - accuracy: 0.8540\n",
            "Epoch 37/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3883 - accuracy: 0.8553\n",
            "Epoch 38/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3877 - accuracy: 0.8569\n",
            "Epoch 39/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3878 - accuracy: 0.8561\n",
            "Epoch 40/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3877 - accuracy: 0.8549\n",
            "Epoch 41/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3876 - accuracy: 0.8543\n",
            "Epoch 42/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3876 - accuracy: 0.8555\n",
            "Epoch 43/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3879 - accuracy: 0.8540\n",
            "Epoch 44/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3871 - accuracy: 0.8560\n",
            "Epoch 45/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3871 - accuracy: 0.8546\n",
            "Epoch 46/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3880 - accuracy: 0.8561\n",
            "Epoch 47/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3875 - accuracy: 0.8558\n",
            "Epoch 48/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3858 - accuracy: 0.8570\n",
            "Epoch 49/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3873 - accuracy: 0.8562\n",
            "Epoch 50/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3869 - accuracy: 0.8547\n",
            "Epoch 51/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3869 - accuracy: 0.8554\n",
            "Epoch 52/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3870 - accuracy: 0.8554\n",
            "Epoch 53/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3868 - accuracy: 0.8550\n",
            "Epoch 54/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3870 - accuracy: 0.8551\n",
            "Epoch 55/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3867 - accuracy: 0.8544\n",
            "Epoch 56/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3867 - accuracy: 0.8555\n",
            "Epoch 57/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3872 - accuracy: 0.8553\n",
            "Epoch 58/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3867 - accuracy: 0.8554\n",
            "Epoch 59/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3865 - accuracy: 0.8564\n",
            "Epoch 60/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3868 - accuracy: 0.8569\n",
            "Epoch 61/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3864 - accuracy: 0.8558\n",
            "Epoch 62/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3864 - accuracy: 0.8568\n",
            "Epoch 63/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3868 - accuracy: 0.8559\n",
            "Epoch 64/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3865 - accuracy: 0.8553\n",
            "Epoch 65/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3861 - accuracy: 0.8547\n",
            "Epoch 66/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3862 - accuracy: 0.8558\n",
            "Epoch 67/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3861 - accuracy: 0.8556\n",
            "Epoch 68/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3860 - accuracy: 0.8564\n",
            "Epoch 69/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3859 - accuracy: 0.8559\n",
            "Epoch 70/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3857 - accuracy: 0.8551\n",
            "Epoch 71/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3861 - accuracy: 0.8566\n",
            "Epoch 72/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3859 - accuracy: 0.8566\n",
            "Epoch 73/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3858 - accuracy: 0.8561\n",
            "Epoch 74/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3859 - accuracy: 0.8551\n",
            "Epoch 75/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3862 - accuracy: 0.8551\n",
            "Epoch 76/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3859 - accuracy: 0.8547\n",
            "Epoch 77/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3857 - accuracy: 0.8562\n",
            "Epoch 78/100\n",
            "250/250 [==============================] - 1s 3ms/step - loss: 0.3858 - accuracy: 0.8558\n",
            "Epoch 79/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3854 - accuracy: 0.8575\n",
            "Epoch 80/100\n",
            "250/250 [==============================] - 1s 2ms/step - loss: 0.3857 - accuracy: 0.8558\n",
            "Epoch 81/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3856 - accuracy: 0.8562\n",
            "Epoch 82/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3858 - accuracy: 0.8579\n",
            "Epoch 83/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3853 - accuracy: 0.8558\n",
            "Epoch 84/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3855 - accuracy: 0.8545\n",
            "Epoch 85/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3853 - accuracy: 0.8553\n",
            "Epoch 86/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3853 - accuracy: 0.8569\n",
            "Epoch 87/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3856 - accuracy: 0.8562\n",
            "Epoch 88/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3850 - accuracy: 0.8564\n",
            "Epoch 89/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3855 - accuracy: 0.8558\n",
            "Epoch 90/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3851 - accuracy: 0.8584\n",
            "Epoch 91/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3852 - accuracy: 0.8562\n",
            "Epoch 92/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3855 - accuracy: 0.8553\n",
            "Epoch 93/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3852 - accuracy: 0.8566\n",
            "Epoch 94/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3857 - accuracy: 0.8564\n",
            "Epoch 95/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3851 - accuracy: 0.8566\n",
            "Epoch 96/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3852 - accuracy: 0.8569\n",
            "Epoch 97/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3854 - accuracy: 0.8560\n",
            "Epoch 98/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3852 - accuracy: 0.8561\n",
            "Epoch 99/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3852 - accuracy: 0.8561\n",
            "Epoch 100/100\n",
            "250/250 [==============================] - 0s 2ms/step - loss: 0.3851 - accuracy: 0.8565\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fec2f77bbe0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItuG86VUvQct",
        "outputId": "ae0fc9dc-9e82-4ede-9e7a-c0b7df9314f1"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 2ms/step - loss: 0.3843 - accuracy: 0.8590\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.38431188464164734, 0.859000027179718]"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yp = model.predict(X_test)\n",
        "yp[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vb1felnuvi1S",
        "outputId": "e50b3340-4269-4784-9657-145006db5c57"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.10672456],\n",
              "       [0.10672456],\n",
              "       [0.10672456],\n",
              "       [0.10672456],\n",
              "       [0.10672456],\n",
              "       [0.10672456],\n",
              "       [0.1639387 ],\n",
              "       [0.10672456],\n",
              "       [0.3018396 ],\n",
              "       [0.10672456],\n",
              "       [0.93250245],\n",
              "       [0.92982227],\n",
              "       [0.63021   ],\n",
              "       [0.31275678],\n",
              "       [0.10672456],\n",
              "       [0.11945565],\n",
              "       [0.10672456],\n",
              "       [0.10672456],\n",
              "       [0.10672456],\n",
              "       [0.10672456]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0z9i2b03vwcr",
        "outputId": "3684386c-3c31-4210-92b6-faa3bb227b79"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6252    0\n",
              "4684    0\n",
              "1731    0\n",
              "4742    0\n",
              "4521    0\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = []\n",
        "for element in yp:\n",
        "  if element > 0.5:\n",
        "    y_pred.append(1)\n",
        "  else:\n",
        "    y_pred.append(0)"
      ],
      "metadata": {
        "id": "kmsq7Hmgv1SF"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F550QyFMw7xt",
        "outputId": "a5e0f274-b2cf-4b9b-dbec-1ef659a87b2b"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 0, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.iloc[6412]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7R_LrGWwBbo",
        "outputId": "f1ff1152-9998-4027-9094-a1779a2c5cb0"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CreditScore        0.504000\n",
              "Gender             0.000000\n",
              "Age                0.472973\n",
              "Tenure             0.500000\n",
              "Balance            0.391668\n",
              "HasCrCard          0.000000\n",
              "IsActiveMember     1.000000\n",
              "EstimatedSalary    0.225155\n",
              "Exited             1.000000\n",
              "NumOfProducts_1    1.000000\n",
              "NumOfProducts_2    0.000000\n",
              "NumOfProducts_3    0.000000\n",
              "NumOfProducts_4    0.000000\n",
              "Name: 6412, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrdAl_vGwEH5",
        "outputId": "3c143ba3-0c59-4eae-ac09-3cda6ec06add"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.98      0.92      1607\n",
            "           1       0.81      0.37      0.51       393\n",
            "\n",
            "    accuracy                           0.86      2000\n",
            "   macro avg       0.84      0.67      0.71      2000\n",
            "weighted avg       0.85      0.86      0.84      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.math.confusion_matrix(labels=y_test, predictions=y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LpFaKTl3xaZa",
        "outputId": "1447c4ac-e4eb-4bda-94f1-69f655453af8"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[1572   35]\n",
            " [ 247  146]], shape=(2, 2), dtype=int32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "cm = tf.math.confusion_matrix(labels=y_test, predictions=y_pred)\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "82PGGdOSxcBo",
        "outputId": "cbb773b0-6f2d-4d37-a513-622e47157e24"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(95.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}