Employee_attrition.ipynb

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      "name": "Employee attrition.ipynb",
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      "metadata": {
        "id": "MVMtqLJCiAQA"
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      "source": [
        "import random\n",
        "#For dealing with tables\n",
        "import pandas as pd\n",
        "#For dealing with linear algebra\n",
        "import numpy as np\n",
        "#For data visualization and plotting graphs\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "#For minmaxscaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "#For shapiro test\n",
        "from scipy.stats import shapiro\n",
        "#For train-test split\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV\n",
        "#For RandomForest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "#Accuracy score, confusion matrix, classification report, ROC curve, AUC\n",
        "from sklearn.metrics import f1_score\n",
        "#To ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Train Data\n",
        "train_df = pd.read_csv(\"/content/train_MpHjUjU.csv\")\n",
        "#Test Data\n",
        "test_df = pd.read_csv(\"/content/train_MpHjUjU.csv\")"
      ],
      "metadata": {
        "id": "9fP1IOjYiJNn"
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      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "#Copy of train_df\n",
        "train_data=train_df.copy()\n",
        "#Copy of test_df\n",
        "test_data=test_df.copy()"
      ],
      "metadata": {
        "id": "NZIGCv4ciUhR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#The first 5 rows of train_data\n",
        "train_data.head()"
      ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MMM-YY</th>\n",
              "      <th>Emp_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>City</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Dateofjoining</th>\n",
              "      <th>LastWorkingDate</th>\n",
              "      <th>Joining Designation</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Total Business Value</th>\n",
              "      <th>Quarterly Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01-01-2016</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>Male</td>\n",
              "      <td>C23</td>\n",
              "      <td>Master</td>\n",
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              "      <td>Master</td>\n",
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            "text/plain": [
              "       MMM-YY  Emp_ID  Age  ... Designation Total Business Value Quarterly Rating\n",
              "0  01-01-2016       1   28  ...           1              2381060                2\n",
              "1  01-02-2016       1   28  ...           1              -665480                2\n",
              "2  01-03-2016       1   28  ...           1                    0                2\n",
              "3  01-11-2017       2   31  ...           2                    0                1\n",
              "4  01-12-2017       2   31  ...           2                    0                1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Features of the train data\n",
        "print(\"The features or column names of the train data are : \\n\",train_data.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o-tmxAv6iaV5",
        "outputId": "8d7ccf57-b99c-4724-a9c0-9073437aa072"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The features or column names of the train data are : \n",
            " Index(['MMM-YY', 'Emp_ID', 'Age', 'Gender', 'City', 'Education_Level',\n",
            "       'Salary', 'Dateofjoining', 'LastWorkingDate', 'Joining Designation',\n",
            "       'Designation', 'Total Business Value', 'Quarterly Rating'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Converting 'MMM-YY' feature to datetime type\n",
        "train_data['MMM-YY'] = pd.to_datetime(train_data['MMM-YY'],format=\"%d-%m-%Y\")\n",
        "\n",
        "##Converting 'Dateofjoining' feature to datetime type\n",
        "train_data['Dateofjoining'] = pd.to_datetime(train_data['Dateofjoining'],format=\"%d-%m-%Y\")\n",
        "\n",
        "##Converting 'LastWorkingDate' feature to datetime type\n",
        "train_data['LastWorkingDate'] = pd.to_datetime(train_data['LastWorkingDate'],format=\"%d-%m-%Y\")"
      ],
      "metadata": {
        "id": "B9sA6p6RigW4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking if the changes are done\n",
        "train_data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ObYB0AdpjLkP",
        "outputId": "f3053a5f-0f11-42af-80a4-b1358dc6aae6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MMM-YY                  datetime64[ns]\n",
              "Emp_ID                           int64\n",
              "Age                              int64\n",
              "Gender                          object\n",
              "City                            object\n",
              "Education_Level                 object\n",
              "Salary                           int64\n",
              "Dateofjoining           datetime64[ns]\n",
              "LastWorkingDate         datetime64[ns]\n",
              "Joining Designation              int64\n",
              "Designation                      int64\n",
              "Total Business Value             int64\n",
              "Quarterly Rating                 int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Shape of the train data\n",
        "print(\"train_data shape : \",train_data.shape)\n",
        "\n",
        "#Shape of the test data\n",
        "print(\"test_data shape : \",test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2JWrHw55CB2",
        "outputId": "1a6162be-2a32-4704-e246-42b59b3d6a09"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_data shape :  (19104, 13)\n",
            "test_data shape :  (19104, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating an empty dataframe\n",
        "df = pd.DataFrame()"
      ],
      "metadata": {
        "id": "zFp0_bh35JnP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Setting the emp_id as the new column which are unique\n",
        "df['Emp_ID'] = train_data['Emp_ID'].unique()\n",
        "\n",
        "#Setting age of the employee which takes the maximum age of the employee\n",
        "df['Age'] = list(train_data.groupby('Emp_ID',axis=0).max('MMM-YY')['Age'])\n",
        "\n",
        "#Setting gender of the employee \n",
        "df['Gender'] = list(train_data.groupby('Emp_ID').agg({'Gender':'first'})['Gender'])\n",
        "\n",
        "#Setting the city of the employee \n",
        "df['City'] = list(train_data.groupby('Emp_ID').agg({'City':'first'})['City'])\n",
        "\n",
        "#Setting the education of the employee \n",
        "df['Education'] = list(train_data.groupby('Emp_ID').agg({'Education_Level':'last'})['Education_Level'])\n",
        "\n",
        "#Setting the salary of the employee for one month\n",
        "df['Salary'] = list(train_data.groupby('Emp_ID').agg({'Salary':'last'})['Salary'])\n",
        "\n",
        "#Setting the joining designtion of the employee \n",
        "df['Joining_Designation'] = list(train_data.groupby('Emp_ID').agg({'Joining Designation':'last'})['Joining Designation'])\n",
        "\n",
        "#Setting the designtion of the employee at the time of reporting\n",
        "df['Designation'] = list(train_data.groupby('Emp_ID').agg({'Designation':'last'})['Designation'])\n",
        "\n",
        "#Setting the total business value acquired by the employee\n",
        "df['Total_Business_Value'] = list(train_data.groupby('Emp_ID',axis=0).sum('Total Business Value')['Total Business Value'])\n",
        "\n",
        "##Setting the quarterly rating of the employee\n",
        "df['Last_Quarterly_Rating'] = list(train_data.groupby('Emp_ID').agg({'Quarterly Rating':'last'})['Quarterly Rating'])"
      ],
      "metadata": {
        "id": "yZGl-Gzk5PR7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a column which tells if the quarterly rating has increased for that employee \n",
        "#for those whose quarterly rating has increased we assign the value 1\n",
        "\n",
        "#Quarterly rating at the beginning\n",
        "qrf = train_data.groupby('Emp_ID').agg({'Quarterly Rating':'first'})\n",
        "\n",
        "#Quarterly rating at the end\n",
        "qrl = train_data.groupby('Emp_ID').agg({'Quarterly Rating':'last'})\n",
        "\n",
        "#The dataset which has the employee ids and a bollean value which tells if the rating has increased\n",
        "qr = (qrl['Quarterly Rating']>qrf['Quarterly Rating']).reset_index()\n",
        "\n",
        "#the employee ids whose rating has increased\n",
        "empid = qr[qr['Quarterly Rating']==True]['Emp_ID']\n",
        "\n",
        "qri = []\n",
        "for i in df['Emp_ID']:\n",
        "    if i in empid:\n",
        "        qri.append(1)\n",
        "    else:\n",
        "        qri.append(0)\n",
        "\n",
        "df['Quarterly_Rating_Increased'] = qri"
      ],
      "metadata": {
        "id": "Mb03q1Y76kng"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a column called target which tells if the person has left the company\n",
        "#persons who have a last working date will have the value 1\n",
        "\n",
        "#The dataset which has the employee ids and specifies if last working date is null\n",
        "lwr = (train_data.groupby('Emp_ID').agg({'LastWorkingDate':'last'})['LastWorkingDate'].isna()).reset_index()\n",
        "\n",
        "#The employee ids who do not have last working date\n",
        "empid = list(lwr[lwr['LastWorkingDate']==True]['Emp_ID'])\n",
        "\n",
        "target = []\n",
        "for i in df['Emp_ID']:\n",
        "    if i in empid:\n",
        "        target.append(0)\n",
        "    elif i not in empid:\n",
        "        target.append(1)\n",
        "        \n",
        "df['Target'] = target"
      ],
      "metadata": {
        "id": "klVgbisE7yaE"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a column which tells if the monthly income has increased for that employee \n",
        "#for those whose monthly income has increased we assign the value 1\n",
        "\n",
        "#Quarterly rating at the beginning\n",
        "sf = train_data.groupby('Emp_ID').agg({'Salary':'first'})\n",
        "\n",
        "#Quarterly rating at the end\n",
        "sl = train_data.groupby('Emp_ID').agg({'Salary':'last'})\n",
        "\n",
        "#The dataset which has the employee ids and a bollean value which tells if the monthly income has increased\n",
        "s = (sl['Salary']>sf['Salary']).reset_index()\n",
        "\n",
        "#the employee ids whose monthly income has increased\n",
        "empid = s[s['Salary']==True]['Emp_ID']\n",
        "\n",
        "si = []\n",
        "for i in df['Emp_ID']:\n",
        "    if i in empid:\n",
        "        si.append(1)\n",
        "    else:\n",
        "        si.append(0)\n",
        "\n",
        "df['Salary_Increased'] = si"
      ],
      "metadata": {
        "id": "rfnRg9Yh9abV"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "fEcYQz5g-A_4",
        "outputId": "483ffcc9-f7fc-4756-ae8b-db46c0773f9d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>City</th>\n",
              "      <th>Education</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Joining_Designation</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Total_Business_Value</th>\n",
              "      <th>Last_Quarterly_Rating</th>\n",
              "      <th>Quarterly_Rating_Increased</th>\n",
              "      <th>Target</th>\n",
              "      <th>Salary_Increased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>Male</td>\n",
              "      <td>C23</td>\n",
              "      <td>Master</td>\n",
              "      <td>57387</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1715580</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>31</td>\n",
              "      <td>Male</td>\n",
              "      <td>C7</td>\n",
              "      <td>Master</td>\n",
              "      <td>67016</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>43</td>\n",
              "      <td>Male</td>\n",
              "      <td>C13</td>\n",
              "      <td>Master</td>\n",
              "      <td>65603</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>350000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>C9</td>\n",
              "      <td>College</td>\n",
              "      <td>46368</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120360</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>31</td>\n",
              "      <td>Female</td>\n",
              "      <td>C11</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>78728</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1265000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4eaa808f-03c0-4a84-9a9a-160601938245')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    [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-4eaa808f-03c0-4a84-9a9a-160601938245 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-4eaa808f-03c0-4a84-9a9a-160601938245');\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",
              "  "
            ],
            "text/plain": [
              "   Emp_ID  Age  Gender  ... Quarterly_Rating_Increased Target  Salary_Increased\n",
              "0       1   28    Male  ...                          0      1                 0\n",
              "1       2   31    Male  ...                          0      0                 0\n",
              "2       4   43    Male  ...                          1      1                 0\n",
              "3       5   29    Male  ...                          0      1                 0\n",
              "4       6   31  Female  ...                          0      0                 0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "HzRI-Oja-FIx",
        "outputId": "d256a35a-842c-47c9-deab-3fb36fa4466c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4c0c39a9-0c97-46a2-a520-ca20fefbc696\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Emp_ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Joining_Designation</th>\n",
              "      <th>Designation</th>\n",
              "      <th>Total_Business_Value</th>\n",
              "      <th>Last_Quarterly_Rating</th>\n",
              "      <th>Quarterly_Rating_Increased</th>\n",
              "      <th>Target</th>\n",
              "      <th>Salary_Increased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2.381000e+03</td>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2381.000000</td>\n",
              "      <td>2381.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1397.559009</td>\n",
              "      <td>33.663167</td>\n",
              "      <td>59334.157077</td>\n",
              "      <td>1.820244</td>\n",
              "      <td>2.096598</td>\n",
              "      <td>4.586742e+06</td>\n",
              "      <td>1.427971</td>\n",
              "      <td>0.128097</td>\n",
              "      <td>0.678706</td>\n",
              "      <td>0.015120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>806.161628</td>\n",
              "      <td>5.983375</td>\n",
              "      <td>28383.666384</td>\n",
              "      <td>0.841433</td>\n",
              "      <td>0.941522</td>\n",
              "      <td>9.127115e+06</td>\n",
              "      <td>0.809839</td>\n",
              "      <td>0.334268</td>\n",
              "      <td>0.467071</td>\n",
              "      <td>0.122055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>10747.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.385530e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>695.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>39104.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1400.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>55315.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.176800e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2100.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>75986.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.173650e+06</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2788.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>188418.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.533106e+07</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c0c39a9-0c97-46a2-a520-ca20fefbc696')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\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",
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              "    }\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-4c0c39a9-0c97-46a2-a520-ca20fefbc696 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-4c0c39a9-0c97-46a2-a520-ca20fefbc696');\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",
              "  "
            ],
            "text/plain": [
              "            Emp_ID          Age  ...       Target  Salary_Increased\n",
              "count  2381.000000  2381.000000  ...  2381.000000       2381.000000\n",
              "mean   1397.559009    33.663167  ...     0.678706          0.015120\n",
              "std     806.161628     5.983375  ...     0.467071          0.122055\n",
              "min       1.000000    21.000000  ...     0.000000          0.000000\n",
              "25%     695.000000    29.000000  ...     0.000000          0.000000\n",
              "50%    1400.000000    33.000000  ...     1.000000          0.000000\n",
              "75%    2100.000000    37.000000  ...     1.000000          0.000000\n",
              "max    2788.000000    58.000000  ...     1.000000          1.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=['O'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "eF-Y5qSY-aaD",
        "outputId": "008c43cd-3bee-4499-d7b1-e1d7a8ee840b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4c6fcc01-f3b8-4c59-ab6f-a40ef34983aa\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>City</th>\n",
              "      <th>Education</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2381</td>\n",
              "      <td>2381</td>\n",
              "      <td>2381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>29</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Male</td>\n",
              "      <td>C20</td>\n",
              "      <td>Master</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1404</td>\n",
              "      <td>152</td>\n",
              "      <td>802</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Gender  City Education\n",
              "count    2381  2381      2381\n",
              "unique      2    29         3\n",
              "top      Male   C20    Master\n",
              "freq     1404   152       802"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNnyz2-Q-u8u",
        "outputId": "24e40fc7-b9ea-4d83-c56c-4e988523a8a8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1616\n",
              "0     765\n",
              "Name: Target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Target'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L22PfbyU_LJa",
        "outputId": "33693bf0-9a5a-4c4d-99d5-3559147d84f9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.678706\n",
              "0    0.321294\n",
              "Name: Target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gender encoding\n",
        "df['Gender'] = df['Gender'].astype('category')\n",
        "df['Gender_encode'] = df['Gender'].cat.codes"
      ],
      "metadata": {
        "id": "RZn36m1UFnxf"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#One hot encoding for City, Education, Joining_Designation, Designation, Last_Quarterly_Rating\n",
        "df = pd.concat([df,pd.get_dummies(df['City'],prefix='City')],axis=1)\n",
        "\n",
        "df = pd.concat([df,pd.get_dummies(df['Education'],prefix='Education')],axis=1)\n",
        "\n",
        "df = pd.concat([df,pd.get_dummies(df['Joining_Designation'],prefix='JDesignation')],axis=1)\n",
        "\n",
        "df = pd.concat([df,pd.get_dummies(df['Designation'],prefix='Designation')],axis=1)\n",
        "\n",
        "df = pd.concat([df,pd.get_dummies(df['Last_Quarterly_Rating'],prefix='LQR')],axis=1)"
      ],
      "metadata": {
        "id": "mShNlTAJHMxF"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "#Mathematically learning the distribution\n",
        "df['Age_scaled'] = scaler.fit_transform((df['Age'].values).reshape(-1,1))\n",
        "df['Salary_scaled'] = scaler.fit_transform((df['Salary'].values).reshape(-1,1))\n",
        "df['Business _Value_scaled']=scaler.fit_transform((df['Total_Business_Value'].values).reshape(-1,1))"
      ],
      "metadata": {
        "id": "Cs68e8JvHPzt"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df.copy()\n",
        "df.drop(['Emp_ID', 'Age', 'Gender', 'City', 'Education', 'Salary',\n",
        "       'Joining_Designation', 'Designation', 'Total_Business_Value'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "KcAf83wXHm2b"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Variables\n",
        "X = df.drop('Target',axis=1)\n",
        "\n",
        "#Target Variable\n",
        "y = df['Target']"
      ],
      "metadata": {
        "id": "kQhc7SqDHrE2"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modelling"
      ],
      "metadata": {
        "id": "ryjb0pjCMVGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param = {'max_depth':[4,5,6,7,8,9,10], 'n_estimators':[50,100,150,200,250]}\n",
        "\n",
        "random_forest = RandomForestClassifier()\n",
        "\n",
        "c = GridSearchCV(random_forest,param,cv=5,scoring='f1')\n",
        "c.fit(X,y)\n",
        "\n",
        "def display(results):\n",
        "    print(f'Best parameters are : {results.best_params_}')\n",
        "    print(f'The score is : {results.best_score_}')\n",
        "display(c)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnJyeFUCHwkA",
        "outputId": "57721efb-d1f7-4b20-db80-230e31025cfa"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters are : {'max_depth': 8, 'n_estimators': 250}\n",
            "The score is : 0.8696675903768589\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree = RandomForestClassifier(max_depth=10, n_estimators=250)\n",
        "rf_model=decision_tree.fit(X,y)"
      ],
      "metadata": {
        "id": "MHeaH50fID32"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = rf_model.predict(X)\n",
        "print(\"The f1-score = \",f1_score(y,pred))\n",
        "df1['Pred'] = pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADirRvk-LHNg",
        "outputId": "8de21024-1ba4-47f0-92eb-2f8ecba74df7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The f1-score =  0.8991981672394044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission = test_df.copy()\n",
        "empid = submission['Emp_ID']\n",
        "\n",
        "tr = []\n",
        "\n",
        "for i in empid:\n",
        "    tr.append(df1[df1['Emp_ID']==i]['Pred'].item())"
      ],
      "metadata": {
        "id": "B_6L4t47KV9R"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "ajK9v5eXKtdb",
        "outputId": "1abab1ee-549e-4c1a-eeeb-38316b3a8b4e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-78b4efab-a31f-4fc8-bde8-fe98c0edb6a5\">\n",
              "    <div 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>Emp_ID</th>\n",
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              "      <th>Designation</th>\n",
              "      <th>Total_Business_Value</th>\n",
              "      <th>Last_Quarterly_Rating</th>\n",
              "      <th>Quarterly_Rating_Increased</th>\n",
              "      <th>Target</th>\n",
              "      <th>Salary_Increased</th>\n",
              "      <th>Gender_encode</th>\n",
              "      <th>City_C1</th>\n",
              "      <th>City_C10</th>\n",
              "      <th>City_C11</th>\n",
              "      <th>City_C12</th>\n",
              "      <th>City_C13</th>\n",
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              "      <th>City_C16</th>\n",
              "      <th>City_C17</th>\n",
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              "      <th>City_C2</th>\n",
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              "      <th>City_C26</th>\n",
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              "      <th>City_C28</th>\n",
              "      <th>City_C29</th>\n",
              "      <th>City_C3</th>\n",
              "      <th>City_C4</th>\n",
              "      <th>City_C5</th>\n",
              "      <th>City_C6</th>\n",
              "      <th>City_C7</th>\n",
              "      <th>City_C8</th>\n",
              "      <th>City_C9</th>\n",
              "      <th>Education_Bachelor</th>\n",
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              "      <th>Education_Master</th>\n",
              "      <th>JDesignation_1</th>\n",
              "      <th>JDesignation_2</th>\n",
              "      <th>JDesignation_3</th>\n",
              "      <th>JDesignation_4</th>\n",
              "      <th>JDesignation_5</th>\n",
              "      <th>Designation_1</th>\n",
              "      <th>Designation_2</th>\n",
              "      <th>Designation_3</th>\n",
              "      <th>Designation_4</th>\n",
              "      <th>Designation_5</th>\n",
              "      <th>LQR_1</th>\n",
              "      <th>LQR_2</th>\n",
              "      <th>LQR_3</th>\n",
              "      <th>LQR_4</th>\n",
              "      <th>Age_scaled</th>\n",
              "      <th>Salary_scaled</th>\n",
              "      <th>Business _Value_scaled</th>\n",
              "      <th>Pred</th>\n",
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              "  </thead>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.594595</td>\n",
              "      <td>0.308750</td>\n",
              "      <td>0.017944</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>C9</td>\n",
              "      <td>College</td>\n",
              "      <td>46368</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120360</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.216216</td>\n",
              "      <td>0.200489</td>\n",
              "      <td>0.015570</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>31</td>\n",
              "      <td>Female</td>\n",
              "      <td>C11</td>\n",
              "      <td>Bachelor</td>\n",
              "      <td>78728</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1265000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.270270</td>\n",
              "      <td>0.382623</td>\n",
              "      <td>0.027405</td>\n",
              "      <td>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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2376</th>\n",
              "      <td>2784</td>\n",
              "      <td>34</td>\n",
              "      <td>Male</td>\n",
              "      <td>C24</td>\n",
              "      <td>College</td>\n",
              "      <td>82815</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>21748820</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.351351</td>\n",
              "      <td>0.405626</td>\n",
              "      <td>0.239197</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2377</th>\n",
              "      <td>2785</td>\n",
              "      <td>34</td>\n",
              "      <td>Female</td>\n",
              "      <td>C9</td>\n",
              "      <td>College</td>\n",
              "      <td>12105</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.351351</td>\n",
              "      <td>0.007643</td>\n",
              "      <td>0.014326</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2378</th>\n",
              "      <td>2786</td>\n",
              "      <td>45</td>\n",
              "      <td>Male</td>\n",
              "      <td>C19</td>\n",
              "      <td>College</td>\n",
              "      <td>35370</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2815090</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.648649</td>\n",
              "      <td>0.138588</td>\n",
              "      <td>0.043432</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2379</th>\n",
              "      <td>2787</td>\n",
              "      <td>28</td>\n",
              "      <td>Female</td>\n",
              "      <td>C20</td>\n",
              "      <td>Master</td>\n",
              "      <td>69498</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>977830</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.189189</td>\n",
              "      <td>0.330673</td>\n",
              "      <td>0.024436</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2380</th>\n",
              "      <td>2788</td>\n",
              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>C27</td>\n",
              "      <td>Master</td>\n",
              "      <td>70254</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2298240</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.243243</td>\n",
              "      <td>0.334928</td>\n",
              "      <td>0.038088</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2381 rows × 64 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78b4efab-a31f-4fc8-bde8-fe98c0edb6a5')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \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",
              "      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",
              "    [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-78b4efab-a31f-4fc8-bde8-fe98c0edb6a5 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-78b4efab-a31f-4fc8-bde8-fe98c0edb6a5');\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",
              "  "
            ],
            "text/plain": [
              "      Emp_ID  Age  Gender  ... Salary_scaled Business _Value_scaled  Pred\n",
              "0          1   28    Male  ...      0.262508               0.032064     1\n",
              "1          2   31    Male  ...      0.316703               0.014326     1\n",
              "2          4   43    Male  ...      0.308750               0.017944     1\n",
              "3          5   29    Male  ...      0.200489               0.015570     1\n",
              "4          6   31  Female  ...      0.382623               0.027405     0\n",
              "...      ...  ...     ...  ...           ...                    ...   ...\n",
              "2376    2784   34    Male  ...      0.405626               0.239197     0\n",
              "2377    2785   34  Female  ...      0.007643               0.014326     1\n",
              "2378    2786   45    Male  ...      0.138588               0.043432     1\n",
              "2379    2787   28  Female  ...      0.330673               0.024436     1\n",
              "2380    2788   30    Male  ...      0.334928               0.038088     0\n",
              "\n",
              "[2381 rows x 64 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Total number of columns (64) exceeds max_columns (20) limiting to first (20) columns.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gGG-h3a4LQs6",
        "outputId": "ccba6bcb-677e-4bc0-dd26-774377ca54db"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(741, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MbJZVlrRLX4g"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}