SalaryPredictionEDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary Prediction EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining Problem\n",
    "Salary is important for job seekers, it's also crucial for employeer to offer a competetive salary for the right candidate. Generally, salary depends on various factor, such as jobtitle, degree, years of experiences, type of company and major. In this project, we will predict the salary based on the job descriptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic data analysis tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic data analysis tools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import sklearn as sk\n",
    "# Personal Information\n",
    "__author__ = \"Emrul Hasan\"\n",
    "__email__ = \"emrul.phy@gmail.com\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load available datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load all the datasets available into Pandas dataframe\n",
    "train_data=pd.read_csv('train_features.csv')\n",
    "test_data=pd.read_csv('test_features.csv')\n",
    "salary_data=pd.read_csv('train_salaries.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are total of three datasets. They are train data, test data and salary or the target data. In this notebook, for exploratory data analysis, we will be playing with train and target data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JOB1362684407687</td>\n",
       "      <td>COMP37</td>\n",
       "      <td>CFO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JOB1362684407688</td>\n",
       "      <td>COMP19</td>\n",
       "      <td>CEO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>WEB</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JOB1362684407689</td>\n",
       "      <td>COMP52</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOB1362684407690</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOB1362684407691</td>\n",
       "      <td>COMP7</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId         jobType       degree      major  \\\n",
       "0  JOB1362684407687    COMP37             CFO      MASTERS       MATH   \n",
       "1  JOB1362684407688    COMP19             CEO  HIGH_SCHOOL       NONE   \n",
       "2  JOB1362684407689    COMP52  VICE_PRESIDENT     DOCTORAL    PHYSICS   \n",
       "3  JOB1362684407690    COMP38         MANAGER     DOCTORAL  CHEMISTRY   \n",
       "4  JOB1362684407691     COMP7  VICE_PRESIDENT    BACHELORS    PHYSICS   \n",
       "\n",
       "  industry  yearsExperience  milesFromMetropolis  \n",
       "0   HEALTH               10                   83  \n",
       "1      WEB                3                   73  \n",
       "2   HEALTH               10                   38  \n",
       "3     AUTO                8                   17  \n",
       "4  FINANCE                8                   16  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read first 5 columns of training data\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to print basic information about the data\n",
    "\n",
    "def data_info(df):\n",
    "    \n",
    "\n",
    "    print('Shape of the data: ', df.shape)\n",
    "    \n",
    "    print('------------########################------------------')\n",
    "    print('                                                     ')\n",
    "    print('Information of the data:')\n",
    "    print(' ', df.info())\n",
    "    \n",
    "    print('------------########################------------------')\n",
    "    print('                                                     ')\n",
    "    print('Check the duplication of the data:', df.duplicated().sum())\n",
    "    \n",
    "    print('------------########################------------------')\n",
    "    print('                                                     ')\n",
    "    print('\\nCheck the missing value of the data:\\n', df.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data:  (1000000, 8)\n",
      "------------########################------------------\n",
      "                                                     \n",
      "Information of the data:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000000 entries, 0 to 999999\n",
      "Data columns (total 8 columns):\n",
      "jobId                  1000000 non-null object\n",
      "companyId              1000000 non-null object\n",
      "jobType                1000000 non-null object\n",
      "degree                 1000000 non-null object\n",
      "major                  1000000 non-null object\n",
      "industry               1000000 non-null object\n",
      "yearsExperience        1000000 non-null int64\n",
      "milesFromMetropolis    1000000 non-null int64\n",
      "dtypes: int64(2), object(6)\n",
      "memory usage: 61.0+ MB\n",
      "  None\n",
      "------------########################------------------\n",
      "                                                     \n",
      "Check the duplication of the data: 0\n",
      "------------########################------------------\n",
      "                                                     \n",
      "\n",
      "Check the missing value of the data:\n",
      " jobId                  0\n",
      "companyId              0\n",
      "jobType                0\n",
      "degree                 0\n",
      "major                  0\n",
      "industry               0\n",
      "yearsExperience        0\n",
      "milesFromMetropolis    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Print the informantion of the training dataset\n",
    "data_info(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the data:  (1000000, 2)\n",
      "------------########################------------------\n",
      "                                                     \n",
      "Information of the data:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000000 entries, 0 to 999999\n",
      "Data columns (total 2 columns):\n",
      "jobId     1000000 non-null object\n",
      "salary    1000000 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 15.3+ MB\n",
      "  None\n",
      "------------########################------------------\n",
      "                                                     \n",
      "Check the duplication of the data: 0\n",
      "------------########################------------------\n",
      "                                                     \n",
      "\n",
      "Check the missing value of the data:\n",
      " jobId     0\n",
      "salary    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Informantion of the target data\n",
    "data_info(salary_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are total of 1000000 entry and 8 columns. Six of them are categorical data and rest of the two are numerical data and our target column is also a numerical column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Summary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for Statistical susmmary \n",
    "def summary(df):\n",
    "    print('\\n Statistical Summary of Numberical data:\\n', df.describe(include=np.number))\n",
    "    print('------------########################------------------')\n",
    "    print('\\n Statistical Summary of categorical data:\\n',df.describe(include='O'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Statistical Summary of Numberical data:\n",
      "        yearsExperience  milesFromMetropolis\n",
      "count   1000000.000000       1000000.000000\n",
      "mean         11.992386            49.529260\n",
      "std           7.212391            28.877733\n",
      "min           0.000000             0.000000\n",
      "25%           6.000000            25.000000\n",
      "50%          12.000000            50.000000\n",
      "75%          18.000000            75.000000\n",
      "max          24.000000            99.000000\n",
      "------------########################------------------\n",
      "\n",
      " Statistical Summary of categorical data:\n",
      "                    jobId companyId  jobType       degree    major industry\n",
      "count            1000000   1000000  1000000      1000000  1000000  1000000\n",
      "unique           1000000        63        8            5        9        7\n",
      "top     JOB1362685163667    COMP39   SENIOR  HIGH_SCHOOL     NONE      WEB\n",
      "freq                   1     16193   125886       236976   532355   143206\n"
     ]
    }
   ],
   "source": [
    "# Summary of the training data\n",
    "summary(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to visualize the target variable\n",
    "def plot_target(df):\n",
    "    plt.figure(figsize=(14,6))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.title('Boxplot')\n",
    "    sns.boxplot(df.salary)\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.title('Distribution Plot')\n",
    "    sns.distplot(df.salary, bins=20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\T420\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_target(salary_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that there are outliers in the target column(left panel). Furhter analysis would guide us how to tackle these outliers. There can be many reasons for outliers. It can be incorrectly reported or some unsual data. Right box plot shows that the data is normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's Merge the salary data with the training data that would help us to see the depending of input feature with target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the target column with the training dataset\n",
    "def full_data(df1,df2,key=None ):\n",
    "    return pd.merge(df1, df2,on=key)\n",
    "\n",
    "train=full_data(train_data, salary_data,key='jobId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to check outliers\n",
    "def outlier(df):\n",
    "    stat=df.describe()\n",
    "    IQR=stat['75%']-stat['25%']\n",
    "    upper=stat['75%']+1.5*IQR\n",
    "    lower=stat['25%']-1.5*IQR\n",
    "    print('The upper and lower bounds for outliers are {} and {}'.format(upper,lower))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The upper and lower bounds for outliers are 220.5 and 8.5\n"
     ]
    }
   ],
   "source": [
    "outlier(train.salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30559</th>\n",
       "      <td>JOB1362684438246</td>\n",
       "      <td>COMP44</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>MATH</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495984</th>\n",
       "      <td>JOB1362684903671</td>\n",
       "      <td>COMP34</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652076</th>\n",
       "      <td>JOB1362685059763</td>\n",
       "      <td>COMP25</td>\n",
       "      <td>CTO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816129</th>\n",
       "      <td>JOB1362685223816</td>\n",
       "      <td>COMP42</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828156</th>\n",
       "      <td>JOB1362685235843</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId         jobType       degree        major  \\\n",
       "30559   JOB1362684438246    COMP44          JUNIOR     DOCTORAL         MATH   \n",
       "495984  JOB1362684903671    COMP34          JUNIOR         NONE         NONE   \n",
       "652076  JOB1362685059763    COMP25             CTO  HIGH_SCHOOL         NONE   \n",
       "816129  JOB1362685223816    COMP42         MANAGER     DOCTORAL  ENGINEERING   \n",
       "828156  JOB1362685235843    COMP40  VICE_PRESIDENT      MASTERS  ENGINEERING   \n",
       "\n",
       "       industry  yearsExperience  milesFromMetropolis  salary  \n",
       "30559      AUTO               11                    7       0  \n",
       "495984      OIL                1                   25       0  \n",
       "652076     AUTO                6                   60       0  \n",
       "816129  FINANCE               18                    6       0  \n",
       "828156      WEB                3                   29       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# salry is equal to zero\n",
    "train[train['salary']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No one can have zero salary. We can just simply drop the data with zero salary. Check the upper and lower bound of the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_low=train[train.salary<8.5]\n",
    "outliers_up=train[train.salary>220.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30559</th>\n",
       "      <td>JOB1362684438246</td>\n",
       "      <td>COMP44</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>MATH</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495984</th>\n",
       "      <td>JOB1362684903671</td>\n",
       "      <td>COMP34</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652076</th>\n",
       "      <td>JOB1362685059763</td>\n",
       "      <td>COMP25</td>\n",
       "      <td>CTO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816129</th>\n",
       "      <td>JOB1362685223816</td>\n",
       "      <td>COMP42</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828156</th>\n",
       "      <td>JOB1362685235843</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId         jobType       degree        major  \\\n",
       "30559   JOB1362684438246    COMP44          JUNIOR     DOCTORAL         MATH   \n",
       "495984  JOB1362684903671    COMP34          JUNIOR         NONE         NONE   \n",
       "652076  JOB1362685059763    COMP25             CTO  HIGH_SCHOOL         NONE   \n",
       "816129  JOB1362685223816    COMP42         MANAGER     DOCTORAL  ENGINEERING   \n",
       "828156  JOB1362685235843    COMP40  VICE_PRESIDENT      MASTERS  ENGINEERING   \n",
       "\n",
       "       industry  yearsExperience  milesFromMetropolis  salary  \n",
       "30559      AUTO               11                    7       0  \n",
       "495984      OIL                1                   25       0  \n",
       "652076     AUTO                6                   60       0  \n",
       "816129  FINANCE               18                    6       0  \n",
       "828156      WEB                3                   29       0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outliers below the first quartiles\n",
    "outliers_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>JOB1362684407953</td>\n",
       "      <td>COMP30</td>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>OIL</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>JOB1362684408049</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>CTO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>JOB1362684408247</td>\n",
       "      <td>COMP53</td>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>WEB</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>JOB1362684408357</td>\n",
       "      <td>COMP26</td>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>JOB1362684408406</td>\n",
       "      <td>COMP54</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>OIL</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>JOB1362684408653</td>\n",
       "      <td>COMP32</td>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>JOB1362684408673</td>\n",
       "      <td>COMP37</td>\n",
       "      <td>CFO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>JOB1362684408898</td>\n",
       "      <td>COMP24</td>\n",
       "      <td>CTO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>JOB1362684408909</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>JOB1362684409100</td>\n",
       "      <td>COMP56</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>WEB</td>\n",
       "      <td>24</td>\n",
       "      <td>15</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>JOB1362684409187</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>CTO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1577</th>\n",
       "      <td>JOB1362684409264</td>\n",
       "      <td>COMP50</td>\n",
       "      <td>CEO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>JOB1362684409514</td>\n",
       "      <td>COMP33</td>\n",
       "      <td>CFO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>JOB1362684409594</td>\n",
       "      <td>COMP4</td>\n",
       "      <td>CFO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>JOB1362684409803</td>\n",
       "      <td>COMP3</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>JOB1362684409860</td>\n",
       "      <td>COMP0</td>\n",
       "      <td>CTO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2217</th>\n",
       "      <td>JOB1362684409904</td>\n",
       "      <td>COMP45</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2356</th>\n",
       "      <td>JOB1362684410043</td>\n",
       "      <td>COMP3</td>\n",
       "      <td>CFO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>WEB</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2396</th>\n",
       "      <td>JOB1362684410083</td>\n",
       "      <td>COMP8</td>\n",
       "      <td>CTO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>JOB1362684410263</td>\n",
       "      <td>COMP16</td>\n",
       "      <td>CTO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2869</th>\n",
       "      <td>JOB1362684410556</td>\n",
       "      <td>COMP33</td>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>JOB1362684410753</td>\n",
       "      <td>COMP44</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3098</th>\n",
       "      <td>JOB1362684410785</td>\n",
       "      <td>COMP45</td>\n",
       "      <td>CTO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>OIL</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3517</th>\n",
       "      <td>JOB1362684411204</td>\n",
       "      <td>COMP58</td>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3606</th>\n",
       "      <td>JOB1362684411293</td>\n",
       "      <td>COMP42</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>JOB1362684411310</td>\n",
       "      <td>COMP51</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>MATH</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4016</th>\n",
       "      <td>JOB1362684411703</td>\n",
       "      <td>COMP19</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4072</th>\n",
       "      <td>JOB1362684411759</td>\n",
       "      <td>COMP56</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4154</th>\n",
       "      <td>JOB1362684411841</td>\n",
       "      <td>COMP39</td>\n",
       "      <td>CEO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4299</th>\n",
       "      <td>JOB1362684411986</td>\n",
       "      <td>COMP47</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995905</th>\n",
       "      <td>JOB1362685403592</td>\n",
       "      <td>COMP1</td>\n",
       "      <td>CFO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995947</th>\n",
       "      <td>JOB1362685403634</td>\n",
       "      <td>COMP23</td>\n",
       "      <td>CEO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>15</td>\n",
       "      <td>55</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996133</th>\n",
       "      <td>JOB1362685403820</td>\n",
       "      <td>COMP30</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996187</th>\n",
       "      <td>JOB1362685403874</td>\n",
       "      <td>COMP58</td>\n",
       "      <td>CTO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>17</td>\n",
       "      <td>46</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996288</th>\n",
       "      <td>JOB1362685403975</td>\n",
       "      <td>COMP7</td>\n",
       "      <td>CFO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>22</td>\n",
       "      <td>49</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996366</th>\n",
       "      <td>JOB1362685404053</td>\n",
       "      <td>COMP59</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996416</th>\n",
       "      <td>JOB1362685404103</td>\n",
       "      <td>COMP52</td>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996850</th>\n",
       "      <td>JOB1362685404537</td>\n",
       "      <td>COMP24</td>\n",
       "      <td>CTO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>OIL</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997007</th>\n",
       "      <td>JOB1362685404694</td>\n",
       "      <td>COMP47</td>\n",
       "      <td>CFO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>22</td>\n",
       "      <td>44</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997016</th>\n",
       "      <td>JOB1362685404703</td>\n",
       "      <td>COMP4</td>\n",
       "      <td>SENIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997044</th>\n",
       "      <td>JOB1362685404731</td>\n",
       "      <td>COMP35</td>\n",
       "      <td>CEO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997127</th>\n",
       "      <td>JOB1362685404814</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>CTO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>WEB</td>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997201</th>\n",
       "      <td>JOB1362685404888</td>\n",
       "      <td>COMP25</td>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997215</th>\n",
       "      <td>JOB1362685404902</td>\n",
       "      <td>COMP3</td>\n",
       "      <td>CFO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997218</th>\n",
       "      <td>JOB1362685404905</td>\n",
       "      <td>COMP11</td>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997500</th>\n",
       "      <td>JOB1362685405187</td>\n",
       "      <td>COMP34</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997562</th>\n",
       "      <td>JOB1362685405249</td>\n",
       "      <td>COMP28</td>\n",
       "      <td>CEO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997733</th>\n",
       "      <td>JOB1362685405420</td>\n",
       "      <td>COMP33</td>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998021</th>\n",
       "      <td>JOB1362685405708</td>\n",
       "      <td>COMP62</td>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>WEB</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998235</th>\n",
       "      <td>JOB1362685405922</td>\n",
       "      <td>COMP45</td>\n",
       "      <td>CTO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998246</th>\n",
       "      <td>JOB1362685405933</td>\n",
       "      <td>COMP16</td>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>OIL</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998346</th>\n",
       "      <td>JOB1362685406033</td>\n",
       "      <td>COMP0</td>\n",
       "      <td>CEO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998365</th>\n",
       "      <td>JOB1362685406052</td>\n",
       "      <td>COMP11</td>\n",
       "      <td>SENIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998421</th>\n",
       "      <td>JOB1362685406108</td>\n",
       "      <td>COMP46</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998446</th>\n",
       "      <td>JOB1362685406133</td>\n",
       "      <td>COMP46</td>\n",
       "      <td>CFO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998516</th>\n",
       "      <td>JOB1362685406203</td>\n",
       "      <td>COMP29</td>\n",
       "      <td>CFO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999249</th>\n",
       "      <td>JOB1362685406936</td>\n",
       "      <td>COMP30</td>\n",
       "      <td>CEO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999280</th>\n",
       "      <td>JOB1362685406967</td>\n",
       "      <td>COMP2</td>\n",
       "      <td>CFO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>SERVICE</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999670</th>\n",
       "      <td>JOB1362685407357</td>\n",
       "      <td>COMP20</td>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>LITERATURE</td>\n",
       "      <td>SERVICE</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999893</th>\n",
       "      <td>JOB1362685407580</td>\n",
       "      <td>COMP9</td>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7117 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId         jobType       degree        major  \\\n",
       "266     JOB1362684407953    COMP30             CEO      MASTERS      BIOLOGY   \n",
       "362     JOB1362684408049    COMP38             CTO      MASTERS         NONE   \n",
       "560     JOB1362684408247    COMP53             CEO      MASTERS      BIOLOGY   \n",
       "670     JOB1362684408357    COMP26             CEO      MASTERS         MATH   \n",
       "719     JOB1362684408406    COMP54  VICE_PRESIDENT     DOCTORAL      BIOLOGY   \n",
       "966     JOB1362684408653    COMP32             CEO     DOCTORAL  ENGINEERING   \n",
       "986     JOB1362684408673    COMP37             CFO  HIGH_SCHOOL         NONE   \n",
       "1211    JOB1362684408898    COMP24             CTO     DOCTORAL     BUSINESS   \n",
       "1222    JOB1362684408909    COMP40          JUNIOR      MASTERS      COMPSCI   \n",
       "1413    JOB1362684409100    COMP56  VICE_PRESIDENT      MASTERS         MATH   \n",
       "1500    JOB1362684409187    COMP40             CTO    BACHELORS      PHYSICS   \n",
       "1577    JOB1362684409264    COMP50             CEO  HIGH_SCHOOL         NONE   \n",
       "1827    JOB1362684409514    COMP33             CFO  HIGH_SCHOOL         NONE   \n",
       "1907    JOB1362684409594     COMP4             CFO    BACHELORS  ENGINEERING   \n",
       "2116    JOB1362684409803     COMP3             CFO     DOCTORAL  ENGINEERING   \n",
       "2173    JOB1362684409860     COMP0             CTO     DOCTORAL         NONE   \n",
       "2217    JOB1362684409904    COMP45  VICE_PRESIDENT     DOCTORAL     BUSINESS   \n",
       "2356    JOB1362684410043     COMP3             CFO    BACHELORS         MATH   \n",
       "2396    JOB1362684410083     COMP8             CTO    BACHELORS  ENGINEERING   \n",
       "2576    JOB1362684410263    COMP16             CTO      MASTERS      COMPSCI   \n",
       "2869    JOB1362684410556    COMP33             CEO      MASTERS    CHEMISTRY   \n",
       "3066    JOB1362684410753    COMP44             CFO     DOCTORAL  ENGINEERING   \n",
       "3098    JOB1362684410785    COMP45             CTO     DOCTORAL      COMPSCI   \n",
       "3517    JOB1362684411204    COMP58             CEO      MASTERS  ENGINEERING   \n",
       "3606    JOB1362684411293    COMP42  VICE_PRESIDENT     DOCTORAL      BIOLOGY   \n",
       "3623    JOB1362684411310    COMP51             CFO     DOCTORAL         MATH   \n",
       "4016    JOB1362684411703    COMP19             CFO     DOCTORAL         NONE   \n",
       "4072    JOB1362684411759    COMP56  VICE_PRESIDENT      MASTERS  ENGINEERING   \n",
       "4154    JOB1362684411841    COMP39             CEO    BACHELORS      COMPSCI   \n",
       "4299    JOB1362684411986    COMP47  VICE_PRESIDENT     DOCTORAL  ENGINEERING   \n",
       "...                  ...       ...             ...          ...          ...   \n",
       "995905  JOB1362685403592     COMP1             CFO      MASTERS  ENGINEERING   \n",
       "995947  JOB1362685403634    COMP23             CEO    BACHELORS     BUSINESS   \n",
       "996133  JOB1362685403820    COMP30         MANAGER     DOCTORAL  ENGINEERING   \n",
       "996187  JOB1362685403874    COMP58             CTO      MASTERS     BUSINESS   \n",
       "996288  JOB1362685403975     COMP7             CFO    BACHELORS      PHYSICS   \n",
       "996366  JOB1362685404053    COMP59             CFO     DOCTORAL  ENGINEERING   \n",
       "996416  JOB1362685404103    COMP52             CEO     DOCTORAL     BUSINESS   \n",
       "996850  JOB1362685404537    COMP24             CTO     DOCTORAL    CHEMISTRY   \n",
       "997007  JOB1362685404694    COMP47             CFO      MASTERS  ENGINEERING   \n",
       "997016  JOB1362685404703     COMP4          SENIOR     DOCTORAL  ENGINEERING   \n",
       "997044  JOB1362685404731    COMP35             CEO    BACHELORS         MATH   \n",
       "997127  JOB1362685404814    COMP38             CTO     DOCTORAL    CHEMISTRY   \n",
       "997201  JOB1362685404888    COMP25             CEO     DOCTORAL  ENGINEERING   \n",
       "997215  JOB1362685404902     COMP3             CFO  HIGH_SCHOOL         NONE   \n",
       "997218  JOB1362685404905    COMP11             CEO     DOCTORAL     BUSINESS   \n",
       "997500  JOB1362685405187    COMP34  VICE_PRESIDENT     DOCTORAL  ENGINEERING   \n",
       "997562  JOB1362685405249    COMP28             CEO    BACHELORS     BUSINESS   \n",
       "997733  JOB1362685405420    COMP33             CEO      MASTERS  ENGINEERING   \n",
       "998021  JOB1362685405708    COMP62             CEO     DOCTORAL      COMPSCI   \n",
       "998235  JOB1362685405922    COMP45             CTO      MASTERS      PHYSICS   \n",
       "998246  JOB1362685405933    COMP16             CEO      MASTERS     BUSINESS   \n",
       "998346  JOB1362685406033     COMP0             CEO    BACHELORS         MATH   \n",
       "998365  JOB1362685406052    COMP11          SENIOR     DOCTORAL     BUSINESS   \n",
       "998421  JOB1362685406108    COMP46             CFO     DOCTORAL     BUSINESS   \n",
       "998446  JOB1362685406133    COMP46             CFO      MASTERS  ENGINEERING   \n",
       "998516  JOB1362685406203    COMP29             CFO      MASTERS  ENGINEERING   \n",
       "999249  JOB1362685406936    COMP30             CEO         NONE         NONE   \n",
       "999280  JOB1362685406967     COMP2             CFO    BACHELORS     BUSINESS   \n",
       "999670  JOB1362685407357    COMP20             CEO     DOCTORAL   LITERATURE   \n",
       "999893  JOB1362685407580     COMP9             CEO     DOCTORAL  ENGINEERING   \n",
       "\n",
       "       industry  yearsExperience  milesFromMetropolis  salary  \n",
       "266         OIL               23                   60     223  \n",
       "362      HEALTH               24                    3     223  \n",
       "560         WEB               22                    7     248  \n",
       "670        AUTO               23                    9     240  \n",
       "719         OIL               21                   14     225  \n",
       "966     FINANCE               23                   11     247  \n",
       "986     FINANCE               24                   20     229  \n",
       "1211    FINANCE                5                   46     232  \n",
       "1222        OIL               24                    5     225  \n",
       "1413        WEB               24                   15     241  \n",
       "1500     HEALTH               23                   12     225  \n",
       "1577     HEALTH               19                    7     238  \n",
       "1827        OIL               23                   16     234  \n",
       "1907    FINANCE               21                   21     243  \n",
       "2116       AUTO               22                   16     237  \n",
       "2173    FINANCE               15                    7     222  \n",
       "2217       AUTO               24                    4     232  \n",
       "2356        WEB               20                   22     230  \n",
       "2396        WEB                9                    7     231  \n",
       "2576       AUTO               23                   10     230  \n",
       "2869       AUTO               23                   11     221  \n",
       "3066    FINANCE               13                   17     233  \n",
       "3098        OIL               20                    4     254  \n",
       "3517        OIL               24                    0     235  \n",
       "3606     HEALTH               21                   19     243  \n",
       "3623    FINANCE               23                   30     229  \n",
       "4016    FINANCE               18                    1     239  \n",
       "4072        OIL               20                   14     226  \n",
       "4154    FINANCE               19                   29     243  \n",
       "4299        WEB               17                   18     229  \n",
       "...         ...              ...                  ...     ...  \n",
       "995905      OIL               22                   12     241  \n",
       "995947  FINANCE               15                   55     235  \n",
       "996133      OIL               20                   19     223  \n",
       "996187  FINANCE               17                   46     224  \n",
       "996288  FINANCE               22                   49     223  \n",
       "996366  FINANCE               24                   67     222  \n",
       "996416   HEALTH               15                   35     221  \n",
       "996850      OIL               21                   32     221  \n",
       "997007  FINANCE               22                   44     251  \n",
       "997016      OIL               24                   12     233  \n",
       "997044  FINANCE               24                   22     261  \n",
       "997127      WEB               21                   37     221  \n",
       "997201  FINANCE               13                   19     228  \n",
       "997215  FINANCE               24                   21     235  \n",
       "997218  FINANCE                8                    2     227  \n",
       "997500  FINANCE               19                   17     242  \n",
       "997562  FINANCE               15                    3     221  \n",
       "997733      WEB               20                   20     234  \n",
       "998021      WEB               19                   22     221  \n",
       "998235  FINANCE               19                   12     223  \n",
       "998246      OIL               13                   32     243  \n",
       "998346   HEALTH               23                   30     228  \n",
       "998365  FINANCE               19                   15     224  \n",
       "998421  FINANCE               19                    4     235  \n",
       "998446      WEB               21                    0     247  \n",
       "998516      WEB               14                   46     227  \n",
       "999249      OIL               17                   10     223  \n",
       "999280  SERVICE               21                    0     228  \n",
       "999670  SERVICE               24                   14     233  \n",
       "999893  FINANCE               17                   33     237  \n",
       "\n",
       "[7117 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# outliers below the upper bound\n",
    "outliers_up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need further analysis to check if they are really outliers or not. If we take a close look at the data it is found that junior position is also paid above that outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>JOB1362684408909</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27710</th>\n",
       "      <td>JOB1362684435397</td>\n",
       "      <td>COMP21</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31355</th>\n",
       "      <td>JOB1362684439042</td>\n",
       "      <td>COMP45</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100042</th>\n",
       "      <td>JOB1362684507729</td>\n",
       "      <td>COMP17</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160333</th>\n",
       "      <td>JOB1362684568020</td>\n",
       "      <td>COMP18</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189582</th>\n",
       "      <td>JOB1362684597269</td>\n",
       "      <td>COMP32</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214606</th>\n",
       "      <td>JOB1362684622293</td>\n",
       "      <td>COMP47</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303778</th>\n",
       "      <td>JOB1362684711465</td>\n",
       "      <td>COMP51</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348354</th>\n",
       "      <td>JOB1362684756041</td>\n",
       "      <td>COMP56</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427593</th>\n",
       "      <td>JOB1362684835280</td>\n",
       "      <td>COMP54</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500739</th>\n",
       "      <td>JOB1362684908426</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627534</th>\n",
       "      <td>JOB1362685035221</td>\n",
       "      <td>COMP5</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645555</th>\n",
       "      <td>JOB1362685053242</td>\n",
       "      <td>COMP36</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656572</th>\n",
       "      <td>JOB1362685064259</td>\n",
       "      <td>COMP28</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>OIL</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685775</th>\n",
       "      <td>JOB1362685093462</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743326</th>\n",
       "      <td>JOB1362685151013</td>\n",
       "      <td>COMP14</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787674</th>\n",
       "      <td>JOB1362685195361</td>\n",
       "      <td>COMP43</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796956</th>\n",
       "      <td>JOB1362685204643</td>\n",
       "      <td>COMP30</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855219</th>\n",
       "      <td>JOB1362685262906</td>\n",
       "      <td>COMP13</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954368</th>\n",
       "      <td>JOB1362685362055</td>\n",
       "      <td>COMP11</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId jobType     degree        major industry  \\\n",
       "1222    JOB1362684408909    COMP40  JUNIOR    MASTERS      COMPSCI      OIL   \n",
       "27710   JOB1362684435397    COMP21  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "31355   JOB1362684439042    COMP45  JUNIOR   DOCTORAL      COMPSCI  FINANCE   \n",
       "100042  JOB1362684507729    COMP17  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "160333  JOB1362684568020    COMP18  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "189582  JOB1362684597269    COMP32  JUNIOR   DOCTORAL     BUSINESS      OIL   \n",
       "214606  JOB1362684622293    COMP47  JUNIOR    MASTERS     BUSINESS  FINANCE   \n",
       "303778  JOB1362684711465    COMP51  JUNIOR    MASTERS  ENGINEERING      WEB   \n",
       "348354  JOB1362684756041    COMP56  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "427593  JOB1362684835280    COMP54  JUNIOR   DOCTORAL  ENGINEERING  FINANCE   \n",
       "500739  JOB1362684908426    COMP40  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "627534  JOB1362685035221     COMP5  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "645555  JOB1362685053242    COMP36  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "656572  JOB1362685064259    COMP28  JUNIOR   DOCTORAL     BUSINESS      OIL   \n",
       "685775  JOB1362685093462    COMP38  JUNIOR  BACHELORS  ENGINEERING      OIL   \n",
       "743326  JOB1362685151013    COMP14  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "787674  JOB1362685195361    COMP43  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "796956  JOB1362685204643    COMP30  JUNIOR    MASTERS     BUSINESS      OIL   \n",
       "855219  JOB1362685262906    COMP13  JUNIOR    MASTERS  ENGINEERING      OIL   \n",
       "954368  JOB1362685362055    COMP11  JUNIOR   DOCTORAL     BUSINESS      OIL   \n",
       "\n",
       "        yearsExperience  milesFromMetropolis  salary  \n",
       "1222                 24                    5     225  \n",
       "27710                24                    3     246  \n",
       "31355                24                    0     225  \n",
       "100042               23                    8     248  \n",
       "160333               22                    3     223  \n",
       "189582               24                   11     221  \n",
       "214606               22                    4     222  \n",
       "303778               24                    2     226  \n",
       "348354               23                   25     226  \n",
       "427593               23                    3     221  \n",
       "500739               21                    0     227  \n",
       "627534               24                   29     230  \n",
       "645555               24                    1     225  \n",
       "656572               22                    3     222  \n",
       "685775               24                   13     225  \n",
       "743326               19                    0     236  \n",
       "787674               18                   15     232  \n",
       "796956               24                    2     228  \n",
       "855219               22                   26     225  \n",
       "954368               24                   26     223  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the potential suspicious outliers above the upper bound\n",
    "train[(train.salary>220.5) & (train.jobType=='JUNIOR')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From outliers above the third quartile, it is seen that they are legitimate because majority of the position  are C-level. Employees with advanced degree and years of experience are well paid. So it seems the data are realistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the outliers below 8.5\n",
    "train=train[train.salary>8.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "industry\n",
       "EDUCATION     99.448386\n",
       "SERVICE      104.446820\n",
       "AUTO         109.435222\n",
       "HEALTH       115.735540\n",
       "WEB          121.645362\n",
       "FINANCE      130.747659\n",
       "OIL          130.953863\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby based on industry\n",
    "train.groupby('industry')['salary'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highly paid industries are OIL and FINANCE and lowest paid is Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['jobId', 'companyId', 'jobType', 'degree', 'major', 'industry',\n",
       "       'yearsExperience', 'milesFromMetropolis', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All Colmuns\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JobId and companyId columns have no contribution to the target. So we will drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorical Columns\n",
    "cat_col=['jobType', 'degree', 'major', 'industry']\n",
    "# Numerical Columns\n",
    "num_col=['yearsExperience', 'milesFromMetropolis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot salary vs categorical plot\n",
    "def cat_plot(df):\n",
    "    for col in cat_col:\n",
    "        f, ax = plt.subplots(figsize=(12, 6))\n",
    "        sns.boxplot(x=col,y='salary', data=df)\n",
    "        plt.xlabel(col)\n",
    "        plt.title('{}'.format(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_plot(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the degree plots, it is seen that salary increase with higher degree. Having doctoral and master degree are higly paid. Similarly, Oil and Finance industry pay more compare to others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot salary vs categorical plot\n",
    "def numerical_plot(df):\n",
    "    for col in num_col:\n",
    "        f, ax = plt.subplots(figsize=(10, 6))\n",
    "        mean = df.groupby(col)['salary'].mean()\n",
    "        mean.plot()\n",
    "        plt.xlabel(col)\n",
    "        plt.ylabel('Salary')\n",
    "        plt.title('{} vs {}'.format('Salary',col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_plot(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is obvious that yearsofExperience is strongly positively correlated with salary whereas milesFromMetropolis is negatively correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting categorical data to numerical data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before converting the data let's remove the jobId and companyId columns becuase they do not have any contribution to predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=train.drop(columns=['jobId','companyId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding for categorical columns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cat_col:\n",
    "    df_train[col]=le.fit_transform(df_train[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>83</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   jobType  degree  major  industry  yearsExperience  milesFromMetropolis  \\\n",
       "0        1       3      6         3               10                   83   \n",
       "1        0       2      7         6                3                   73   \n",
       "2        7       1      8         3               10                   38   \n",
       "3        5       1      2         0                8                   17   \n",
       "4        7       0      8         2                8                   16   \n",
       "\n",
       "   salary  \n",
       "0     130  \n",
       "1     101  \n",
       "2     137  \n",
       "3     142  \n",
       "4     163  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap plot for numerical data\n",
    "fig = plt.figure(figsize=(12, 10))\n",
    "#features = ['jobType', 'degree', 'major', 'industry', 'yearsExperience', 'milesFromMetropolis']\n",
    "sns.heatmap(df_train[num_col + ['salary']].corr(), cmap='Blues', annot=True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation plot confirms that yearsExperience has positive correlation with salary whereas milesFrom Metropolis has negative correlation with salaries"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}