amcat.ipynb

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  "metadata": {
    "colab": {
      "name": "amcat.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hS8JuUm5xVrm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPeXiToxxdwA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "employment_stats = pd.read_csv (\"amcatData.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZ9jPoHCDNYG",
        "colab_type": "code",
        "outputId": "1f9d964c-1f76-4ddc-fafa-1c47e03e65d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "employment_stats.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Salary', 'DOJ', 'DOL', 'Designation', 'JobCity', 'Gender', 'DOB',\n",
              "       '10percentage', '10board', '12graduation', '12percentage', '12board',\n",
              "       'CollegeID', 'CollegeTier', 'Degree', 'Specialization', 'collegeGPA',\n",
              "       'CollegeCityID', 'CollegeCityTier', 'CollegeState', 'GraduationYear',\n",
              "       'English', 'Logical', 'Quant', 'Domain', 'ComputerProgramming',\n",
              "       'ElectronicsAndSemicon', 'ComputerScience', 'MechanicalEngg',\n",
              "       'ElectricalEngg', 'TelecomEngg', 'CivilEngg', 'conscientiousness',\n",
              "       'agreeableness', 'extraversion', 'nueroticism',\n",
              "       'openess_to_experience'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJtMJoRuneg7",
        "colab_type": "code",
        "outputId": "5b9b4c84-eb80-43ae-ebce-b33ab90dff6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        }
      },
      "source": [
        "employment_stats.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>ID</th>\n",
              "      <th>Salary</th>\n",
              "      <th>DOJ</th>\n",
              "      <th>DOL</th>\n",
              "      <th>Designation</th>\n",
              "      <th>JobCity</th>\n",
              "      <th>Gender</th>\n",
              "      <th>DOB</th>\n",
              "      <th>10percentage</th>\n",
              "      <th>10board</th>\n",
              "      <th>12graduation</th>\n",
              "      <th>12percentage</th>\n",
              "      <th>12board</th>\n",
              "      <th>CollegeID</th>\n",
              "      <th>CollegeTier</th>\n",
              "      <th>Degree</th>\n",
              "      <th>Specialization</th>\n",
              "      <th>collegeGPA</th>\n",
              "      <th>CollegeCityID</th>\n",
              "      <th>CollegeCityTier</th>\n",
              "      <th>CollegeState</th>\n",
              "      <th>GraduationYear</th>\n",
              "      <th>English</th>\n",
              "      <th>Logical</th>\n",
              "      <th>Quant</th>\n",
              "      <th>Domain</th>\n",
              "      <th>ComputerProgramming</th>\n",
              "      <th>ElectronicsAndSemicon</th>\n",
              "      <th>ComputerScience</th>\n",
              "      <th>MechanicalEngg</th>\n",
              "      <th>ElectricalEngg</th>\n",
              "      <th>TelecomEngg</th>\n",
              "      <th>CivilEngg</th>\n",
              "      <th>conscientiousness</th>\n",
              "      <th>agreeableness</th>\n",
              "      <th>extraversion</th>\n",
              "      <th>nueroticism</th>\n",
              "      <th>openess_to_experience</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>203097</td>\n",
              "      <td>420000</td>\n",
              "      <td>01-06-2012 00:00</td>\n",
              "      <td>present</td>\n",
              "      <td>senior quality engineer</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>f</td>\n",
              "      <td>19-02-1990 00:00</td>\n",
              "      <td>84.3</td>\n",
              "      <td>board ofsecondary education,ap</td>\n",
              "      <td>2007</td>\n",
              "      <td>95.8</td>\n",
              "      <td>board of intermediate education,ap</td>\n",
              "      <td>1141</td>\n",
              "      <td>2</td>\n",
              "      <td>B.Tech/B.E.</td>\n",
              "      <td>computer engineering</td>\n",
              "      <td>78.00</td>\n",
              "      <td>1141</td>\n",
              "      <td>0</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>2011</td>\n",
              "      <td>515</td>\n",
              "      <td>585</td>\n",
              "      <td>525</td>\n",
              "      <td>0.635979</td>\n",
              "      <td>445</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.9737</td>\n",
              "      <td>0.8128</td>\n",
              "      <td>0.5269</td>\n",
              "      <td>1.35490</td>\n",
              "      <td>-0.4455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>579905</td>\n",
              "      <td>500000</td>\n",
              "      <td>01-09-2013 00:00</td>\n",
              "      <td>present</td>\n",
              "      <td>assistant manager</td>\n",
              "      <td>Indore</td>\n",
              "      <td>m</td>\n",
              "      <td>04-10-1989 00:00</td>\n",
              "      <td>85.4</td>\n",
              "      <td>cbse</td>\n",
              "      <td>2007</td>\n",
              "      <td>85.0</td>\n",
              "      <td>cbse</td>\n",
              "      <td>5807</td>\n",
              "      <td>2</td>\n",
              "      <td>B.Tech/B.E.</td>\n",
              "      <td>electronics and communication engineering</td>\n",
              "      <td>70.06</td>\n",
              "      <td>5807</td>\n",
              "      <td>0</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>2012</td>\n",
              "      <td>695</td>\n",
              "      <td>610</td>\n",
              "      <td>780</td>\n",
              "      <td>0.960603</td>\n",
              "      <td>-1</td>\n",
              "      <td>466</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.7335</td>\n",
              "      <td>0.3789</td>\n",
              "      <td>1.2396</td>\n",
              "      <td>-0.10760</td>\n",
              "      <td>0.8637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>810601</td>\n",
              "      <td>325000</td>\n",
              "      <td>01-06-2014 00:00</td>\n",
              "      <td>present</td>\n",
              "      <td>systems engineer</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>f</td>\n",
              "      <td>03-08-1992 00:00</td>\n",
              "      <td>85.0</td>\n",
              "      <td>cbse</td>\n",
              "      <td>2010</td>\n",
              "      <td>68.2</td>\n",
              "      <td>cbse</td>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>B.Tech/B.E.</td>\n",
              "      <td>information technology</td>\n",
              "      <td>70.00</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>2014</td>\n",
              "      <td>615</td>\n",
              "      <td>545</td>\n",
              "      <td>370</td>\n",
              "      <td>0.450877</td>\n",
              "      <td>395</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.2718</td>\n",
              "      <td>1.7109</td>\n",
              "      <td>0.1637</td>\n",
              "      <td>-0.86820</td>\n",
              "      <td>0.6721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>267447</td>\n",
              "      <td>1100000</td>\n",
              "      <td>01-07-2011 00:00</td>\n",
              "      <td>present</td>\n",
              "      <td>senior software engineer</td>\n",
              "      <td>Gurgaon</td>\n",
              "      <td>m</td>\n",
              "      <td>05-12-1989 00:00</td>\n",
              "      <td>85.6</td>\n",
              "      <td>cbse</td>\n",
              "      <td>2007</td>\n",
              "      <td>83.6</td>\n",
              "      <td>cbse</td>\n",
              "      <td>6920</td>\n",
              "      <td>1</td>\n",
              "      <td>B.Tech/B.E.</td>\n",
              "      <td>computer engineering</td>\n",
              "      <td>74.64</td>\n",
              "      <td>6920</td>\n",
              "      <td>1</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2011</td>\n",
              "      <td>635</td>\n",
              "      <td>585</td>\n",
              "      <td>625</td>\n",
              "      <td>0.974396</td>\n",
              "      <td>615</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.0464</td>\n",
              "      <td>0.3448</td>\n",
              "      <td>-0.3440</td>\n",
              "      <td>-0.40780</td>\n",
              "      <td>-0.9194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>343523</td>\n",
              "      <td>200000</td>\n",
              "      <td>01-03-2014 00:00</td>\n",
              "      <td>01-03-2015 00:00</td>\n",
              "      <td>get</td>\n",
              "      <td>Manesar</td>\n",
              "      <td>m</td>\n",
              "      <td>27-02-1991 00:00</td>\n",
              "      <td>78.0</td>\n",
              "      <td>cbse</td>\n",
              "      <td>2008</td>\n",
              "      <td>76.8</td>\n",
              "      <td>cbse</td>\n",
              "      <td>11368</td>\n",
              "      <td>2</td>\n",
              "      <td>B.Tech/B.E.</td>\n",
              "      <td>electronics and communication engineering</td>\n",
              "      <td>73.90</td>\n",
              "      <td>11368</td>\n",
              "      <td>0</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>2012</td>\n",
              "      <td>545</td>\n",
              "      <td>625</td>\n",
              "      <td>465</td>\n",
              "      <td>0.124502</td>\n",
              "      <td>-1</td>\n",
              "      <td>233</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.8810</td>\n",
              "      <td>-0.2793</td>\n",
              "      <td>-1.0697</td>\n",
              "      <td>0.09163</td>\n",
              "      <td>-0.1295</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ID   Salary  ... nueroticism openess_to_experience\n",
              "0  203097   420000  ...     1.35490               -0.4455\n",
              "1  579905   500000  ...    -0.10760                0.8637\n",
              "2  810601   325000  ...    -0.86820                0.6721\n",
              "3  267447  1100000  ...    -0.40780               -0.9194\n",
              "4  343523   200000  ...     0.09163               -0.1295\n",
              "\n",
              "[5 rows x 38 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPJmSrhBFDka",
        "colab_type": "code",
        "outputId": "a7b4b4fe-d8db-42c7-c62d-4c2386ef2fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "non_numeric_cols = []\n",
        "for column in employment_stats.columns:\n",
        "  if (employment_stats [column].dtype == 'object'):\n",
        "    non_numeric_cols.append (column)\n",
        "    print(\"{:15}{:15}\".format(column,(employment_stats.groupby(column).size()).shape[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DOJ                         81\n",
            "DOL                         67\n",
            "Designation                419\n",
            "JobCity                    339\n",
            "Gender                       2\n",
            "DOB                       1872\n",
            "10board                    275\n",
            "12board                    340\n",
            "Degree                       4\n",
            "Specialization              46\n",
            "CollegeState                26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRkHSyVsD3wt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "employment_stats = employment_stats.drop (\"DOB\", axis = 1)\n",
        "employment_stats = employment_stats.drop (\"DOL\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"ID\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"Designation\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"DOJ\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"CollegeID\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"CollegeCityID\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"CollegeCityTier\", axis = 1)\n",
        "non_numeric_cols.remove(\"DOB\")\n",
        "non_numeric_cols.remove(\"DOL\")\n",
        "non_numeric_cols.remove(\"DOJ\")\n",
        "non_numeric_cols.remove(\"Designation\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0bR8hZ2FYDQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "description_table = employment_stats.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gpSgjmKjl89",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "employment_stats[\"TechnicalScore\"] = 0\n",
        "employment_stats[\"TechnicalStrengthCount\"] = 0\n",
        "employment_stats[\"MajorTechnicalSubject\"] = \"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZVTSSuEhI4b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "technical_subjects = [\"ComputerProgramming\", \"ElectronicsAndSemicon\", \"ComputerScience\", \"MechanicalEngg\", \"ElectricalEngg\", \"TelecomEngg\", \"CivilEngg\"]\n",
        "for index, row in employment_stats.iterrows():\n",
        "  technical_score = [row[\"ComputerProgramming\"], row[\"ElectronicsAndSemicon\"], row[\"ComputerScience\"], row[\"MechanicalEngg\"], row[\"ElectricalEngg\"], row[\"TelecomEngg\"], row[\"CivilEngg\"]]\n",
        "  employment_stats.at[index, \"TechnicalScore\"] = max(technical_score)\n",
        "  employment_stats.at[index, \"TechnicalStrengthCount\"] = sum(x > 0 for x in technical_score)\n",
        "  employment_stats.at[index, \"MajorTechnicalSubject\"] = technical_subjects[technical_score.index(max(technical_score))]\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYNNCruXiXTL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "employment_stats = employment_stats.drop(\"Specialization\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"ComputerScience\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"ComputerProgramming\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"ElectronicsAndSemicon\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"MechanicalEngg\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"ElectricalEngg\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"TelecomEngg\", axis = 1)\n",
        "employment_stats = employment_stats.drop(\"CivilEngg\", axis = 1)\n",
        "non_numeric_cols.remove(\"Specialization\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbF6E6JvwiNL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IT_CITY = 1\n",
        "NON_IT_CITY = 2\n",
        "it_hubs = [\"bangalore\", \"gurgaon\", \"noida\", \"pune\", \"mumbai\", \"delhi\", \"new delhi\", \"navi mumbai\", \"hyderabad\", \"ncr\", \"chennai\"]\n",
        "for index,row in employment_stats.iterrows():\n",
        "  if row[\"JobCity\"].lower() in it_hubs:\n",
        "    employment_stats.at[index, \"JobCity\"] = IT_CITY\n",
        "  else:\n",
        "    employment_stats.at[index, \"JobCity\"] = NON_IT_CITY\n",
        "non_numeric_cols.remove(\"JobCity\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQb4vhkskxXh",
        "colab_type": "code",
        "outputId": "e4b43c7f-8aa1-4fd6-d97e-78610d514d12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "employment_stats.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Salary', 'JobCity', 'Gender', '10percentage', '10board',\n",
              "       '12graduation', '12percentage', '12board', 'CollegeTier', 'Degree',\n",
              "       'collegeGPA', 'CollegeState', 'GraduationYear', 'English', 'Logical',\n",
              "       'Quant', 'Domain', 'conscientiousness', 'agreeableness', 'extraversion',\n",
              "       'nueroticism', 'openess_to_experience', 'TechnicalScore',\n",
              "       'TechnicalStrengthCount', 'MajorTechnicalSubject'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3de0IdMMBDE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "CBSE = 1\n",
        "ICSE = 2\n",
        "STATE_BOARD = 3\n",
        "UNAVAILABLE = 4\n",
        "for index, row in employment_stats.iterrows():\n",
        "  if row[\"10board\"].lower() == \"cbse\" or row[\"10board\"].lower() == \"central board of secondary education\":\n",
        "    employment_stats.at[index, \"10board\"] = CBSE\n",
        "  elif row[\"10board\"].lower() == \"icse\" or row[\"10board\"].lower() == \"isc\":\n",
        "    employment_stats.at[index, \"10board\"] = ICSE\n",
        "  elif row[\"10board\"] == '':\n",
        "    employment_stats.at[index, \"10board\"] = UNAVAILABLE\n",
        "  else:\n",
        "    employment_stats.at[index, \"10board\"] = STATE_BOARD\n",
        "  if row[\"12board\"].lower() == \"cbse\" or row[\"12board\"].lower() == \"central board of secondary education\":\n",
        "    employment_stats.at[index, \"12board\"] = CBSE\n",
        "  elif row[\"12board\"].lower() == \"icse\" or row[\"12board\"].lower() == \"isc\":\n",
        "    employment_stats.at[index, \"12board\"] = ICSE\n",
        "  elif row[\"12board\"] == '':\n",
        "    employment_stats.at[index, \"12board\"] = UNAVAILABLE\n",
        "  else:\n",
        "    employment_stats.at[index, \"12board\"] = STATE_BOARD"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOVTcj2dOK9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "non_numeric_cols.remove(\"10board\")\n",
        "non_numeric_cols.remove(\"12board\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKz1r9RJOPC_",
        "colab_type": "code",
        "outputId": "62a6ce33-933b-4fbd-e704-30522b52393a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "employment_stats.groupby(\"Degree\").size()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Degree\n",
              "B.Tech/B.E.      3700\n",
              "M.Sc. (Tech.)       2\n",
              "M.Tech./M.E.       53\n",
              "MCA               243\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRpEFkKEmOpe",
        "colab_type": "code",
        "outputId": "6c73e0cf-fa0d-4450-ecae-d99158b6ecf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "employment_stats.groupby(\"Gender\").size()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "f     957\n",
              "m    3041\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awghIW15logo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXXSXffHRt_i",
        "colab_type": "code",
        "outputId": "120e736d-eb56-42f8-eb50-81d1a17be10d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "for col_name in employment_stats.columns:\n",
        "    if(employment_stats[col_name].dtype == 'object'):\n",
        "        print(col_name)\n",
        "        employment_stats[col_name]= employment_stats[col_name].astype('category')\n",
        "        employment_stats[col_name] = employment_stats[col_name].cat.codes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "JobCity\n",
            "Gender\n",
            "10board\n",
            "12board\n",
            "Degree\n",
            "CollegeState\n",
            "MajorTechnicalSubject\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Eyc7qlaRzJa",
        "colab_type": "code",
        "outputId": "b19ff72d-f65f-47e8-f577-efd4bf555ab1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "\n",
        "minOutliers = []\n",
        "maxOutliers = []\n",
        "outlierFeatures = []\n",
        "descriptionTable = employment_stats.describe()\n",
        "for i in employment_stats.columns:\n",
        "  if(descriptionTable[i][6] != descriptionTable[i][4]):\n",
        "    outlierFeatures.append(i)\n",
        "    interQuartile = ((descriptionTable[i][6] - descriptionTable[i][4]) * 1.5)\n",
        "    minOutliers.append((descriptionTable[i][4]) - interQuartile)\n",
        "    maxOutliers.append((descriptionTable[i][6]) + interQuartile)\n",
        "print(outlierFeatures)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Salary', 'JobCity', '10percentage', '10board', '12graduation', '12percentage', '12board', 'collegeGPA', 'CollegeState', 'GraduationYear', 'English', 'Logical', 'Quant', 'Domain', 'conscientiousness', 'agreeableness', 'extraversion', 'nueroticism', 'openess_to_experience', 'TechnicalScore', 'TechnicalStrengthCount', 'MajorTechnicalSubject']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFFPZVkbrjPz",
        "colab_type": "code",
        "outputId": "7b255b73-86b7-49f6-df1e-690b4b144c4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plt.hist(employment_stats['Salary'], bins = 30, edgecolor = \"black\")\n",
        "plt.xticks([0, 500000, 1000000, 1500000, 2000000, 2500000, 3000000, 3500000, 4000000, 4500000], [0, 5, 10, 15, 20, 25, 30, 35, 40, 45])\n",
        "plt.xlabel(\"Salary (value in lakhs)\")\n",
        "plt.ylabel(\"Number of candidates\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of candidates')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eqiaRiNgIPCTJszyZmdkbFOlY3xpYIekO4Lnewog4vG5RmZlZWyiSRL5e9yjMzKwtFXlP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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZ8X3528Vfri",
        "colab_type": "code",
        "outputId": "e50167ed-23fa-483f-e0ad-d6e6265d8a79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "for i in range(len(outlierFeatures)):\n",
        "    employment_stats = employment_stats [employment_stats [outlierFeatures[i]] >= minOutliers[i]]\n",
        "    employment_stats = employment_stats [employment_stats [outlierFeatures[i]] <= maxOutliers[i]]\n",
        "    print(outlierFeatures[i], minOutliers[i], maxOutliers[i])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Salary -105000.0 655000.0\n",
            "JobCity -1.5 2.5\n",
            "10percentage 50.69499999999998 106.65500000000002\n",
            "10board -3.0 5.0\n",
            "12graduation 2004.0 2012.0\n",
            "12percentage 41.10000000000001 107.49999999999999\n",
            "12board -3.0 5.0\n",
            "collegeGPA 51.527499999999996 91.20750000000001\n",
            "CollegeState -7.0 41.0\n",
            "GraduationYear 2009.0 2017.0\n",
            "English 207.5 787.5\n",
            "Logical 265.0 745.0\n",
            "Quant 182.5 842.5\n",
            "Domain -0.4075852329999999 1.5921484549999998\n",
            "conscientiousness -2.8378625000000004 2.8270375000000003\n",
            "agreeableness -1.93695 2.46265\n",
            "extraversion -2.5200000000000005 2.5872\n",
            "nueroticism -2.9598 2.6178\n",
            "openess_to_experience -2.4266 2.2598000000000003\n",
            "TechnicalScore 180.0 700.0\n",
            "TechnicalStrengthCount -0.5 3.5\n",
            "MajorTechnicalSubject -2.0 6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmYjL_l1WGaE",
        "colab_type": "code",
        "outputId": "5943a0c4-34e9-4a36-82d3-213c1ec72a65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "employment_stats.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3276, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2__f4Q0dyPe",
        "colab_type": "code",
        "outputId": "057ceecc-c6ad-42ab-aebd-ca592cd3f5fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "source": [
        "BELOW_AVG_SALARY = 0\n",
        "ABOVE_AVG_SALARY = 1\n",
        "AVG_SAL = 300000\n",
        "salaries = [] \n",
        "female_salary = []\n",
        "male_salary = []\n",
        "for index,row in employment_stats.iterrows():\n",
        "  salaries.append(row['Salary'])\n",
        "  if row['Gender'] == 0:\n",
        "    female_salary.append(row['Salary'])\n",
        "  else:\n",
        "    male_salary.append(row['Salary'])\n",
        "  if row['Salary'] <= AVG_SAL:\n",
        "    employment_stats.at[index, \"Salary\"] = BELOW_AVG_SALARY\n",
        "  else:\n",
        "    employment_stats.at[index, \"Salary\"] = ABOVE_AVG_SALARY\n",
        "print(employment_stats.groupby(\"Salary\").size())\n",
        "plt.hist(salaries, bins = 12, edgecolor = \"black\", linewidth = 1)\n",
        "plt.xlabel(\"Salaries (value in lakhs)\")\n",
        "locs, labels = plt.xticks()\n",
        "plt.xticks(locs, [int(i//100000) for i in locs])\n",
        "plt.ylabel(\"Number of Candidates\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Salary\n",
            "0    1795\n",
            "1    1481\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Candidates')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3KXsDz3TnOmYDggfjWR8pMpvrH9MiQfeSNS39JiK6bGKS1AK8FhHPSdoDOJbswfOdwKnA\ndcBU4OZ0yPy0/6v0+c+LXMdswPFgPOsjRZYcPQH4V7LnBwLGSjo7In7axaGtwBxJg8juVOZGxK2S\nHgWuk/RlspXpZqf6s4EfSFoJ/B74aI9+IjMz6xVFBspdBhwTESsBJL0VuA2omyAiYjlwWE75KrLn\nER3LXwZOKxCPmZn1gSLPILa0J4dkFbClpHjMzKxBdHoHIenDaXOJpAXAXLJnEKeRjaY2M7N+rF4T\n00k1208Bf5m2NwN7lBaRmZk1hE4TRESc1ZeBmJlZYynSi2ks8GlgTG19T/dtZta/FenFdBNZF9Rb\ngD+WG46ZmTWKIgni5Yi4ovRIzMysoRRJEJdLugi4g2wKbwAioss1IczMrHkVSRAHAx8jm6a7vYmp\nfdpuMzPrp4okiNOAt9RO+W1mZv1fkZHUDwPDyg7EzMwaS5E7iGHA45LuY8dnEO7mambWjxVJEBeV\nHoWZmTWcIutB3N0XgZiZWWMpMpJ6C9vXoB4CDAa2RsSbygzMzMyqVeQOYq/2bUkCJgMTygzKzMyq\nV6QX0xsicxPwoZLiMTOzBlGkienDNbu7AG3Ay6VFZGZmDaFIL6badSFeB1aTNTOZmVk/VuQZRI/W\nhZA0CrgaGEH2kHtWRFwuaThwPdn04auBKRHxbHq+cTlwPPAS8HHP92RmVp16S45eWOe4iIhLujj3\n68DfR8T9kvYClkpaCHwcWBQRX5U0E5gJzAAmAePS6z3AlendzMwqUO8h9dacF8A0sj/odUXEhvY7\ngIjYAjwG7E/WPDUnVZsDnJK2JwNXpwfhi4Fhklq79+OYmVlvqbfk6GXt2+kO4DzgLOA64LLOjssj\naQxwGHAvMCIiNqSPNpI1QUGWPNbWHLYulW2oKUPSdGA6wOjRo7sThpmZdUPdbq6Shkv6MrCcLJmM\nj4gZEbGp6AUk7Qn8GDg/Il6o/Swigu2D8AqJiFkR0RYRbS0tLd051MzMuqHTBCHpH4H7gC3AwRFx\ncUQ8252TSxpMlhyuiYifpOKn2puO0nt7slkPjKo5fGQqMzOzCtS7g/h74M+BC4DfSXohvbZIeqHO\nccAbo65nA49FxNdrPpoPTE3bU4Gba8rPVGYC8HxNU5SZmfWxes8gujXKOseRZCvRPSRpWSr7AvBV\nYK6kacAaYEr6bAFZF9eVZN1ce9S91szMekeRgXI9EhG/BNTJxxNz6gdwTlnxmJlZ9+zsXYKZmfVT\npd1BmHXboMFkj67MrBE4QVjj2PYaB8y4tbTTr7n0xNLObdYfuYnJzMxyOUGYmVkuJwgzM8vlBGFm\nZrmcIMzMLJcThJmZ5XKCMLM/lcaklPVqHemp+puBx0GY2Z/ymBTDdxBmZtYJJwgzM8vlBGFmfa/k\nZxx+ztE7/AzCzPpeyc84wM85eoPvIMzMLJcThJmZ5XKCMDOzXKUlCEnflbRJ0sM1ZcMlLZS0Ir3v\nk8ol6QpJKyUtlzS+rLjMzKyYMu8gvg8c16FsJrAoIsYBi9I+wCRgXHpNB64sMS4zMyugtAQREfcA\nv+9QPBmYk7bnAKfUlF8dmcXAMEmtZcVmZmZd6+tnECMiYkPa3giMSNv7A2tr6q1LZWZmVpHKHlJH\nRADR3eMkTZe0RNKSzZs3lxCZmZlB3yeIp9qbjtL7plS+HhhVU29kKvsTETErItoioq2lpaXUYM3M\nBrK+ThDzgalpeypwc035mak30wTg+ZqmKDMzq0BpU21IuhY4GthP0jrgIuCrwFxJ04A1wJRUfQFw\nPLASeAk4q6y4zMysmNISRESc3slHE3PqBnBOWbGYmVn3eSS1mZnlcoIwM7NcThBmZpbLCcLMzHI5\nQZiZWS4nCDMzy+UEYWb9k9e93mlek9rM+ieve73TBvwdROvI0aV/yzAza0YD/g5i4/q1/pZhZpZj\nwN9BmJlZPicIMzPL5QRhZma5nCDMzHqq5K60VXejHfAPqc3MeqzkrrRVd3DxHYSZmeVygjAzs1xO\nEGZmlssJwszMcjVUgpB0nKTfSFopaWbV8ZiZDWQNkyAkDQL+GZgEHAScLumgaqMyMxu4GiZBAEcA\nKyNiVUS8ClwHTK44JjOzAauREsT+wNqa/XWpzMzMKqCIqDoGACSdChwXEZ9I+x8D3hMR53aoNx2Y\nnnbfCTzcp4H2rv2Ap6sOYic4/uo0c+zg+Kt2YETs1VWlRhpJvR4YVbM/MpXtICJmAbMAJC2JiLa+\nCa/3Of5qNXP8zRw7OP6qSVpSpF4jNTHdB4yTNFbSEOCjwPyKYzIzG7Aa5g4iIl6XdC7wM2AQ8N2I\neKTisMzMBqyGSRAAEbEAWNCNQ2aVFUsfcfzVaub4mzl2cPxVKxR/wzykNjOzxtJIzyDMzKyBNG2C\naOZpOSR9V9ImSU3XRVfSKEl3SnpU0iOSzqs6pu6QtLukX0t6MMX/papj6glJgyQ9IKm8xQhKImm1\npIckLSvam6aRSBomaZ6kxyU9Jum9VcdUlKQD0++9/fWCpPM7rd+MTUxpWo4ngGPJBtTdB5weEY9W\nGlhBko4CXgSujoh3Vh1Pd0hqBVoj4n5JewFLgVOa6HcvYGhEvChpMPBL4LyIWFxxaN0i6TNAG/Cm\niKh2VZlukrQaaIuIphxHIGkO8IuIuCr1uPyziHiu6ri6K/0dXU823mxNXp1mvYNo6mk5IuIe4PdV\nx9ETEbEhIu5P21uAx2iiEe+ReTHtDk6vpvqWJGkkcAJwVdWxDDSS9gaOAmYDRMSrzZgckonAk50l\nB2jeBOFpORqApDHAYcC91UbSPal5ZhmwCVgYEU0VP/BN4HPAH6sOpIcCuEPS0jQzQjMZC2wGvpea\n+K6SNLTqoHroo8C19So0a4KwiknaE/gxcH5EvFB1PN0REdsi4lCy0fpHSGqaZj5JJwKbImJp1bHs\nhPdHxHiymZvPSU2uzWJXYDxwZUQcBmwFmuoZKEBqGjsZuKFevWZNEIWm5bBypLb7HwPXRMRPqo6n\np1LTwJ3AcVXH0g1HAiendvzrgA9K+mG1IXVPRKxP75uAG8majJvFOmBdzV3nPLKE0WwmAfdHxFP1\nKjVrgvC0HBVJD3lnA49FxNerjqe7JLVIGpa29yDr6PB4tVEVFxGfj4iRETGG7N/9zyPibysOqzBJ\nQ1PnBlLTzF/RRBNuRsRGYK2kA1PRRKApOmh0cDpdNC9Bg42kLqrZp+WQdC1wNLCfpHXARRExu9qo\nCjsS+BjwUGrHB/hCGgXfDFqBOakHxy7A3Ihouq6iTWwEcGP2PYNdgR9FxO3VhtRtnwauSV9OVwFn\nVRxPt6TEfCxwdpd1m7Gbq5mZla9Zm5jMzKxkThBmZpbLCcLMzHI5QZiZWS4nCDMzy+UEYWZmuZwg\nrE9J+oc0zfbyNN3we7qo/31Jp3bzGp+SdObORZrNXNvT6bQlHd1bU3Gn+X4O6kb9j0v6dhd1Lpb0\n2ZzyuyS1FbzOEEn3SGrK8VTWNf+HtT6T5s0/ERgfEa9I2g8Y0svX2DUi/rWXTvcZ4N966Vw9FhGf\nqDqGPBHxqqRFwN8A11Qdj/U+30FYX2oFno6IVwAi4umI+B2ApAsl3SfpYUmz0pQeO+isTvrW+820\n+Mx5td+OJb1V0u1p5tBfSHp7Kj8tnedBSfd0Eu9HgNtT/cWS/qImlrsktUk6QtKv0sye/1kzBUNt\n3Dt8W0/XHZO2/1bZAkbLJH0njfDuePwb3+olvSjp/6a4F0saUe8XLukkSfem+P49r76kT0r6aZp6\nBOC0FNMTkj6Q6vxFTZzLJY1LdW8CzqgXgzUvJwjrS3cAo9Ifnn+R9Jc1n307It6dFlDag+xOo6N6\ndYZERFtEXNbhmFnApyPicOCzwL+k8guBD0XEIWSzWu5A0ljg2fZkBlwPTEmftS+atIRsHqcPpJk9\nLwT+X8HfBZLeQfbt+8g0u+w2uv5jOxRYnOK+B/hkF/V/CUxI8V1HNk14bQznkv0eT4mIP6TiXSPi\nCOB84KJU9ing8hRnG9mkdZDNo/TuLmKwJuUmJuszaRW3w4EPAMcA10uaGRHfB46R9Dngz4DhwCPA\nLR1OUa/O9R2vp2xK8vcBN9TckOyW3v8D+L6kuUDejLStZPP+t5tLluAuIksU81L53mRzO40jW+dg\ncBe/hloTgcOB+1J8e5CtUVHPq0D7s42lZHPq1DOS7PfcStac99uaz84kW1fllIh4raa8/fexFBiT\ntn8F/IOyxYp+EhErIJs6XdKrkvZKC0hZP+I7COtTaS2GuyLiIuBc4COSdif7Zn9qRBxM1u6/e+1x\nBepszbncLsBzEXFozesdKY5PAReQTRu/VNK+HY79Q+350xTVz0h6F9m3/vaEdAlwZ7qrOalj3Mnr\n7Pj/WnsdAXNqYjswIi7OOb7Wa7F9ArVtdP0l71tkd14Hk03OVhvfQ2QJYGSHY9rvmt44f0T8iOxO\n6w/AAkkfrKm/G/ByF3FYE3KCsD6jbMH0cTVFhwJr2P5H6+n0rT+v11KROjtICxn9VtJp6fqSdEja\nfmtE3BsRF5LdKYzqcPgTbP/23O56siaavSNieSrbm+1rkXy8k1BWk9YMkDSebFUygEXAqZLenD4b\nLumArn6ubqqNb2qHzx4gSxrzJf15vZNIeguwKiKuAG4G3pXK9yV7rvRaveOtOTlBWF/ak6w55lFJ\ny4GDgIvTwj3/Rtae/TOy9T52UKROJ84Apkl6kKxJqn3t8n+U9JCkh4H/BB7scL2twJOS3lZTPI9s\nDYa5NWVfA74i6QE6/zb/Y2C4pEfI7pqeSNd4lOwu5o70+1hI1rTVmy4ma2JbCjzd8cOI+CXZs5nb\nUq+yzkwBHlY2xfs7gatT+THAbb0asTUMT/dt1glJfw0cHhEXVB1Lo5L0E2BmRDxRdSzW+/yQ2qwT\nEXFjzrMJS5QtmHOTk0P/5TsIMzPL5WcQZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrn+PyT6stzr\nB+iBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XJXUFA-WwIf",
        "colab_type": "code",
        "outputId": "7e660dfd-1dda-410d-aa09-133a8a6014fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "employment_stats.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Salary', 'JobCity', 'Gender', '10percentage', '10board',\n",
              "       '12graduation', '12percentage', '12board', 'CollegeTier', 'Degree',\n",
              "       'collegeGPA', 'CollegeState', 'GraduationYear', 'English', 'Logical',\n",
              "       'Quant', 'Domain', 'conscientiousness', 'agreeableness', 'extraversion',\n",
              "       'nueroticism', 'openess_to_experience', 'TechnicalScore',\n",
              "       'TechnicalStrengthCount', 'MajorTechnicalSubject'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O0UlLVXXjhJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "employment_stats = employment_stats[employment_stats[\"Degree\"] != \"M.Sc. (Tech.)\"]    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjC1suyTiWej",
        "colab_type": "code",
        "outputId": "6b9fa3ff-dd1a-4147-c542-507dcc769d6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plt.hist([female_salary, male_salary], histtype='bar', stacked=True, edgecolor = \"black\")\n",
        "plt.legend(\"FM\")\n",
        "plt.xlabel(\"Salaries (value in lakhs)\")\n",
        "locs, labels = plt.xticks()\n",
        "plt.xticks(locs, [int(i//100000) for i in locs])\n",
        "plt.ylabel(\"Number of Candidates\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Candidates')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lHwQuTXG2kU1aB9k8Sq/rIQZrUG5isrpJq7gdDfwt8CbgakkLIuJbwJskfQzY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\nd9aR4Jdpy+8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1z7UoTmcLx7T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPPdDvWUoZVv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "modelList = []\n",
        "modelList.append(('KNN', KNeighborsClassifier()))\n",
        "modelList.append(('SVC', SVC()))\n",
        "modelList.append(('LR', LogisticRegression()))\n",
        "modelList.append(('DT', DecisionTreeClassifier()))\n",
        "modelList.append(('GNB', GaussianNB()))\n",
        "modelList.append(('RFC', RandomForestClassifier()))\n",
        "modelList.append(('GB', GradientBoostingClassifier()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBN2cUXHXDfB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "input_feature = ['JobCity', 'Gender', '10percentage', '10board', '12graduation',\n",
        "       '12percentage', '12board', 'CollegeTier', 'Degree',\n",
        "       'collegeGPA', 'CollegeState',\n",
        "       'GraduationYear', 'English', 'Logical', 'Quant', 'Domain',\n",
        "       'conscientiousness', 'agreeableness', 'extraversion', 'nueroticism',\n",
        "       'openess_to_experience', 'TechnicalScore', 'TechnicalStrengthCount',  \n",
        "       'MajorTechnicalSubject']\n",
        "X = employment_stats[input_feature]\n",
        "Y = employment_stats.Salary\n",
        "X, X_VAL, Y, Y_VAL = train_test_split(X, Y, stratify = employment_stats.Salary, train_size = 0.9, random_state = 0)\n",
        "X = np.array(X)\n",
        "Y = np.array(Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwKyd7T6XXnI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "import warnings"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P5InTT_LXbSc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "k_folds = StratifiedKFold(n_splits = 9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HOOwSVYrZUUj",
        "colab_type": "code",
        "outputId": "f33c63a6-61e0-480f-b468-17b06f0bd80a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "warnings.simplefilter('ignore')\n",
        "names = []\n",
        "score = []\n",
        "score_on_validation = []\n",
        "for name, model in modelList:\n",
        "  names.append(name)\n",
        "  accuracy = []\n",
        "  for train_index, test_index in k_folds.split(X, Y):\n",
        "    X_train, X_test = X[train_index], X[test_index]\n",
        "    Y_train, Y_test =  Y[train_index], Y[test_index]\n",
        "    model.fit(X_train, Y_train)\n",
        "    accuracy.append(model.score(X_test, Y_test))\n",
        "  score.append(sum(accuracy) / len(accuracy))\n",
        "  score_on_validation.append(model.score(X_VAL, Y_VAL))\n",
        "table_testdata = pd.DataFrame({'Name ': names, 'Score': score})\n",
        "table_validation = pd.DataFrame({'Name ': names, 'Score': score_on_validation})\n",
        "print(\"\\n Accuracy for test data \\n\")\n",
        "print(table_testdata)\n",
        "print(\"\\n Accuracy on validation data \\n\")\n",
        "print(table_validation)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Accuracy for test data \n",
            "\n",
            "  Name      Score\n",
            "0   KNN  0.615322\n",
            "1   SVC  0.547829\n",
            "2    LR  0.698769\n",
            "3    DT  0.602097\n",
            "4   GNB  0.694363\n",
            "5   RFC  0.675041\n",
            "6    GB  0.709976\n",
            "\n",
            " Accuracy on validation data \n",
            "\n",
            "  Name      Score\n",
            "0   KNN  0.640244\n",
            "1   SVC  0.548780\n",
            "2    LR  0.695122\n",
            "3    DT  0.615854\n",
            "4   GNB  0.689024\n",
            "5   RFC  0.698171\n",
            "6    GB  0.725610\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7pDuSFIZlen",
        "colab_type": "code",
        "outputId": "f6b8801a-cae0-487f-f050-80bf9ea82722",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plt.xlabel(\"Algorithm\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.plot(names, score_on_validation)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f961bb42240>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ubofSIS44sS95A3ryq4XrOVBd1/oBbfTQf9ZRdaSOB78yiqgou2fEhA5/+khe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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6LVevp38PEk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}