hacker_earth.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "hacker_earth.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VO5bCki77gky"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOIbdEjA8984"
      },
      "source": [
        "data_train=pd.read_csv(\"/content/Train.csv\",index_col=0)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "OMLcw0Oa9NAM",
        "outputId": "b73c76bc-c1a2-41a4-edab-b9db42291581"
      },
      "source": [
        "data_train.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>EmpName</th>\n",
              "      <th>LanguageOfCommunication</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobProfileIDApplyingFor</th>\n",
              "      <th>HighestDegree</th>\n",
              "      <th>DegreeBranch</th>\n",
              "      <th>GraduatingInstitute</th>\n",
              "      <th>LatestDegreeCGPA</th>\n",
              "      <th>YearsOfExperince</th>\n",
              "      <th>GraduationYear</th>\n",
              "      <th>CurrentCTC</th>\n",
              "      <th>ExpectedCTC</th>\n",
              "      <th>MartialStatus</th>\n",
              "      <th>EmpScore</th>\n",
              "      <th>CurrentDesignation</th>\n",
              "      <th>CurrentCompanyType</th>\n",
              "      <th>DepartmentInCompany</th>\n",
              "      <th>TotalLeavesTaken</th>\n",
              "      <th>BiasInfluentialFactor</th>\n",
              "      <th>FitmentPercent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmpID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11041</th>\n",
              "      <td>John</td>\n",
              "      <td>English</td>\n",
              "      <td>35</td>\n",
              "      <td>Male</td>\n",
              "      <td>JR85289</td>\n",
              "      <td>B.Tech</td>\n",
              "      <td>Electrical</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>2009</td>\n",
              "      <td>21</td>\n",
              "      <td>26</td>\n",
              "      <td>Married</td>\n",
              "      <td>5</td>\n",
              "      <td>SSE</td>\n",
              "      <td>Enterprise</td>\n",
              "      <td>Design</td>\n",
              "      <td>20</td>\n",
              "      <td>YearsOfExperince</td>\n",
              "      <td>95.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15079</th>\n",
              "      <td>William</td>\n",
              "      <td>English</td>\n",
              "      <td>26</td>\n",
              "      <td>Male</td>\n",
              "      <td>JR87525</td>\n",
              "      <td>B.Tech</td>\n",
              "      <td>Artificial Intelligence</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>2018</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>Married</td>\n",
              "      <td>5</td>\n",
              "      <td>BA</td>\n",
              "      <td>MidSized</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>67.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18638</th>\n",
              "      <td>James</td>\n",
              "      <td>English</td>\n",
              "      <td>36</td>\n",
              "      <td>Female</td>\n",
              "      <td>JR87525</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Computer Science</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>15</td>\n",
              "      <td>24</td>\n",
              "      <td>Single</td>\n",
              "      <td>5</td>\n",
              "      <td>SDE</td>\n",
              "      <td>MidSized</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>19</td>\n",
              "      <td>Gender</td>\n",
              "      <td>91.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3941</th>\n",
              "      <td>Charles</td>\n",
              "      <td>English</td>\n",
              "      <td>29</td>\n",
              "      <td>Female</td>\n",
              "      <td>JR87525</td>\n",
              "      <td>BCA</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>16</td>\n",
              "      <td>24</td>\n",
              "      <td>Married</td>\n",
              "      <td>5</td>\n",
              "      <td>SDE</td>\n",
              "      <td>Startup</td>\n",
              "      <td>Product</td>\n",
              "      <td>16</td>\n",
              "      <td>Gender</td>\n",
              "      <td>72.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5936</th>\n",
              "      <td>George</td>\n",
              "      <td>English</td>\n",
              "      <td>25</td>\n",
              "      <td>Male</td>\n",
              "      <td>JR70175</td>\n",
              "      <td>Dual M.Tech</td>\n",
              "      <td>Computer Science</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2019</td>\n",
              "      <td>24</td>\n",
              "      <td>32</td>\n",
              "      <td>Married</td>\n",
              "      <td>5</td>\n",
              "      <td>SDE</td>\n",
              "      <td>Enterprise</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>10</td>\n",
              "      <td>DegreeBranch</td>\n",
              "      <td>86.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       EmpName LanguageOfCommunication  ...  BiasInfluentialFactor FitmentPercent\n",
              "EmpID                                   ...                                      \n",
              "11041     John                 English  ...       YearsOfExperince          95.40\n",
              "15079  William                 English  ...                    NaN          67.09\n",
              "18638    James                 English  ...                 Gender          91.26\n",
              "3941   Charles                 English  ...                 Gender          72.29\n",
              "5936    George                 English  ...           DegreeBranch          86.34\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xx1GGwfk9op8",
        "outputId": "8940df8b-6263-4538-a84f-bdae17ca4bcb"
      },
      "source": [
        "data_train.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13645, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ao64DucmrgS",
        "outputId": "5b392f87-1da7-40a5-92ab-5e54766caaf2"
      },
      "source": [
        "data_train.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 13645 entries, 11041 to 4668\n",
            "Data columns (total 21 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   EmpName                  13645 non-null  object \n",
            " 1   LanguageOfCommunication  13645 non-null  object \n",
            " 2   Age                      13645 non-null  int64  \n",
            " 3   Gender                   13645 non-null  object \n",
            " 4   JobProfileIDApplyingFor  13645 non-null  object \n",
            " 5   HighestDegree            13645 non-null  object \n",
            " 6   DegreeBranch             13645 non-null  object \n",
            " 7   GraduatingInstitute      13645 non-null  object \n",
            " 8   LatestDegreeCGPA         13645 non-null  int64  \n",
            " 9   YearsOfExperince         13645 non-null  int64  \n",
            " 10  GraduationYear           13645 non-null  int64  \n",
            " 11  CurrentCTC               13645 non-null  int64  \n",
            " 12  ExpectedCTC              13645 non-null  int64  \n",
            " 13  MartialStatus            13645 non-null  object \n",
            " 14  EmpScore                 13645 non-null  int64  \n",
            " 15  CurrentDesignation       13645 non-null  object \n",
            " 16  CurrentCompanyType       13645 non-null  object \n",
            " 17  DepartmentInCompany      13645 non-null  object \n",
            " 18  TotalLeavesTaken         13645 non-null  int64  \n",
            " 19  BiasInfluentialFactor    10309 non-null  object \n",
            " 20  FitmentPercent           13645 non-null  float64\n",
            "dtypes: float64(1), int64(8), object(12)\n",
            "memory usage: 2.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmoR1lC89PHI",
        "outputId": "622e02a8-3544-43d4-c207-3c60c988bf72"
      },
      "source": [
        "data_train.isnull().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EmpName                       0\n",
              "LanguageOfCommunication       0\n",
              "Age                           0\n",
              "Gender                        0\n",
              "JobProfileIDApplyingFor       0\n",
              "HighestDegree                 0\n",
              "DegreeBranch                  0\n",
              "GraduatingInstitute           0\n",
              "LatestDegreeCGPA              0\n",
              "YearsOfExperince              0\n",
              "GraduationYear                0\n",
              "CurrentCTC                    0\n",
              "ExpectedCTC                   0\n",
              "MartialStatus                 0\n",
              "EmpScore                      0\n",
              "CurrentDesignation            0\n",
              "CurrentCompanyType            0\n",
              "DepartmentInCompany           0\n",
              "TotalLeavesTaken              0\n",
              "BiasInfluentialFactor      3336\n",
              "FitmentPercent                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIGQgOs5_Ebq"
      },
      "source": [
        "data_train=data_train.drop(\"EmpName\",axis=1)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGms5jvq3kvx",
        "outputId": "d34e655e-1ad9-4193-f21d-161bf41e7db4"
      },
      "source": [
        "data_train.columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['LanguageOfCommunication', 'Age', 'Gender', 'JobProfileIDApplyingFor',\n",
              "       'HighestDegree', 'DegreeBranch', 'GraduatingInstitute',\n",
              "       'LatestDegreeCGPA', 'YearsOfExperince', 'GraduationYear', 'CurrentCTC',\n",
              "       'ExpectedCTC', 'MartialStatus', 'EmpScore', 'CurrentDesignation',\n",
              "       'CurrentCompanyType', 'DepartmentInCompany', 'TotalLeavesTaken',\n",
              "       'BiasInfluentialFactor', 'FitmentPercent'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-GS__z43sCF"
      },
      "source": [
        "features=['LanguageOfCommunication', 'Age', 'Gender', 'JobProfileIDApplyingFor',\n",
        "       'HighestDegree', 'DegreeBranch', 'GraduatingInstitute',\n",
        "       'LatestDegreeCGPA', 'YearsOfExperince', 'GraduationYear', 'CurrentCTC',\n",
        "       'ExpectedCTC', 'MartialStatus', 'EmpScore', 'CurrentDesignation',\n",
        "       'CurrentCompanyType', 'DepartmentInCompany', 'TotalLeavesTaken']"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYaXiFAt3zxy"
      },
      "source": [
        "x=data_train.loc[:,features]\n",
        "y=data_train.FitmentPercent"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9P75i353-Tz"
      },
      "source": [
        "x_object=x.select_dtypes(include=\"object\")\n",
        "x_other=x.select_dtypes(exclude=\"object\")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "qMI0XJiW43YC",
        "outputId": "6313761b-c87e-49d7-e53e-da32fc0bd9ce"
      },
      "source": [
        "x_other.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Age</th>\n",
              "      <th>LatestDegreeCGPA</th>\n",
              "      <th>YearsOfExperince</th>\n",
              "      <th>GraduationYear</th>\n",
              "      <th>CurrentCTC</th>\n",
              "      <th>ExpectedCTC</th>\n",
              "      <th>EmpScore</th>\n",
              "      <th>TotalLeavesTaken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmpID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11041</th>\n",
              "      <td>35</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>2009</td>\n",
              "      <td>21</td>\n",
              "      <td>26</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15079</th>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>2018</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18638</th>\n",
              "      <td>36</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>15</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3941</th>\n",
              "      <td>29</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>16</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5936</th>\n",
              "      <td>25</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2019</td>\n",
              "      <td>24</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Age  LatestDegreeCGPA  ...  EmpScore  TotalLeavesTaken\n",
              "EmpID                         ...                            \n",
              "11041   35                 7  ...         5                20\n",
              "15079   26                 7  ...         5                 6\n",
              "18638   36                 6  ...         5                19\n",
              "3941    29                 5  ...         5                16\n",
              "5936    25                 8  ...         5                10\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3s9clN1DDfw"
      },
      "source": [
        "from sklearn import preprocessing"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgqW2Wpf4-Sl"
      },
      "source": [
        "x_object=x_object.apply(preprocessing.LabelEncoder().fit_transform)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jB1O0Jb2DKfN"
      },
      "source": [
        "X=pd.concat([x_object,x_other],axis=1)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "vbxxX-nEDaVG",
        "outputId": "449dc6fd-b7fa-4dec-af32-1553c8f7c429"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>LanguageOfCommunication</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobProfileIDApplyingFor</th>\n",
              "      <th>HighestDegree</th>\n",
              "      <th>DegreeBranch</th>\n",
              "      <th>GraduatingInstitute</th>\n",
              "      <th>MartialStatus</th>\n",
              "      <th>CurrentDesignation</th>\n",
              "      <th>CurrentCompanyType</th>\n",
              "      <th>DepartmentInCompany</th>\n",
              "      <th>Age</th>\n",
              "      <th>LatestDegreeCGPA</th>\n",
              "      <th>YearsOfExperince</th>\n",
              "      <th>GraduationYear</th>\n",
              "      <th>CurrentCTC</th>\n",
              "      <th>ExpectedCTC</th>\n",
              "      <th>EmpScore</th>\n",
              "      <th>TotalLeavesTaken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmpID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11041</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>2009</td>\n",
              "      <td>21</td>\n",
              "      <td>26</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15079</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>2018</td>\n",
              "      <td>15</td>\n",
              "      <td>19</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18638</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>36</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>15</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3941</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>29</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>16</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5936</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2019</td>\n",
              "      <td>24</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       LanguageOfCommunication  Gender  ...  EmpScore  TotalLeavesTaken\n",
              "EmpID                                   ...                            \n",
              "11041                        0       1  ...         5                20\n",
              "15079                        0       1  ...         5                 6\n",
              "18638                        0       0  ...         5                19\n",
              "3941                         0       0  ...         5                16\n",
              "5936                         0       1  ...         5                10\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LP7ObfF9DnkK",
        "outputId": "6c0a5a83-c595-445a-9df1-2f314dd7dea7"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EmpID\n",
              "11041    95.40\n",
              "15079    67.09\n",
              "18638    91.26\n",
              "3941     72.29\n",
              "5936     86.34\n",
              "Name: FitmentPercent, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HziKHirDxGO"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCG4DpWCD87r"
      },
      "source": [
        "rf_regr=RandomForestRegressor(max_depth=5,random_state=0)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9KA2PFRfJ3EQ",
        "outputId": "78880d7f-a35a-4d40-a3b9-3efc1bfeeecc"
      },
      "source": [
        "rf_regr.fit(X,y)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyxh8SzmKBMe",
        "outputId": "80e420b5-aaa4-442a-e0d9-0b27df01eaa4"
      },
      "source": [
        "score1=rf_regr.score(X,y)\n",
        "score1"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6504709760052619"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7c3Ca73-MTih"
      },
      "source": [
        "data_train[\"BiasInfluentialFactor\"]=data_train.BiasInfluentialFactor.fillna(\" \")"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6W8ss8WM14N",
        "outputId": "2250cb7e-821e-497f-8325-5efbf39c5fbc"
      },
      "source": [
        "data_train[\"BiasInfluentialFactor\"].isnull().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNek1HnvLNH6",
        "outputId": "d2ea5c54-2c2e-497b-e364-e55736530f3f"
      },
      "source": [
        "data_train.BiasInfluentialFactor.unique()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['YearsOfExperince', ' ', 'Gender', 'DegreeBranch',\n",
              "       'CurrentCompanyType', 'MartialStatus', 'HighestDegree', 'EmpScore',\n",
              "       'Ethinicity', 'LatestDegreeCGPA'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upChewGBL9Ka"
      },
      "source": [
        "data_train[\"BiasInfluentialFactor\"]=data_train.BiasInfluentialFactor.map({'YearsOfExperince':1, \n",
        "                                                                          ' ':0, \n",
        "                                                                          'Gender':2,\n",
        "                                                                          'DegreeBranch':3,\n",
        "       'CurrentCompanyType':4, 'MartialStatus':5, 'HighestDegree':6, 'EmpScore':7,\n",
        "       'Ethinicity':8, 'LatestDegreeCGPA':9})"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_O_FrirpU69w"
      },
      "source": [
        "y1=data_train.BiasInfluentialFactor"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IfG1P2uAVR0M",
        "outputId": "3033d70b-54a8-4a9b-80e0-9437f7a5fc55"
      },
      "source": [
        "y1.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EmpID\n",
              "11041    1\n",
              "15079    0\n",
              "18638    2\n",
              "3941     2\n",
              "5936     3\n",
              "Name: BiasInfluentialFactor, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ile8MpFUV0fW"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf_clf=RandomForestClassifier(max_depth=4,random_state=0)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UejRW8dkWOHL",
        "outputId": "79280fd9-880d-47b4-9c13-2fc9529e52ee"
      },
      "source": [
        "rf_clf.fit(X,y1)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=4, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z50mii1LVXun",
        "outputId": "4a0ad7f2-6e49-40bb-81af-a3478242ac60"
      },
      "source": [
        "score2=rf_clf.score(X,y1)\n",
        "score2"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXZPBCHRWzcb"
      },
      "source": [
        "data_test=pd.read_csv(\"/content/Test.csv\",index_col=0)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "oR4DrPRTW5jT",
        "outputId": "4c813ffa-49c7-4123-94e4-3cc06a5dd4b1"
      },
      "source": [
        "data_test.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>EmpName</th>\n",
              "      <th>LanguageOfCommunication</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobProfileIDApplyingFor</th>\n",
              "      <th>HighestDegree</th>\n",
              "      <th>DegreeBranch</th>\n",
              "      <th>GraduatingInstitute</th>\n",
              "      <th>LatestDegreeCGPA</th>\n",
              "      <th>YearsOfExperince</th>\n",
              "      <th>GraduationYear</th>\n",
              "      <th>CurrentCTC</th>\n",
              "      <th>ExpectedCTC</th>\n",
              "      <th>MartialStatus</th>\n",
              "      <th>EmpScore</th>\n",
              "      <th>CurrentDesignation</th>\n",
              "      <th>CurrentCompanyType</th>\n",
              "      <th>DepartmentInCompany</th>\n",
              "      <th>TotalLeavesTaken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmpID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5664</th>\n",
              "      <td>Theo</td>\n",
              "      <td>Native</td>\n",
              "      <td>35</td>\n",
              "      <td>Male</td>\n",
              "      <td>JR85289</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2016</td>\n",
              "      <td>27</td>\n",
              "      <td>34</td>\n",
              "      <td>Single</td>\n",
              "      <td>4</td>\n",
              "      <td>SDE</td>\n",
              "      <td>Enterprise</td>\n",
              "      <td>Finance</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23568</th>\n",
              "      <td>Vernie</td>\n",
              "      <td>English</td>\n",
              "      <td>35</td>\n",
              "      <td>Male</td>\n",
              "      <td>JR85289</td>\n",
              "      <td>B.Tech</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>2009</td>\n",
              "      <td>29</td>\n",
              "      <td>33</td>\n",
              "      <td>Married</td>\n",
              "      <td>4</td>\n",
              "      <td>EM</td>\n",
              "      <td>Enterprise</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21490</th>\n",
              "      <td>Wendell</td>\n",
              "      <td>English</td>\n",
              "      <td>28</td>\n",
              "      <td>Male</td>\n",
              "      <td>JR87525</td>\n",
              "      <td>M.Tech</td>\n",
              "      <td>Artificial Intelligence</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>2019</td>\n",
              "      <td>18</td>\n",
              "      <td>25</td>\n",
              "      <td>Married</td>\n",
              "      <td>3</td>\n",
              "      <td>SDE</td>\n",
              "      <td>Startup</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8363</th>\n",
              "      <td>West</td>\n",
              "      <td>Hindi</td>\n",
              "      <td>37</td>\n",
              "      <td>Male</td>\n",
              "      <td>JR87525</td>\n",
              "      <td>MS</td>\n",
              "      <td>Computer Science</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>2010</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>Single</td>\n",
              "      <td>5</td>\n",
              "      <td>DS</td>\n",
              "      <td>Enterprise</td>\n",
              "      <td>Product</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6165</th>\n",
              "      <td>Alford</td>\n",
              "      <td>Hindi</td>\n",
              "      <td>36</td>\n",
              "      <td>Female</td>\n",
              "      <td>JR85289</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Artificial Intelligence</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>22</td>\n",
              "      <td>32</td>\n",
              "      <td>Married</td>\n",
              "      <td>4</td>\n",
              "      <td>SDE</td>\n",
              "      <td>Startup</td>\n",
              "      <td>Engineering</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       EmpName LanguageOfCommunication  ...  DepartmentInCompany TotalLeavesTaken\n",
              "EmpID                                   ...                                      \n",
              "5664      Theo                  Native  ...              Finance               18\n",
              "23568   Vernie                 English  ...          Engineering                8\n",
              "21490  Wendell                 English  ...          Engineering               18\n",
              "8363      West                   Hindi  ...              Product               17\n",
              "6165    Alford                   Hindi  ...          Engineering                9\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niQ7FvijXDm_"
      },
      "source": [
        "data_test=data_test.drop(\"EmpName\",axis=1)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOb2kECgXQR7"
      },
      "source": [
        "data_test=data_test.apply(preprocessing.LabelEncoder().fit_transform)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "CKmvKARoXdeo",
        "outputId": "330b87ab-21a0-43ef-a9d9-47445c1d89e3"
      },
      "source": [
        "data_test.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>LanguageOfCommunication</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobProfileIDApplyingFor</th>\n",
              "      <th>HighestDegree</th>\n",
              "      <th>DegreeBranch</th>\n",
              "      <th>GraduatingInstitute</th>\n",
              "      <th>LatestDegreeCGPA</th>\n",
              "      <th>YearsOfExperince</th>\n",
              "      <th>GraduationYear</th>\n",
              "      <th>CurrentCTC</th>\n",
              "      <th>ExpectedCTC</th>\n",
              "      <th>MartialStatus</th>\n",
              "      <th>EmpScore</th>\n",
              "      <th>CurrentDesignation</th>\n",
              "      <th>CurrentCompanyType</th>\n",
              "      <th>DepartmentInCompany</th>\n",
              "      <th>TotalLeavesTaken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmpID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5664</th>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23568</th>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21490</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8363</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6165</th>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       LanguageOfCommunication  Age  ...  DepartmentInCompany  TotalLeavesTaken\n",
              "EmpID                                ...                                       \n",
              "5664                         2   11  ...                    3                14\n",
              "23568                        0   11  ...                    2                 4\n",
              "21490                        0    4  ...                    2                14\n",
              "8363                         1   13  ...                    4                13\n",
              "6165                         1   12  ...                    2                 5\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGiUshc-XjLf",
        "outputId": "90d61200-4fa4-473c-8ffb-ba867ae04861"
      },
      "source": [
        "prediction1=rf_regr.predict(data_test)\n",
        "prediction1"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([57.44644047, 54.94387358, 57.26363157, ..., 57.21059514,\n",
              "       57.19731729, 57.22600902])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lng5H7ygYmaP",
        "outputId": "bf982b86-2704-4bc0-baf2-b62e9f0b38fd"
      },
      "source": [
        "prediction2=rf_clf.predict(data_test)\n",
        "prediction2"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GacDJhaiYzmz"
      },
      "source": [
        "submission1=pd.DataFrame(prediction1,index=data_test.index,columns=[\"FitmentPercent\"])"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-4G2RQtbgWO"
      },
      "source": [
        "submission2=pd.DataFrame(prediction2,index=data_test.index,columns=[\"BiasInfluentialFactor\"])"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMaiI27oaaOj"
      },
      "source": [
        "submission=pd.merge(submission2,submission1,on=\"EmpID\",how=\"inner\")"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "VhSAaaEwf5M0",
        "outputId": "8d7d36f0-7040-4e1c-fbd2-b56f02256b04"
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>BiasInfluentialFactor</th>\n",
              "      <th>FitmentPercent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmpID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5664</th>\n",
              "      <td>0</td>\n",
              "      <td>57.446440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23568</th>\n",
              "      <td>0</td>\n",
              "      <td>54.943874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21490</th>\n",
              "      <td>0</td>\n",
              "      <td>57.263632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8363</th>\n",
              "      <td>0</td>\n",
              "      <td>56.653630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6165</th>\n",
              "      <td>0</td>\n",
              "      <td>55.905071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       BiasInfluentialFactor  FitmentPercent\n",
              "EmpID                                       \n",
              "5664                       0       57.446440\n",
              "23568                      0       54.943874\n",
              "21490                      0       57.263632\n",
              "8363                       0       56.653630\n",
              "6165                       0       55.905071"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHjFd3EkgCJb",
        "outputId": "a6aebe0d-91b5-45e2-d6f0-6243848ff111"
      },
      "source": [
        "submission.shape"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8745, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b29NTxNwgWNe"
      },
      "source": [
        "submission.BiasInfluentialFactor=submission.BiasInfluentialFactor.map({1:'YearsOfExperince', \n",
        "                                                 0:' ', \n",
        "                                                 2:'Gender',\n",
        "                                                 3:'DegreeBranch',\n",
        "       4:'CurrentCompanyType',5:'MartialStatus',6:'HighestDegree',7:'EmpScore',\n",
        "       8:'Ethinicity', 9:'LatestDegreeCGPA'})"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "i5qJOra_i1DR",
        "outputId": "a2927b5d-8148-439d-c7c3-f2b343f76db3"
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>BiasInfluentialFactor</th>\n",
              "      <th>FitmentPercent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EmpID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5664</th>\n",
              "      <td></td>\n",
              "      <td>57.446440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23568</th>\n",
              "      <td></td>\n",
              "      <td>54.943874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21490</th>\n",
              "      <td></td>\n",
              "      <td>57.263632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8363</th>\n",
              "      <td></td>\n",
              "      <td>56.653630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6165</th>\n",
              "      <td></td>\n",
              "      <td>55.905071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      BiasInfluentialFactor  FitmentPercent\n",
              "EmpID                                      \n",
              "5664                              57.446440\n",
              "23568                             54.943874\n",
              "21490                             57.263632\n",
              "8363                              56.653630\n",
              "6165                              55.905071"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTKfKYEfqJDC"
      },
      "source": [
        "submission.to_csv(\"/content/submission1\")"
      ],
      "execution_count": 57,
      "outputs": []
    }
  ]
}