Exploratory Data Analysis/Exploratory Data Analysis Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Storytelling-IBM HR Analytics Employee Attrition & Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "from numpy import median\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "from plotly import __version__\n",
    "from plotly import plotly\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "# Import and suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the file\n",
    "df = pd.read_csv('C:/users/eturk/Data_Science/Capstone_Project-IBM_HR_Analytics/data/HR-Employee-Attrition.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reassign response variable\n",
    "df['Attrition'] = df['Attrition'].apply(lambda x: 0 if x == 'No' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop useless features and rename the dataframe\n",
    "df2 = df.drop(labels = ['Over18','StandardHours','EmployeeCount'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Moving Response Variable to the last column\n",
    "front = df2['Attrition']\n",
    "df2.drop(labels=['Attrition'], axis=1,inplace=True)\n",
    "df2.insert(31,'Attrition',front)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing object types to categories\n",
    "cols = ['BusinessTravel','Department','EducationField','Gender','JobRole','MaritalStatus','OverTime']\n",
    "for col in cols:\n",
    "    df2[col] = df2[col].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 32 features consisting of both categorical as well as the numerical features. Response variable is 'Attrition' of the employees which can 1 and 0 (representing 'Yes' and 'No' respectively). This is what we will predict.  \n",
    "\n",
    "Now, I will try to analyze visually the trends in how and why employees are quitting their jobs. For that, I will deep dive into the details about features and their relationships between each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KOjcDm5FqEQCTgR2BbYDbJTXnR5s2RERjlfs0M7MqVHKj3EJJ80qjuUp6KSIWVbm/lYE1gN1IgwHeBPSV1JznzwMGAssDs8rWK5U3tbbhQYOWoaHBT0E1s96lsXFAzbZdSYL4W0QcAfSPiE8AhwOPVLm/WcBT+bJZRcSbpGamkgHAHGBunm5Z3qrZs9+oMiQzs8VXU9O8Dq3fVoKppIlpLPARYD5pqI25pCRRjT8Cu0REn4hYDVgWuDP3TQCMAqYB9wIjI6JvRKxOqmXMrHKfZmZWhUpqEIcD50mqtt/hXZJujojtgAdJyWks8BwwISKWAKYD10laGBHTgPvKljMzsy5USYJYBpgSEc+QLkf9raR3qt2hpGMLiocXLDcOGFftfszMrGPabWKSdKqk9YAzgO2BRyPiwtwfYWZmPVRFz4OIiGVJVx2tBSwCXgHOi4gzahibmZnVUbtNTBHxc9K9CbcAp0n6Yy5fEniJ6u+JMDOzbqySPoi7gMMkvV5eKOmtiPh4bcIyM7N6qyRBXAucHBGfzsvfTRoa43VJL9c0OjMzq5tK+iAuIN2vcBCwP9AfuLiWQZmZWf1VUoPYTNImZa+PiIgnaxWQmZl1D5XUIPpGxAqlF3m66uG+zcxs8VBJDWI88OeIuIk0HPdnSPdEmJlZD1bJjXJXAHsCz5KGxdhL0uW1DszMzOqr1RpEROzXoqg0ZOCmEbGppKtqF5aZmdVbW01M27cxrxlwgjAz68FaTRCSDixNR0QDsDGpc/rxsgf8mJlZD1XJM6l3BF4AfgZcCTwbEVvUOjAzM6uvSq5i+jEwStKjABGxOelGuc1rGZiZmdVXJfdBvFVKDgCSHiJd7mpmZj1YJTWIeyLiUmACqQ9iH+D5/GQ4JN1Tw/jMzKxOKkkQpQcDndmi/FTS1Uw7dGpEZmbWLbSbICS1dbmrmZn1UJU8MGhb4GhgUHm5JNcczMx6sEqamCaSmpNm1DYUMzPrTipJEP/ysBpmZr1PJQni/Pxc6rsoG+bbScPMrGerJEEcBCwFbFtW5rGYzMx6uEoSxKqShtY8EjMz61YquZP6gYjYLSL61TwaMzPrNiqpQewBHAY0RwSkYTaaJVWdMCJiFeAvwE6kfo2JpGarJ4CxkhZFxCnA6Dz/aEkPVrs/MzP74Cq5Ue7DnbnDiOgPXALMz0XjgZMkTYmIi4ExETEDGA4MAwYDkwCPIGtm1oVabWKKiK+XTW/QYt6PO7DPc0ijwf47v94MmJqnJwM7AtsAt0tqlvQC0BARjR3Yp5mZfUBt1SAOAX6ap68Gyjuqt6tmZxFxANAk6baIOCEX9yl7ANE8YCCwPDCrbNVSeVNr2x40aBkaGtxNYma9S2PjgJptu60E0aeV6Y44iNSXsSNpEMCrgFXK5g8A5gBz83TL8lbNnv1GJ4VoZrb4aGqa16H120owlVzFBKkDucMkbSdpuKQRwCPAfsDkiBiRFxkFTAPuBUZGRN+IWB3oK2lmZ8RgZmaVaasG0VXPnT4GmBARSwDTgeskLYyIacB9pCQ2totiMTOzrK0EsUFEPJunP1I23Qfo8JVNuRZRMrxg/jhgXEf3Y2Zm1WkrQazbZVGYmVm302qCkOThvc3MerFKO6nNzKyXcYIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo11DuA7uSos2+qdwjWDZ337d3rHYJZXbgGYWZmhZwgzMyskBOEmZkV6tI+iIjoD1wODAGWBE4DngQmAs3AE8BYSYsi4hRgNLAAOFrSg10Zq5lZb9fVNYh9gVmStgVGARcC44GTclkfYExEDAWGA8OAfYCLujhOM7Ner6uvYvoNcF3Z6wXAZsDU/HoysDMg4HZJzcALEdEQEY2Smlrb8KBBy9DQ0K9GYVtv1tg4oN4hmLWqlp/PLk0Qkl4DiIgBpERxEnBOTgQA84CBwPLArLJVS+WtJojZs9+oRchmNDXNq3cIZq3q6OezrQTT5Z3UETEYuBu4WtI1wKKy2QOAOcDcPN2y3MzMukiXJoiI+BBwO3CcpMtz8cMRMSJPjwKmAfcCIyOib0SsDvSVNLMrYzUz6+26ug/iRGAQ8N2I+G4uOwo4PyKWAKYD10laGBHTgPtISWxsF8dpZtbrdXUfxFGkhNDS8IJlxwHjahySmZm1wjfKmZlZIScIMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0JOEGZmVsgJwszMCjlBmJlZIScIMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0JOEGZmVsgJwszMCjlBmJlZIScIMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0JOEGZmVsgJwszMCjlBmJlZoYZ6B9CaiOgL/ATYBHgLOFjS0/WNysys9+jONYg9gKUkfQo4HvhRneMxM+tVunOC2Aa4FUDS/cDm9Q3HzKx36dPc3FzvGApFxKXAJEmT8+sXgLUkLahvZGZmvUN3rkHMBQaUve7r5GBm1nW6c4K4F9gVICK2BB6vbzhmZr1Lt72KCbgB2Cki/gT0AQ6sczxmZr1Kt+2DMDOz+urOTUxmZlZHThBmZlbICcLMzAp1507qHi8iRgBfk7RPBcv2A34PLAuMAUZKuqbFMtsBcyQ9FhEvS1q1ipgGk+5aXwVYGvgLcLSktz/otqzzRMQGwFnAMsBypM/COEk17USMiI2AQZLuiYjngfUkvdnG8tsBx0sqXYF4AvBtYBVJCyJie+BISXtWsO8D8v6OLyvbBVhd0s+qPJ5VgZMlHV7h8tsApwD9Sd+9KyT9pJp9L45cg1h8fBhYWdI2wEbA7gXLHASsVu0OchK6EfiRpBGShgHvAN+rdpvWcRGxAvArUqLeHtiS9Bk4rAt2/1ng4x9g+fuAjfNYagAjgbuArfPrEeQREqoh6dZqk0Ne/+UPkBzWAi4A9pU0AtgO2C8nqV7BNYhuKCKGA6cDC4FnSCeCnwEfi4hLgLWATSLi0NKXJSI2A3YBhkbEk8CSEXENsDowC/gc6dfnZcBKeVdHSiq/v2Qb4EVJD5SVHUf+IRERxwD7AAuAeyQdFxHjgHWAlYEVSQMsfhZYF9gfeBn4DfAS8FFgsqTvRMSGwPi87RVyLH+KiH+Q7oEJ4D95W1cDv5B0S0SsD5wjaXTVb/DiZwxwl6R/AEhaGBH7AW+3rIWWao4RMZH0d14JOJs0ntnbpM/RC7z/8/Vl0n1HywBrAz8E7gAOyPv5a46lb0Q8DXxS0isR8XVgOUln59jeiYiHSUniedLf91fAaGAqMBzYPyL6A5fnffUDxku6NiKmAE3AIOCX+Zgagd8CJwODgfWAi/P8F/M2HpT09YhYGbgGWBIQsIOkdUpvZEQMAX4lacuIeCzHtDHQDIyR9GrZ+/4V4CpJ/8nHNj8iRgKvtRP/o8CGwGvANFKSXAHYOf8txwDLk74z35M0KSI+B4wlXdIP6fu6Ien79zawJnAtcAbw99be/87mGkQ3ExF9gAnAXpKGA/8ifUkPB56UdBjpy31X+S8pSX8h/TI7VtILpGaIE3ONYyCwKXAicGf+FXoo8NMWu18NeLa8QNKbkt7ITQ17A1vlfx+LiN3yYvMl7QJcD+wq6TPAmaRkAjAkH8MWwA4RMRTYADhG0o6kRFG6z2Ut4Lt5kMbGvM4EUrKBVEu6rLJ3s8co+ru8VkGz312StgJmkwa+3Bb4OcWfL4CBknYj1U6Pl/QvYCLp5PdgXmYR8Ave+9t+BbiqxX7vALYlnRDvyP92ioil8j5mkJLSzBzfjsBp+eQOcE3+XCwEPgTcBHxL0p0t9rMu8FXgk8CuufnoO8Bv87H9hrZ/BC8P/LLsfRjVYn7R+/6qpIXtxP+gpE+TktQbknYCniQlR0jfzZ3y+zM+IhrysYzONRWRkgrAGqQfSZ8ifbcref87jWsQ3U8jqTnp1xEBqR/g9iq284qk5/P0y6RfhhuRTtBfyOWDWqwzg/RhfFdErET6cC4N3C/pnVw+jXSSByj9upxD+iJAPinl6UclvZLXe4BUO3gR+G5EzCcNqTI3LztT0ot5+sW8jSnA+RGxCulLdWLF70LPMAMYWl4QEWuSfk231KdsWgXTrX2+ngEeycuU3vfWXAZcGxH3AC+XfmGXuQM4FXgduFDSqxHxKqmGOyUvsz7wBwBJ83Ktd+2CuHch1T6Lfsw+LWkeQES8lGNeH7gyz5/WxjGUPJz/LzrmGbR4jyNiE9J73Fb87X0fpuYT/X8iYjbpb/Jf4MqIeI1UQ7ovL/t4HmJoQf6uQPvvf6dxDaL7mQn8k1TdHUGqLdzdYplFFP/tysuLOi+fAs7N292b9Euk3P3AmhHxSXi3NjOO1Pb6FDAsIhpy+Xakqm5r+yq3fkQsk/s4hpG+NOcDp0janzSMSunE9r5t5Y7YnwPnAbeXklQvcjOwS0SsDZCbN8aTmiDeJJ3wiYg1SM18JYsKptv6fBX9Hd/3Wcs11DmkX+vvq81Jmk769b0h750sbwP+j/f6H6aTahlExADSj5fnCuK+EtgXuDQilm2xq6J4nyD9oIHUV9Oetj671wAH5yYuImI54BLSsbUVf3vfh83yeh8i1WLeICXUfYCDgfm0/X1o8/3vTE4Q9bdzRDxU+kdqzz8KuCUPM3I46UNf7hlgo4g4ukX5A8CZuZ2+yOnA3rmd9NaW282/aj4PjIuIqcCfSR/Uk3Jfxa9J/QMPAs+T2oUr8Tapuv8AcKOkR0kn/BtzTWRd2u9cn0iq3fS25iUkzSU1sU3If7v7Se3cPwUeAubkmtmpvHeSam1bi2j/81XuL8AR+eqjchNIJ8jWOpz/Dvyt7CqryaQT49T8+mfAShHxR1Kt4lRJ/20l5idJn5dz24iz5Exg94i4GziEdJFFVXIN/Fjg+vy+TwWulPT7DxJ/gVUj4k7gFtL7P5f0vforqdYzn/a/D+29/53CQ21YTZV3CnZwOx8hdRh+ulMCsw6JiL2BDSWdXO9YykXErkCTpD9HxI6kfrgd6h1XSdGlu1Vup0vef/dBWLcXEZ8lNXV9tc6hGBARPyD9eh1T71gKPAdcHhELSFcXHVnneDpdV77/rkGYmVkh90GYmVkhJwgzMyvkBGFmZoXcSW09Xh7W43Hgc5Im5bJTgT9ImhYRhwCvSfplwbq/J12bviTpct+vRsTmpOEtDu5gXKX7TPYkXe/+FmkguVvz/LvzXe9tbePd4+hILGZFXIOw3uAg0n0Y5YPbDSdd5QJpILkli1aUtKukf5OGPFg7lz3U0eSQ7U26N2CopE1IN4Rdne8YhzSwXXvKj8OsU/kqJuvR8l3H/yRdFvgn0p3cW5MGFXyZdHf2qaSB1Q4Bvkga4G4d0k1SF5BO1DeRxom6kpRsxkkaERHrkm6aWpE0tMSR+Rr8icCrpATwEdKgbFe0iO0o0lASn5P0ei4bDjyWY/oGaVyfYRFxBGncnWVJNx5+MR9L6Tj2zLGOkzQl338yRdKQiPhSPpaFpMtA91UbQ3ablbgGYT3daGCGpL+T7vw+VNJVpDuQD5Z0Aenkf7Kk2/I6syStL+l3Zds5EnhI0tgW2/85cL6kjYFvAtdFRKk2MpiUmHYHzimI7SrSUAtNEXFbRBwHSNJsSUeSXgyLiOWBPYARkjYkDb1xRIvjeLxg+yWnATtL2oyUINZr6w0zK3GCsJ7uQPKw0aThkg+MiCXaWeeBduYD747Ns46k6wEk3Q+8QhqMENK4Uc2koSxWbLl+TgRbk2oCt5FqE3+L9ByC8uXmAl8C9omIM4DPkEYErdTvgHsj4ixgkqRH2lvBDJwgrAfLbfmjgGPyswkuJY1gu1c7q85vZ35J0fenD+9d/PEmvDvYYFF834qITSQ9Lml87pC+lfePqDuYNLrnCqQxjSbyv6O2ljSXlfcvFUo6Km9zNvDziNi3oqOzXs8Jwnqyr5Cef/FRSUMkrUEasPBrpIcelU7k5dOted8y+Zf9sxGxF0BEbAmsStuD35UbCHw/10TITUlr896w2wvzswK2IA1tfS5pAMX3bnMPAAAAxklEQVQ9ea9jujyumbw3BPseeZsNkR7CNFPSGaRmrU0rjM96OScI68kOIHXilruI9ICZKcDFEbEVaVz/E/NTvVozHVghIq5uUb4vcGREPA5cSHoQT6XP7/5+3u5j+XkCfwImSrojz7+RNGrr7aQnuT1JGvHzKdITxiDVOErHcRZweKSnvy0NkJ8lcDJwRx4teEvS0+LM2uWrmMzMrJBrEGZmVsgJwszMCjlBmJlZIScIMzMr5ARhZmaFnCDMzKyQE4SZmRX6f7mJrG9Jfa/nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Currently Working in Company    1233\n",
       "Left the Company                 237\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trgt = df2['Attrition'].apply(lambda x: 'Currently Working in Company' if x == 0 else 'Left the Company')\n",
    "sns.countplot(trgt)\n",
    "plt.title('Attrition in the Company')\n",
    "plt.xlabel('Attrition Status')\n",
    "plt.ylabel('Employee Number')\n",
    "plt.show()\n",
    "trgt.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the company, there are 1470 employees. 237 employees who compose 16% of the total number of employee left the company for some reasons. Besides that, 1233 employee is currently continuing to work in the same company. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, I will code two functions to implement dataframe and graph on each feature of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Feature Dataframe\n",
    "def table_df(x): \n",
    "    \"\"\"\n",
    "    This function creates dataframe for each feature in the dataset\n",
    "    \"\"\"\n",
    "    \n",
    "    feature = df2.groupby(x).agg({'Attrition':['size', 'sum', 'mean']})\n",
    "    feature.columns = [' '.join(col).strip() for col in feature.columns.values]\n",
    "    feature = feature.reset_index()\n",
    "    feature['Attrition mean'] = feature['Attrition mean']*100\n",
    "    feature.columns = [x, 'Total Employee ', 'Attrition Number', \n",
    "                       '% of Attrition in the Relevant Group'] \n",
    "    feature['% of Attrition in the Company'] = (feature['Attrition Number'] / 237)*100\n",
    "        \n",
    "    return feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create graph for each feature to compare its attrition in the company \n",
    "\n",
    "def graph_df(x, y = 'bar', z = 0):\n",
    "    \"\"\"\n",
    "    This function creates graph for each feature in the dataset\n",
    "    \"\"\"\n",
    "    \n",
    "    feature = df2.groupby(x).agg({'Attrition':['size', 'sum', 'mean']})\n",
    "    feature.columns = [' '.join(col).strip() for col in feature.columns.values]\n",
    "    feature = feature.reset_index()\n",
    "    feature['Attrition mean'] = feature['Attrition mean']*100\n",
    "    feature.columns = [x, 'Total Employee', 'Attrition Number', \n",
    "                       '% of Attrition in the Relevant Group'] \n",
    "    \n",
    "    fig0, ax1 = plt.subplots()\n",
    "    ax2 = ax1.twinx()\n",
    "    feature.set_index(feature[x], drop=True, inplace=True)\n",
    "    feature['Total Employee'].plot(kind = y, stacked = True, ax = ax1, \n",
    "                                   colormap = 'winter', rot = z)\n",
    "    feature['% of Attrition in the Relevant Group'].plot(use_index = False, \n",
    "                                                         kind = 'line', ax = ax2, colormap='Spectral')\n",
    "    plt.title('Attrition compared to' + \" \" + x  + \" \" + \"Groups\")\n",
    "    ax1.set_xlabel(x)\n",
    "    ax1.yaxis.label.set_color('blue')\n",
    "    ax1.set_ylabel(\"Total Employee\")\n",
    "    ax2.set_ylabel(\"Attrition Percentage in the Relevant Group\")\n",
    "    ax2.yaxis.label.set_color('red')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling age groups\n",
    "table_df(x = 'Age')\n",
    "bins = [17, 21, 25, 35, 60]\n",
    "group_names = ['(18-21)', '(22-25)', '(26-35)', '(36-60)']\n",
    "df2['Age'] = pd.cut(df2['Age'], bins, labels = group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Age</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(18-21)</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>53.658537</td>\n",
       "      <td>9.282700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(22-25)</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>26.829268</td>\n",
       "      <td>9.282700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(26-35)</td>\n",
       "      <td>606</td>\n",
       "      <td>116</td>\n",
       "      <td>19.141914</td>\n",
       "      <td>48.945148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(36-60)</td>\n",
       "      <td>741</td>\n",
       "      <td>77</td>\n",
       "      <td>10.391363</td>\n",
       "      <td>32.489451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  Total Employee   Attrition Number  \\\n",
       "0  (18-21)               41                22   \n",
       "1  (22-25)               82                22   \n",
       "2  (26-35)              606               116   \n",
       "3  (36-60)              741                77   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             53.658537                       9.282700  \n",
       "1                             26.829268                       9.282700  \n",
       "2                             19.141914                      48.945148  \n",
       "3                             10.391363                      32.489451  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age Group & Attrition Table\n",
    "table_df(x = 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition Compared to Age Group Graph\n",
    "graph_df(x = 'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In **18-21 age group**, young employees are more likely to leave the company. Their attrition proportion to their age group is approximately 53.7% (22 out of 41) and that makes up 9% of all attrition (22 out of 237). \n",
    "\n",
    "If we evaluate overall attrition number in the company,  **26-35 age group's attrition number** is the highest comparing to other age groups. In this age group, we have 19.1 % of employee attrition(116 out 606). That makes up approximately 49% of all attrition in the company (116 out of 237). \n",
    "\n",
    "**35-60 age group** generally prefers to secure their job in the same company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Business Travel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>BusinessTravel</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>150</td>\n",
       "      <td>12</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.063291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>277</td>\n",
       "      <td>69</td>\n",
       "      <td>24.909747</td>\n",
       "      <td>29.113924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1043</td>\n",
       "      <td>156</td>\n",
       "      <td>14.956855</td>\n",
       "      <td>65.822785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      BusinessTravel  Total Employee   Attrition Number  \\\n",
       "0         Non-Travel              150                12   \n",
       "1  Travel_Frequently              277                69   \n",
       "2      Travel_Rarely             1043               156   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                              8.000000                       5.063291  \n",
       "1                             24.909747                      29.113924  \n",
       "2                             14.956855                      65.822785  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Business Travel & Attrition Table\n",
    "table_df(x = 'BusinessTravel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Business Travel Group Graph\n",
    "graph_df(x = 'BusinessTravel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the company, most of the employee travel rarely or don't travel according to their job description. That group compose the 81.1% of entire company(1193). The rest of the company employees which is 19.9% of them has to travel frequently (277 out of 1470).  \n",
    "\n",
    "The highest attrition number with 156 belongs to the **employees who travels rarely**. That is approximately 15% of employees in that group (156 out of 1043). But when you put this number overall attrition, it makes up 65.8% of all attrition in the company(156 out of 237).\n",
    "\n",
    "if we look at the attrition percentage of relevant travel group, the **employees who are traveling frequently** are in the danger zone. Because they have the highest attrition proportion, which is 24.9%, in their individual travel group(69 out of 277). That group's attrition rate composes of the 29.1% of overall attrition in the company (69 out of 237).\n",
    "\n",
    "**Employees who don't travel** in their current role have the lowest attrition rate, which is 8%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Department**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Department</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Human Resources</td>\n",
       "      <td>63</td>\n",
       "      <td>12</td>\n",
       "      <td>19.047619</td>\n",
       "      <td>5.063291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>961</td>\n",
       "      <td>133</td>\n",
       "      <td>13.839750</td>\n",
       "      <td>56.118143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales</td>\n",
       "      <td>446</td>\n",
       "      <td>92</td>\n",
       "      <td>20.627803</td>\n",
       "      <td>38.818565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Department  Total Employee   Attrition Number  \\\n",
       "0         Human Resources               63                12   \n",
       "1  Research & Development              961               133   \n",
       "2                   Sales              446                92   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             19.047619                       5.063291  \n",
       "1                             13.839750                      56.118143  \n",
       "2                             20.627803                      38.818565  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Department & Attrition Table\n",
    "table_df(x = 'Department')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Department Graph\n",
    "graph_df(x = 'Department')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three departments in the company. **Research & Development Department** has the most attrition number in the company. 13.8% of **Research & Development Department** employee left the organization. In numbers, it is equal to 133, which makes us the 56.1% of all attrition in the company. Actually, that attrition is a big number for company, but compared with other departments, **Research & Development Department** has the lowest attrition rate in itself as an individual department. \n",
    "\n",
    "**Sales Department** has mostly been affected by the attrition. Because 20.6% of its employees left the organization. This is the highest number compared to the other two departments. That attrition makes up 38.8% of the attrition in the company (92 out of 237). \n",
    "\n",
    "**Human Resources Department** follows the **Sales Department** in terms of being affected by attrition itself. 19% of that department employee left the company. But this is not that huge number in terms of whole attrition in company. **Human Resources Department** employee attrition makes up 5% of all attrition in the company (12 out of 237)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distance From Home**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Distance From Home Groups\n",
    "table_df(x = 'DistanceFromHome')\n",
    "bins = [0, 3, 6, 10, 29]\n",
    "group_names = ['1-3', '4-6', '7-10', '10+']\n",
    "df2['DistanceFromHome'] = pd.cut(df2['DistanceFromHome'], bins, labels = group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>DistanceFromHome</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1-3</td>\n",
       "      <td>503</td>\n",
       "      <td>68</td>\n",
       "      <td>13.518887</td>\n",
       "      <td>28.691983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4-6</td>\n",
       "      <td>188</td>\n",
       "      <td>26</td>\n",
       "      <td>13.829787</td>\n",
       "      <td>10.970464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7-10</td>\n",
       "      <td>335</td>\n",
       "      <td>50</td>\n",
       "      <td>14.925373</td>\n",
       "      <td>21.097046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10+</td>\n",
       "      <td>444</td>\n",
       "      <td>93</td>\n",
       "      <td>20.945946</td>\n",
       "      <td>39.240506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DistanceFromHome  Total Employee   Attrition Number  \\\n",
       "0              1-3              503                68   \n",
       "1              4-6              188                26   \n",
       "2             7-10              335                50   \n",
       "3              10+              444                93   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             13.518887                      28.691983  \n",
       "1                             13.829787                      10.970464  \n",
       "2                             14.925373                      21.097046  \n",
       "3                             20.945946                      39.240506  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distance From Home Group & Attrition Table\n",
    "table_df(x = 'DistanceFromHome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Distance From Home Graph\n",
    "graph_df(x = 'DistanceFromHome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Employees whose homes are 1-3 miles far away from the company** compose approximately 1/3 of the whole company employee and their attrition rate is 28.7% of all company (68 out of 237).\n",
    "\n",
    "Also, **employees whose homes are 10+ miles far away from the company** compose approximately the other 1/3 of the whole company employee and their attrition rate is 39.2% of all company (93 out of 237).\n",
    "\n",
    "**Attrition rate within its own distance group seems to increase as the distance from home increases.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Education**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling education levels\n",
    "table_df(x = 'Education')\n",
    "df2['Education'] = df2['Education'].map({1:'Below College', 2:'College', \n",
    "                                         3:'Bachelor', 4:'Master', 5:'Doctor'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Education</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bachelor</td>\n",
       "      <td>572</td>\n",
       "      <td>99</td>\n",
       "      <td>17.307692</td>\n",
       "      <td>41.772152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Below College</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "      <td>18.235294</td>\n",
       "      <td>13.080169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>College</td>\n",
       "      <td>282</td>\n",
       "      <td>44</td>\n",
       "      <td>15.602837</td>\n",
       "      <td>18.565401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Doctor</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>10.416667</td>\n",
       "      <td>2.109705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Master</td>\n",
       "      <td>398</td>\n",
       "      <td>58</td>\n",
       "      <td>14.572864</td>\n",
       "      <td>24.472574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Education  Total Employee   Attrition Number  \\\n",
       "0       Bachelor              572                99   \n",
       "1  Below College              170                31   \n",
       "2        College              282                44   \n",
       "3         Doctor               48                 5   \n",
       "4         Master              398                58   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             17.307692                      41.772152  \n",
       "1                             18.235294                      13.080169  \n",
       "2                             15.602837                      18.565401  \n",
       "3                             10.416667                       2.109705  \n",
       "4                             14.572864                      24.472574  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Education Level & Attrition Table\n",
    "table_df(x = 'Education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Education Level Graph\n",
    "graph_df(x = 'Education')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Employees who have bachelor degree** have the most attrition number (99 employees) in the company. That makes up 41.8% of all attrition in the company. **Employees who have Ph.D. degree** composes the least attrition number in the company.\n",
    "\n",
    "**Employees who have the master, college, and below college degrees** are follower of **employees who have bachelor degrees** in terms of the attrition number in the company respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Education Field**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>EducationField</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Human Resources</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>25.925926</td>\n",
       "      <td>2.953586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>606</td>\n",
       "      <td>89</td>\n",
       "      <td>14.686469</td>\n",
       "      <td>37.552743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marketing</td>\n",
       "      <td>159</td>\n",
       "      <td>35</td>\n",
       "      <td>22.012579</td>\n",
       "      <td>14.767932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Medical</td>\n",
       "      <td>464</td>\n",
       "      <td>63</td>\n",
       "      <td>13.577586</td>\n",
       "      <td>26.582278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other</td>\n",
       "      <td>82</td>\n",
       "      <td>11</td>\n",
       "      <td>13.414634</td>\n",
       "      <td>4.641350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>132</td>\n",
       "      <td>32</td>\n",
       "      <td>24.242424</td>\n",
       "      <td>13.502110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     EducationField  Total Employee   Attrition Number  \\\n",
       "0   Human Resources               27                 7   \n",
       "1     Life Sciences              606                89   \n",
       "2         Marketing              159                35   \n",
       "3           Medical              464                63   \n",
       "4             Other               82                11   \n",
       "5  Technical Degree              132                32   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             25.925926                       2.953586  \n",
       "1                             14.686469                      37.552743  \n",
       "2                             22.012579                      14.767932  \n",
       "3                             13.577586                      26.582278  \n",
       "4                             13.414634                       4.641350  \n",
       "5                             24.242424                      13.502110  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Education Field & Attrition Table\n",
    "table_df(x = 'EducationField')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Education Field Graph\n",
    "graph_df(x = 'EducationField', z = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Employees who have Life Science** education level have the most attrition number which makes up the 37.5% of all attrition (89 out of 237). But that composes only 14.7% of attrition within Life Sciences field. \n",
    "\n",
    "**Medical** education level have the second highest attrition number which makes up the 13.57% of all attrition (63 out of 237). But that composes only 14.7% of attrition within Life Sciences field. \n",
    "\n",
    "Besides that, **Human Resources, Technical Degree, and Marketing** fields are mostly affected by the attrition respectively. Their approximately 22-26% employees left the company. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Environment Satisfaction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling environment satisfaction\n",
    "table_df(x = 'EnvironmentSatisfaction')\n",
    "df2['EnvironmentSatisfaction'] = df2['EnvironmentSatisfaction'].map({1:'Low', 2:'Medium', \n",
    "                                                                     3:'High', 4:'Very High'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>EnvironmentSatisfaction</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High</td>\n",
       "      <td>453</td>\n",
       "      <td>62</td>\n",
       "      <td>13.686534</td>\n",
       "      <td>26.160338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>284</td>\n",
       "      <td>72</td>\n",
       "      <td>25.352113</td>\n",
       "      <td>30.379747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "      <td>287</td>\n",
       "      <td>43</td>\n",
       "      <td>14.982578</td>\n",
       "      <td>18.143460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Very High</td>\n",
       "      <td>446</td>\n",
       "      <td>60</td>\n",
       "      <td>13.452915</td>\n",
       "      <td>25.316456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EnvironmentSatisfaction  Total Employee   Attrition Number  \\\n",
       "0                    High              453                62   \n",
       "1                     Low              284                72   \n",
       "2                  Medium              287                43   \n",
       "3               Very High              446                60   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             13.686534                      26.160338  \n",
       "1                             25.352113                      30.379747  \n",
       "2                             14.982578                      18.143460  \n",
       "3                             13.452915                      25.316456  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Environment & Attrition Table\n",
    "table_df(x = 'EnvironmentSatisfaction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Environment Satisfaction Graph\n",
    "graph_df(x = 'EnvironmentSatisfaction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it may be expected, there is a high attrition rate in the **low satisfaction environment**. That composes the 30.4 % of the whole company's attrition.\n",
    "\n",
    "Shockingly, in the **high and  very high satisfaction environment**, there are still 13.7 % of the these each group's employees leave the company. That attrition composes of the 51.5 % of the whole company's attrition. This result might tell us that environment satisfaction is not the one of  the main reasons for attrition in the company. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Gender</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>588</td>\n",
       "      <td>87</td>\n",
       "      <td>14.795918</td>\n",
       "      <td>36.708861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>882</td>\n",
       "      <td>150</td>\n",
       "      <td>17.006803</td>\n",
       "      <td>63.291139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Total Employee   Attrition Number  \\\n",
       "0  Female              588                87   \n",
       "1    Male              882               150   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             14.795918                      36.708861  \n",
       "1                             17.006803                      63.291139  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gender & Attrition Table\n",
    "table_df(x = 'Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Gender Graph\n",
    "graph_df(x = 'Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Male employees** are more likely to leave the company than **female employees**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Job Involvement**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Job Involvement Groups\n",
    "table_df(x = 'JobInvolvement')\n",
    "df2['JobInvolvement'] = df2['JobInvolvement'].map({1:'Low', 2:'Medium', \n",
    "                                                   3:'High', 4:'Very High'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>JobInvolvement</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High</td>\n",
       "      <td>868</td>\n",
       "      <td>125</td>\n",
       "      <td>14.400922</td>\n",
       "      <td>52.742616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>83</td>\n",
       "      <td>28</td>\n",
       "      <td>33.734940</td>\n",
       "      <td>11.814346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "      <td>375</td>\n",
       "      <td>71</td>\n",
       "      <td>18.933333</td>\n",
       "      <td>29.957806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Very High</td>\n",
       "      <td>144</td>\n",
       "      <td>13</td>\n",
       "      <td>9.027778</td>\n",
       "      <td>5.485232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  JobInvolvement  Total Employee   Attrition Number  \\\n",
       "0           High              868               125   \n",
       "1            Low               83                28   \n",
       "2         Medium              375                71   \n",
       "3      Very High              144                13   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             14.400922                      52.742616  \n",
       "1                             33.734940                      11.814346  \n",
       "2                             18.933333                      29.957806  \n",
       "3                              9.027778                       5.485232  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Job Involvement & Attrition Table\n",
    "table_df(x = 'JobInvolvement')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Job Involvement Graph\n",
    "graph_df(x = 'JobInvolvement')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "59% of all employee's job involvement in the company is in the **high** category(868 out of 1470). The highest attrition number is also observed in high job involvement category. 125 employees in this group, which composes the 52.7% of all attrition, left the company. But that is only 14.4% of high job involvement category. \n",
    "\n",
    "**Medium** job involvement category is following the **high** category group in attrition number with 71 employee. \n",
    "\n",
    "**Low** job involvement category has the highest employee leaving proportion within individual category when it is compared to the other categories. 33.7% of **Low** Job involvement group left the company. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Job Level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling job levels\n",
    "table_df(x = 'JobLevel')\n",
    "df2['JobLevel'] = df2['JobLevel'].map({1:'Level-1', 2:'Level-2', \n",
    "                                       3:'Level-3', 4:'Level-4', 5:'Level-5'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>JobLevel</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Level-1</td>\n",
       "      <td>543</td>\n",
       "      <td>143</td>\n",
       "      <td>26.335175</td>\n",
       "      <td>60.337553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Level-2</td>\n",
       "      <td>534</td>\n",
       "      <td>52</td>\n",
       "      <td>9.737828</td>\n",
       "      <td>21.940928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Level-3</td>\n",
       "      <td>218</td>\n",
       "      <td>32</td>\n",
       "      <td>14.678899</td>\n",
       "      <td>13.502110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Level-4</td>\n",
       "      <td>106</td>\n",
       "      <td>5</td>\n",
       "      <td>4.716981</td>\n",
       "      <td>2.109705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Level-5</td>\n",
       "      <td>69</td>\n",
       "      <td>5</td>\n",
       "      <td>7.246377</td>\n",
       "      <td>2.109705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  JobLevel  Total Employee   Attrition Number  \\\n",
       "0  Level-1              543               143   \n",
       "1  Level-2              534                52   \n",
       "2  Level-3              218                32   \n",
       "3  Level-4              106                 5   \n",
       "4  Level-5               69                 5   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             26.335175                      60.337553  \n",
       "1                              9.737828                      21.940928  \n",
       "2                             14.678899                      13.502110  \n",
       "3                              4.716981                       2.109705  \n",
       "4                              7.246377                       2.109705  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Job Level & Attrition Table\n",
    "table_df(x = 'JobLevel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Job Level Graph\n",
    "graph_df(x = 'JobLevel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With an increase in job level, there is a decrease in attrition number throughout the company. The highest attrition is observed in the **job level-1**. 143 employees in the job level-1, who compose the 60.3% of all attrition, left the company. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Job Role**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>JobRole</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>131</td>\n",
       "      <td>9</td>\n",
       "      <td>6.870229</td>\n",
       "      <td>3.797468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Human Resources</td>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>23.076923</td>\n",
       "      <td>5.063291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>259</td>\n",
       "      <td>62</td>\n",
       "      <td>23.938224</td>\n",
       "      <td>26.160338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>102</td>\n",
       "      <td>5</td>\n",
       "      <td>4.901961</td>\n",
       "      <td>2.109705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>145</td>\n",
       "      <td>10</td>\n",
       "      <td>6.896552</td>\n",
       "      <td>4.219409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Research Director</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.843882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>292</td>\n",
       "      <td>47</td>\n",
       "      <td>16.095890</td>\n",
       "      <td>19.831224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>326</td>\n",
       "      <td>57</td>\n",
       "      <td>17.484663</td>\n",
       "      <td>24.050633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>39.759036</td>\n",
       "      <td>13.924051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     JobRole  Total Employee   Attrition Number  \\\n",
       "0  Healthcare Representative              131                 9   \n",
       "1            Human Resources               52                12   \n",
       "2      Laboratory Technician              259                62   \n",
       "3                    Manager              102                 5   \n",
       "4     Manufacturing Director              145                10   \n",
       "5          Research Director               80                 2   \n",
       "6         Research Scientist              292                47   \n",
       "7            Sales Executive              326                57   \n",
       "8       Sales Representative               83                33   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                              6.870229                       3.797468  \n",
       "1                             23.076923                       5.063291  \n",
       "2                             23.938224                      26.160338  \n",
       "3                              4.901961                       2.109705  \n",
       "4                              6.896552                       4.219409  \n",
       "5                              2.500000                       0.843882  \n",
       "6                             16.095890                      19.831224  \n",
       "7                             17.484663                      24.050633  \n",
       "8                             39.759036                      13.924051  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Job Role & Attrition Table\n",
    "table_df(x = 'JobRole')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Job Role Graph\n",
    "graph_df(x = 'JobRole', z = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Laboratory Technician** has the most attrition number with the 26.2% of all attrition in the company (62 out of 237 employees). **Sales Executive** and **Research Scientist** are following the **Laboratory Technician** in attrition throughout the company with the 57 and 47 employees respectively. Those both job roles' attrition composes 44% of whole company's attrition. \n",
    "\n",
    "**Sales Representative** role has been affected mostly by the attrition. Sales Representative has lost approximately 40% of its' employee. **Laboratory Technician** and **Human Resources** followed it in terms of losing employee as a job role.\n",
    "\n",
    "On the other hand, **Research Director** job role has the lowest attrition number not only in the company (2.5%) but only within its own job role(0.8%). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Job Satisfaction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Job Role \n",
    "table_df(x = 'JobSatisfaction')\n",
    "df2['JobSatisfaction'] = df2['JobSatisfaction'].map({1:'Low', 2:'Medium',\n",
    "                                                     3:'High', 4:'Very High'})                                               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>JobSatisfaction</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High</td>\n",
       "      <td>442</td>\n",
       "      <td>73</td>\n",
       "      <td>16.515837</td>\n",
       "      <td>30.801688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>289</td>\n",
       "      <td>66</td>\n",
       "      <td>22.837370</td>\n",
       "      <td>27.848101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "      <td>280</td>\n",
       "      <td>46</td>\n",
       "      <td>16.428571</td>\n",
       "      <td>19.409283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Very High</td>\n",
       "      <td>459</td>\n",
       "      <td>52</td>\n",
       "      <td>11.328976</td>\n",
       "      <td>21.940928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  JobSatisfaction  Total Employee   Attrition Number  \\\n",
       "0            High              442                73   \n",
       "1             Low              289                66   \n",
       "2          Medium              280                46   \n",
       "3       Very High              459                52   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             16.515837                      30.801688  \n",
       "1                             22.837370                      27.848101  \n",
       "2                             16.428571                      19.409283  \n",
       "3                             11.328976                      21.940928  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Job Satisfaction & Attrition Table\n",
    "table_df(x = 'JobSatisfaction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Job Satisfaction\n",
    "graph_df(x = 'JobSatisfaction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In **high** job satisfaction, surprisingly employees leave the company most and their attrition composes 30.8% of company's attrition. From this picture, I assume that job satisfaction should not be the main reason for employees to leave the company. \n",
    "\n",
    "As it may be expected, in **low** job satisfaction, employees leave the company more than other groups except **high** satisfaction. They compose 27.8% of all attrition in the company. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Marital Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>MaritalStatus</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>327</td>\n",
       "      <td>33</td>\n",
       "      <td>10.091743</td>\n",
       "      <td>13.924051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Married</td>\n",
       "      <td>673</td>\n",
       "      <td>84</td>\n",
       "      <td>12.481426</td>\n",
       "      <td>35.443038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Single</td>\n",
       "      <td>470</td>\n",
       "      <td>120</td>\n",
       "      <td>25.531915</td>\n",
       "      <td>50.632911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MaritalStatus  Total Employee   Attrition Number  \\\n",
       "0      Divorced              327                33   \n",
       "1       Married              673                84   \n",
       "2        Single              470               120   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             10.091743                      13.924051  \n",
       "1                             12.481426                      35.443038  \n",
       "2                             25.531915                      50.632911  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Job Satisfaction & Attrition Table\n",
    "table_df(x = 'MaritalStatus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Job Satisfaction\n",
    "graph_df(x = 'MaritalStatus')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Single** employees are more likely to leave the company. They have the highest attrition number and compose of the 50.6% employees who left the company. **Married** and **Divorced** employees are the followers of **Single** employees in the attrition number of the company respectively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Monthly Income**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Monthly Income Level\n",
    "table_df(x = \"MonthlyIncome\")\n",
    "bins = [1000, 2000, 3000, 4000, 5000, 7500, 9000, 11000, 15000, 20000]\n",
    "group_names = ['1000-2000', '2000-3000', '3000-4000', '4000-5000', '5000-7500', \n",
    "               '7500-9000', '9000-11000', '11000-15000', '15000-20000']             \n",
    "df2[\"MonthlyIncome\"] = pd.cut(df2[\"MonthlyIncome\"], bins, labels = group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>MonthlyIncome</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000-2000</td>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>7.594937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-3000</td>\n",
       "      <td>362</td>\n",
       "      <td>95</td>\n",
       "      <td>26.243094</td>\n",
       "      <td>40.084388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000-4000</td>\n",
       "      <td>148</td>\n",
       "      <td>24</td>\n",
       "      <td>16.216216</td>\n",
       "      <td>10.126582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4000-5000</td>\n",
       "      <td>206</td>\n",
       "      <td>26</td>\n",
       "      <td>12.621359</td>\n",
       "      <td>10.970464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000-7500</td>\n",
       "      <td>310</td>\n",
       "      <td>30</td>\n",
       "      <td>9.677419</td>\n",
       "      <td>12.658228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7500-9000</td>\n",
       "      <td>78</td>\n",
       "      <td>10</td>\n",
       "      <td>12.820513</td>\n",
       "      <td>4.219409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9000-11000</td>\n",
       "      <td>118</td>\n",
       "      <td>22</td>\n",
       "      <td>18.644068</td>\n",
       "      <td>9.282700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11000-15000</td>\n",
       "      <td>82</td>\n",
       "      <td>7</td>\n",
       "      <td>8.536585</td>\n",
       "      <td>2.953586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15000-20000</td>\n",
       "      <td>133</td>\n",
       "      <td>5</td>\n",
       "      <td>3.759398</td>\n",
       "      <td>2.109705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MonthlyIncome  Total Employee   Attrition Number  \\\n",
       "0     1000-2000               33                18   \n",
       "1     2000-3000              362                95   \n",
       "2     3000-4000              148                24   \n",
       "3     4000-5000              206                26   \n",
       "4     5000-7500              310                30   \n",
       "5     7500-9000               78                10   \n",
       "6    9000-11000              118                22   \n",
       "7   11000-15000               82                 7   \n",
       "8   15000-20000              133                 5   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             54.545455                       7.594937  \n",
       "1                             26.243094                      40.084388  \n",
       "2                             16.216216                      10.126582  \n",
       "3                             12.621359                      10.970464  \n",
       "4                              9.677419                      12.658228  \n",
       "5                             12.820513                       4.219409  \n",
       "6                             18.644068                       9.282700  \n",
       "7                              8.536585                       2.953586  \n",
       "8                              3.759398                       2.109705  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Monthly Income & Attrition Table\n",
    "table_df(x = \"MonthlyIncome\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Monthly Income Level Graph\n",
    "graph_df(x = \"MonthlyIncome\", z = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2000-3000 dollars monthly income** level, there is a high  attrition and it compose the 40% of attrition in the company. \n",
    "\n",
    "**1000-2000 dollars monthly income** level, there is a high  attrition in its own income group level, which is 54.5%. \n",
    "\n",
    "As the monthly income increase, it is observed that there is a decrease in attrition. But, in **9000-11000 dollars monthly income** level, there is a rise in  attrition of its own monthly income group level. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Numbers Companies Worked**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Numbers Companied Worked Groups\n",
    "table_df(x = 'NumCompaniesWorked')\n",
    "bins = [0, 1, 2, 3, 4, 5, 10]\n",
    "group_names = ['0', '1', '2', '3', '4', '5+']             \n",
    "df2['NumCompaniesWorked'] = pd.cut(df2['NumCompaniesWorked'], bins, labels = group_names, \n",
    "                                        include_lowest = True, right = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>NumCompaniesWorked</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>23</td>\n",
       "      <td>11.675127</td>\n",
       "      <td>9.704641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>521</td>\n",
       "      <td>98</td>\n",
       "      <td>18.809981</td>\n",
       "      <td>41.350211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>146</td>\n",
       "      <td>16</td>\n",
       "      <td>10.958904</td>\n",
       "      <td>6.751055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>159</td>\n",
       "      <td>16</td>\n",
       "      <td>10.062893</td>\n",
       "      <td>6.751055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>139</td>\n",
       "      <td>17</td>\n",
       "      <td>12.230216</td>\n",
       "      <td>7.172996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5+</td>\n",
       "      <td>308</td>\n",
       "      <td>67</td>\n",
       "      <td>21.753247</td>\n",
       "      <td>28.270042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NumCompaniesWorked  Total Employee   Attrition Number  \\\n",
       "0                  0              197                23   \n",
       "1                  1              521                98   \n",
       "2                  2              146                16   \n",
       "3                  3              159                16   \n",
       "4                  4              139                17   \n",
       "5                 5+              308                67   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             11.675127                       9.704641  \n",
       "1                             18.809981                      41.350211  \n",
       "2                             10.958904                       6.751055  \n",
       "3                             10.062893                       6.751055  \n",
       "4                             12.230216                       7.172996  \n",
       "5                             21.753247                      28.270042  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numbers Companied Worked & Attrition Table\n",
    "table_df(x = 'NumCompaniesWorked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Numbers of Companies Worked Graph\n",
    "graph_df(x = 'NumCompaniesWorked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If employees have **one company experience** before current company, they are more likely to leave the company. They have the highest attrition number and compose of 41.3% all attrition in the company. Besides, if **employees don't have any experience** in other company, they have the second most attrition number.  \n",
    "\n",
    "Also, employees, who has more experience such as **working in 5,6,7, and 9 companies** before the current company, have the highest attrition in their individual experienced group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Over Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>OverTime</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>1054</td>\n",
       "      <td>110</td>\n",
       "      <td>10.436433</td>\n",
       "      <td>46.413502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>416</td>\n",
       "      <td>127</td>\n",
       "      <td>30.528846</td>\n",
       "      <td>53.586498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  OverTime  Total Employee   Attrition Number  \\\n",
       "0       No             1054               110   \n",
       "1      Yes              416               127   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             10.436433                      46.413502  \n",
       "1                             30.528846                      53.586498  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numbers Companied Worked & Attrition Table\n",
    "table_df(x = 'OverTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Numbers of Companies Worked Graph\n",
    "graph_df(x = 'OverTime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "28.3% of employees have the over time work in the company and they have higher attrition number than employees who don't have. There is not a significant difference between these two groups' attrition number. But if you compare individually both groups, over time employees are much more likely to leave the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Percent Salary Hike**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Percent Salary Hike & Attrition Table\n",
    "table_df(x = 'PercentSalaryHike')\n",
    "bins = [10, 14, 18, 22, 25]\n",
    "group_names = ['11-14', '15-18', '19-22', '23-25']             \n",
    "df2['PercentSalaryHike'] = pd.cut(df2['PercentSalaryHike'], bins, labels = group_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>PercentSalaryHike</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-14</td>\n",
       "      <td>818</td>\n",
       "      <td>132</td>\n",
       "      <td>16.136919</td>\n",
       "      <td>55.696203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15-18</td>\n",
       "      <td>350</td>\n",
       "      <td>59</td>\n",
       "      <td>16.857143</td>\n",
       "      <td>24.894515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19-22</td>\n",
       "      <td>235</td>\n",
       "      <td>33</td>\n",
       "      <td>14.042553</td>\n",
       "      <td>13.924051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23-25</td>\n",
       "      <td>67</td>\n",
       "      <td>13</td>\n",
       "      <td>19.402985</td>\n",
       "      <td>5.485232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PercentSalaryHike  Total Employee   Attrition Number  \\\n",
       "0             11-14              818               132   \n",
       "1             15-18              350                59   \n",
       "2             19-22              235                33   \n",
       "3             23-25               67                13   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             16.136919                      55.696203  \n",
       "1                             16.857143                      24.894515  \n",
       "2                             14.042553                      13.924051  \n",
       "3                             19.402985                       5.485232  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percent Salary Hike & Attrition Table\n",
    "table_df(x = 'PercentSalaryHike')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Percent Salary Hike\n",
    "graph_df(x = 'PercentSalaryHike')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it may be expected, the higher percent salary hike is, the more employees are likely and willingly to stay in the current company. The employees who have the highest percent salary hikes are more likely to leave the company. The reason for that might be due to the fact that they are more qualified and have the chance to find better position in other companies or due to the retirement. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performance Rating**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>PerformanceRating</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1244</td>\n",
       "      <td>200</td>\n",
       "      <td>16.077170</td>\n",
       "      <td>84.388186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>226</td>\n",
       "      <td>37</td>\n",
       "      <td>16.371681</td>\n",
       "      <td>15.611814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PerformanceRating  Total Employee   Attrition Number  \\\n",
       "0                  3             1244               200   \n",
       "1                  4              226                37   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             16.077170                      84.388186  \n",
       "1                             16.371681                      15.611814  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Performance Rating & Attrition Table\n",
    "table_df(x = 'PerformanceRating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Performance Rating\n",
    "graph_df(x = 'PerformanceRating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance rating has two category such as 3 and 4. 84.6% of employees are in the **performance rating 3 group** and 16.08 attrition. The rest of the employees are in **performance rating 4 group** and their attrition percentage(%16.37) is a little bit higher than previous group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Relationship Satisfaction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Relationship Satisfaction Levels\n",
    "table_df(x = 'RelationshipSatisfaction')\n",
    "df2['RelationshipSatisfaction'] = df2['RelationshipSatisfaction'].map({1:'Low', 2:'Medium', \n",
    "                                                                       3:'High', 4:'Very High'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>RelationshipSatisfaction</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High</td>\n",
       "      <td>459</td>\n",
       "      <td>71</td>\n",
       "      <td>15.468410</td>\n",
       "      <td>29.957806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>276</td>\n",
       "      <td>57</td>\n",
       "      <td>20.652174</td>\n",
       "      <td>24.050633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Medium</td>\n",
       "      <td>303</td>\n",
       "      <td>45</td>\n",
       "      <td>14.851485</td>\n",
       "      <td>18.987342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Very High</td>\n",
       "      <td>432</td>\n",
       "      <td>64</td>\n",
       "      <td>14.814815</td>\n",
       "      <td>27.004219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RelationshipSatisfaction  Total Employee   Attrition Number  \\\n",
       "0                     High              459                71   \n",
       "1                      Low              276                57   \n",
       "2                   Medium              303                45   \n",
       "3                Very High              432                64   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             15.468410                      29.957806  \n",
       "1                             20.652174                      24.050633  \n",
       "2                             14.851485                      18.987342  \n",
       "3                             14.814815                      27.004219  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Relationship & Attrition Table\n",
    "table_df(x = 'RelationshipSatisfaction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Relationship Satisfaction Graph\n",
    "graph_df(x = 'RelationshipSatisfaction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relationship satisfaction is aligned with high and very high in the company. But, still **High** and **very high** relationship satisfaction level have the most attrition number respectively and compose of 52.7% all attrition in the company. \n",
    "\n",
    "Besides that, the highest attrition percentage in the satisfaction group is **low** as it might be expected (20.7% of attrition in the low relationship satisfaction level. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Stock Option Level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>StockOptionLevel</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>631</td>\n",
       "      <td>154</td>\n",
       "      <td>24.405705</td>\n",
       "      <td>64.978903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>596</td>\n",
       "      <td>56</td>\n",
       "      <td>9.395973</td>\n",
       "      <td>23.628692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>12</td>\n",
       "      <td>7.594937</td>\n",
       "      <td>5.063291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>85</td>\n",
       "      <td>15</td>\n",
       "      <td>17.647059</td>\n",
       "      <td>6.329114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StockOptionLevel  Total Employee   Attrition Number  \\\n",
       "0                 0              631               154   \n",
       "1                 1              596                56   \n",
       "2                 2              158                12   \n",
       "3                 3               85                15   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             24.405705                      64.978903  \n",
       "1                              9.395973                      23.628692  \n",
       "2                              7.594937                       5.063291  \n",
       "3                             17.647059                       6.329114  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Relationship & Attrition Table\n",
    "table_df(x = 'StockOptionLevel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Stock Option Level Graph\n",
    "graph_df(x = 'StockOptionLevel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If **stock option level** is 0, there occurs a huge attrition in the company and it composes the 65% of the all attrition in the company. Besides, as the stock option level increase, there is a decrease in attrition number. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Total Working Years**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total Working Years & Attrition Table\n",
    "table_df(x = 'TotalWorkingYears')\n",
    "bins = [0, 1, 5, 10, 15, 20, 40]\n",
    "group_names = ['1', '2-5', '6-10', '11-15', '16-20', '20+']             \n",
    "df2['TotalWorkingYears'] = pd.cut(df2['TotalWorkingYears'], bins, labels = group_names, \n",
    "                                  include_lowest = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>TotalWorkingYears</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>45</td>\n",
       "      <td>48.913043</td>\n",
       "      <td>18.987342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-5</td>\n",
       "      <td>224</td>\n",
       "      <td>46</td>\n",
       "      <td>20.535714</td>\n",
       "      <td>19.409283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6-10</td>\n",
       "      <td>607</td>\n",
       "      <td>91</td>\n",
       "      <td>14.991763</td>\n",
       "      <td>38.396624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11-15</td>\n",
       "      <td>191</td>\n",
       "      <td>24</td>\n",
       "      <td>12.565445</td>\n",
       "      <td>10.126582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16-20</td>\n",
       "      <td>149</td>\n",
       "      <td>15</td>\n",
       "      <td>10.067114</td>\n",
       "      <td>6.329114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20+</td>\n",
       "      <td>207</td>\n",
       "      <td>16</td>\n",
       "      <td>7.729469</td>\n",
       "      <td>6.751055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  TotalWorkingYears  Total Employee   Attrition Number  \\\n",
       "0                 1               92                45   \n",
       "1               2-5              224                46   \n",
       "2              6-10              607                91   \n",
       "3             11-15              191                24   \n",
       "4             16-20              149                15   \n",
       "5               20+              207                16   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             48.913043                      18.987342  \n",
       "1                             20.535714                      19.409283  \n",
       "2                             14.991763                      38.396624  \n",
       "3                             12.565445                      10.126582  \n",
       "4                             10.067114                       6.329114  \n",
       "5                              7.729469                       6.751055  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Working Years & Attrition Table\n",
    "table_df(x = 'TotalWorkingYears')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_df(x = 'TotalWorkingYears')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Employees who have one year or less working experience** are more likely to leave the company and compose the 18.98% of all attrition throughout the company. In addition to that, **employees who have 6-10 years' experience** have also second highest attrition percentage throughout the company and it compose the 38.4% of all attrition. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training Times Last Year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>TrainingTimesLastYear</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>15</td>\n",
       "      <td>27.777778</td>\n",
       "      <td>6.329114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>9</td>\n",
       "      <td>12.676056</td>\n",
       "      <td>3.797468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>547</td>\n",
       "      <td>98</td>\n",
       "      <td>17.915905</td>\n",
       "      <td>41.350211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>491</td>\n",
       "      <td>69</td>\n",
       "      <td>14.052953</td>\n",
       "      <td>29.113924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>123</td>\n",
       "      <td>26</td>\n",
       "      <td>21.138211</td>\n",
       "      <td>10.970464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>14</td>\n",
       "      <td>11.764706</td>\n",
       "      <td>5.907173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>9.230769</td>\n",
       "      <td>2.531646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TrainingTimesLastYear  Total Employee   Attrition Number  \\\n",
       "0                      0               54                15   \n",
       "1                      1               71                 9   \n",
       "2                      2              547                98   \n",
       "3                      3              491                69   \n",
       "4                      4              123                26   \n",
       "5                      5              119                14   \n",
       "6                      6               65                 6   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             27.777778                       6.329114  \n",
       "1                             12.676056                       3.797468  \n",
       "2                             17.915905                      41.350211  \n",
       "3                             14.052953                      29.113924  \n",
       "4                             21.138211                      10.970464  \n",
       "5                             11.764706                       5.907173  \n",
       "6                              9.230769                       2.531646  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training Times Last Year & Attrition Table\n",
    "table_df(x = 'TrainingTimesLastYear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Training Times Last Year Graph\n",
    "graph_df(x = 'TrainingTimesLastYear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employees who has **2 and 3 times training last year** has the most attrition number respectively and both of their attrition compose the 70.5% of all attrition in the company. Employees who don't have training time beforehand has the highest attrition number in its individual group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Work Life Balance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Work Life Balance Groups\n",
    "table_df(x = 'WorkLifeBalance')\n",
    "df2['WorkLifeBalance'] = df2['WorkLifeBalance'].map({1:'Bad', 2:'Good', \n",
    "                                                     3:'Better', 4:'Best'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>WorkLifeBalance</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bad</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>10.548523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Best</td>\n",
       "      <td>153</td>\n",
       "      <td>27</td>\n",
       "      <td>17.647059</td>\n",
       "      <td>11.392405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Better</td>\n",
       "      <td>893</td>\n",
       "      <td>127</td>\n",
       "      <td>14.221725</td>\n",
       "      <td>53.586498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Good</td>\n",
       "      <td>344</td>\n",
       "      <td>58</td>\n",
       "      <td>16.860465</td>\n",
       "      <td>24.472574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WorkLifeBalance  Total Employee   Attrition Number  \\\n",
       "0             Bad               80                25   \n",
       "1            Best              153                27   \n",
       "2          Better              893               127   \n",
       "3            Good              344                58   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             31.250000                      10.548523  \n",
       "1                             17.647059                      11.392405  \n",
       "2                             14.221725                      53.586498  \n",
       "3                             16.860465                      24.472574  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Work Life Balance & Attrition Table\n",
    "table_df(x = 'WorkLifeBalance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attrition compared to Work Life Balance Graph\n",
    "graph_df(x = 'WorkLifeBalance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, work life balance is satisfactorily good throughout the company. But we have the highest attrition number and percentage throughout the company. \n",
    "\n",
    "Besides, bad work life balance group has highest attrition percentage in its individual group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Years at Company**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Years at Company Groups\n",
    "table_df(x = 'YearsAtCompany')\n",
    "bins = [0, 1, 5, 10, 40]\n",
    "group_names = ['1', '2-5', '6-10', '10+']             \n",
    "df2['YearsAtCompany'] = pd.cut(df2['YearsAtCompany'], bins, labels = group_names, \n",
    "                               include_lowest = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>YearsAtCompany</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "      <td>75</td>\n",
       "      <td>34.883721</td>\n",
       "      <td>31.645570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-5</td>\n",
       "      <td>561</td>\n",
       "      <td>87</td>\n",
       "      <td>15.508021</td>\n",
       "      <td>36.708861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6-10</td>\n",
       "      <td>448</td>\n",
       "      <td>55</td>\n",
       "      <td>12.276786</td>\n",
       "      <td>23.206751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10+</td>\n",
       "      <td>246</td>\n",
       "      <td>20</td>\n",
       "      <td>8.130081</td>\n",
       "      <td>8.438819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearsAtCompany  Total Employee   Attrition Number  \\\n",
       "0              1              215                75   \n",
       "1            2-5              561                87   \n",
       "2           6-10              448                55   \n",
       "3            10+              246                20   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             34.883721                      31.645570  \n",
       "1                             15.508021                      36.708861  \n",
       "2                             12.276786                      23.206751  \n",
       "3                              8.130081                       8.438819  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Years at Company & Attrition Table\n",
    "table_df(x = 'YearsAtCompany')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Years at Company & Attrition Graph \n",
    "graph_df(x = 'YearsAtCompany')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employees who have one year or less working experience in the company has the highest attrition percentage in its individual experience group (34.9% of attrition in the relevant group). Besides that, highest attrition number, which is 87 employees, is in the 2-5 years working experience at the company and that composes of the 36.7 % of all attrition in the company. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Years in Current Role**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Years in Current Role Groups\n",
    "table_df(x = 'YearsInCurrentRole')\n",
    "bins = [0, 1, 4, 6, 7, 10, 18]\n",
    "group_names = ['1', '2-4', '5-6', '7', '8-10', '10+']             \n",
    "df2['YearsInCurrentRole'] = pd.cut(df2['YearsInCurrentRole'], bins, labels = group_names, \n",
    "                                   include_lowest = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>YearsAtCompany</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "      <td>75</td>\n",
       "      <td>34.883721</td>\n",
       "      <td>31.645570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-5</td>\n",
       "      <td>561</td>\n",
       "      <td>87</td>\n",
       "      <td>15.508021</td>\n",
       "      <td>36.708861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6-10</td>\n",
       "      <td>448</td>\n",
       "      <td>55</td>\n",
       "      <td>12.276786</td>\n",
       "      <td>23.206751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10+</td>\n",
       "      <td>246</td>\n",
       "      <td>20</td>\n",
       "      <td>8.130081</td>\n",
       "      <td>8.438819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearsAtCompany  Total Employee   Attrition Number  \\\n",
       "0              1              215                75   \n",
       "1            2-5              561                87   \n",
       "2           6-10              448                55   \n",
       "3            10+              246                20   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             34.883721                      31.645570  \n",
       "1                             15.508021                      36.708861  \n",
       "2                             12.276786                      23.206751  \n",
       "3                              8.130081                       8.438819  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Years at Company & Attrition Table\n",
    "table_df(x = 'YearsAtCompany')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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toMMRQoh4aoa1w7H2d6zdgrWP4JbtiEoSVEAO++tJpB5Wj1WvfMa2ZauDDkcIIeLFolSj/beUagzs81NQElRAkitVoO3dF2Nzcll8+0uysKEQoqy6HZiBUqNR6n1ghrctKklQAcoY2I30fp3Y+OV8fh07K+hwhBAiHlYDnYCXgVeATlj7qZ+CkqACpJSi7b2XolKSWXLnK+Ts3ht0SEIIEWvvYG0W1n6KtR9j7Qa/BSVBBaxai4Y0vXQwO1f/yg/Pfxh0OEIIEWtLUOoOlDpu/xpQUdaByiMzSSSAljcMZe17U1nx5GgantmfKvV8zQIihBBhUAvo513yWCDqbOZxTVBa63nAVu/mj7jx8E8C2cB4Y8zdWusk4FmgA7AHuMwYsyKecSWaCjWq0erW81hwwzMsu/c1Oj37j6BDEkKI2DiI9aDi1sWnta4MYIzJ9C4XA88D5wBHAj201p2BU4DKxphewL+BR+MVUyJrdHZ/arRvxtrRU/ntm2VBhyOEEAdPqUEoleldfw+lJqHUhESYSaIDkKq1Hq+1nqS1PhqoZIxZaYyxuLXtB+CS1ecAxpiZQNc4xpSwVHLyH/P0DXsRm5sbcERCCHEQlLoUeAjY5W1pjVsT6lvgFj+7iGcX305gODACaAGMBbZE3L8NN/VFdf7oBgTI0VqnGGOyC9txzZqppKQkxz7iOEhPT/P/2MHdWX/OAH54ayJbPp1Oy0sGxTEyEak4/6eySuog9sp5nV4PHIu1673be7F2KkrNBuZ79xcpngnqe2CF11r6Xmu9FXewLE8aLmGletfzJBWVnAA2b94Z61jjIj09jaysbcUqc9hNZ7Pqg6/55t8vUq1vJypUrxqn6EpDeD6cxf0/lTUlea+KooWlTuOYRFVEcgJ3HhRYuwuldhVc5EDx7OK7BO94kta6Pi4R7dBaH661VrgVFb8CpgEneI/rCSyMY0wJr0r9Q2n+99PZu3Eryx8bFXQ4QghRUhVRquL+W9Y+BYBSlQHlZwfxTFAvAYdorb8G3sElrMuAN4HZwDxjzCzgA2C31no68DhQ7oewNbtqCFUaZfDji5+wfcXaoMMRQoiS+BB4EqX+OB6jlMId+vnIzw5UGOeAy8raFoqgD6aJ/8unM5hzycOkD+hMj7fuiHFkpSMjIzxdfBs2JH5XTDyFpTsqTMJSp+npab5aM8WmVCquAdIat8SGBXoAi4G/YG3UqXNkJokEVfeEntQ+8giyJs7l1wnfBh2OEEIUj7U7sfY44EzcBLEzgfOwdrCf5ASSoBKWUoq2912GSk5iyR0vk7vX1+z0QgiRWKydgbWPe5evi1NUElQCq966CU0uPJ4dP6zjxxc/CTocIYQoVZKgElzLf51NhVppLH9sFLs3bA46HCGEKDWSoBJcxZpp6JvPIXv7Lpbd/3rQ4QghRPEo9edZI5R6wE9Rmc08BJqcP5BVr45jzduTaHLh8dTs3DLokIQQomhKPQRkACejVIuIeyrgRvPdGm0XkqBCQCUn0+7+y5jxl9tYPGwEfT59CJUkjV8hRPxprZOBFwEN5AAX4060HYkbOr4IuMYYk38C0dFAG9ycq1MjtmcD9/h5bvmWC4navdtR7+TebJn7PWtHT41eQAghYuMkAGNMH+AO4DHvcpsx5ihcshryp1LWfoO1rwLtsfbViMubWLvczxOH8kTd7OwcG5bJYmNp+6pfeb/1RVSsmcZpy0ZSIS016JCEEOEX9UTdvAm8tdYXAn2AwUBDY4zVWg8BBhpjril47+pC3LR3NSOez2Jt1C/xUHbxleXJYouUmkqza//C8uHvMOO2kbS+7fzY7TsOZCaJ8AjLrAdhEpY69TNZrJecXgX+ApwOnOhNBA5uZYoaRRS/A8jE2kXFjU26+EKm+TWnUrnBofz43w/Z8eMvQYcjhCgnjDEXAi1xx6OqRNyVtzJFYdaVJDmBJKjQSU6tRJs7LiJ3bzZL7nwl6HCEEGWc1vp8rXXeUPGdQC7wrdY609s2CLcyRWHmeKvpXo5SF+y/+BDKLr7yrt6QPtQaOZZfx80ma8o80jM7BR2SEKLseh94RWv9JW6I+PXAUuBFrXVF7/p7RZSvgesG7BWxzQKvRXviUA6SKA+zmUezddEPfHXsjVRrXp+jJz1BUoXE+60hx6DCIyzHS8IkLHUat9nMi6JUFayNumihdPGFVI12zWh83rFs/34NP738WdDhCCFEwZQ6CaXmo9RKlPoBpVYBq/wUTbyf3cK3Vv8+l18+/Jrvh79Ng9P6UunQogbSiLIgvq3S2O67vLdKxX6PA38FbgDuB04BqvopKC2oEKtYuzotbzqb7N93Yh58I+hwhBCiIFuwdjJuPagaWHsz0N9PQUlQIdfkouOpphux+s0v2LpgZdDhCCFEfrtQqiVuMEUmSlUEKvopKAkq5JIqpND23svAWhbfNoIwDnoRQpRpw4D7gE9w8/L9CozxU1ASVBmQ3rcDdQb14LdZS1k3plgLVgohRLxtxdqhWLsHa7sBzbD2Rj8FJUGVEW3uupikShVYes9IsnfsDjocIYTI8xJKLUSpm1GqEdb6XnlVElQZUbVpXZpdOYTd6zax8un3gw5HCCEca7sCp+KOO32GUpNR6hI/RSVBlSHN/34alerWYuWzY9i5+tegwxFCCMctr/EY8CBQHfjzKrsFkARVhqRUrULr2y8gd/deltw9MuhwhBAClPoLSr0LLAOOBK7D2hZRSgGSoMqcBqf1pWZXzfpPZrDx64VBhyOEEOcBb+AGR1yNtdP9FpQEVcYopWh7/19BKRYPe5Hc7JygQxJClGfWngZsBC5FqYoodbTfopKgyqBDOjan0Vn92bZsNatfGxd0OEKI8kypv+POg/onbj6t/6KUDDMvz1rdej4paamYh99i72+/Bx2OEKL8ugg4DtiBtZuAboCM4ivPKmUcQosbhrJvy3bMI/8LOhwhRPmVg7V7I27vBnwde5AEVYYddulgqjZvwKpXx/H7kp+CDkcIUT5NRanhQFWUOgX4CJjop2Bcl9vQWmcAc4BjgWxgJG4lxUXANcaYXK31ncBg7/7rjTGz4xlTeZJUsQJt77mE2efcy+LbXqLn6HtQqvTXJhNClGs34ZbbmA9cAHwGPO+nYNxaUFrrCsB/gbxVEx8DbjPGHAUoYIjWujPQF+gBnAU8E694yquMAV3IOKYrm6YtZP2nM4IORwhRXijVGKUaAw2Bsbj1oK7HtaDq+9lFPLv4huOy5Drvdhdgqnd9LHAM7qSt8cYYa4xZDaRordPjGFO51ObeS1AVUlhy1yvk7NoTdDhCiPJhKjDF+zs13+0pfnYQly4+rfVFQJYxZpzWOm9KC2WMyVsLYhtQAzflxaaIonnbs4raf82aqaSkJMc26DhJT4/nCqh+Y9Bs/PupLBo+ivWvjqXj7ecHHVLCSYT/U1kjdVrO68Daww52F/E6BnUJYLXWxwAdgdeAjIj704AtwO8cuM503vYibd68M3aRxlF6ehpZWYmx7HWDK09h+avjmf/QW9Q6qQ9VGpRGQzU8H85E+T9FJ3UaFon0+S9K3JOoUjWBR4DDgdNxvWv/xNqo3/Vx6eIzxhxtjOlrjMkEvsMdGBurtc70HjII+AqYBhyntU7SWjcGkowxG+MRU3lXIS2VVsPOJ3fXXpbe81rQ4Qghyo8XgW+A2sB24BfgTT8FfScopQ76p9sNwN1a6xm4adffM8bMwSWqGcBo4JqDfA5RhIZn9qNGxxasG/MVm2YuDjocIUT5cBjWvgDkYu1erB2GGzgRlYq2RLhStAf+hzte1BOYBAy1lvkHF3PJZWVtC8W65onYxN/8rWHa4Jup3u4wjho/HJUcv2N5GRnh6Y7asCGx/k+FkToNj0T8/BckPT0tvueeKDULGAhMxtrOKNUCGIW1naIV9dOCego4E9hoLWtxwwRfOJh4RXBqdtU0OCOT3xf9yOo3vwg6HCFE2XcnbtReE5QaA3wN3O6noJ8EVdVaFuXdsJaxQKUSBCkSROvbLiA5tTLmoTfZu2V70OEIIcoyaz/HTdZwAfAy0B53sm5UfhLUZqVoh5sBAqU4Ex8j7UTiqly3Fi3+cQZ7N/3O8uFvBx2OEKIsUqoKSl2BUmdg7Uas/RRrPwI6Awv87MJPgroaNwqjjVJsBP4NXFnioEVCOOyKk0ltWpefXv6MbebnoMMRQpQ9r+JOOboXpa5GqQyU+hR4G3jLzw6iJihrWW4tvYA6QEtr6WQtyw4mahG85EoVaHP3JdicXJbc8RLRBssIIUQxdQP6AEcDFwOzgJ+B5lj7gJ8dRE1QStFIKcYCM4EKSjFeKZqUPGaRKOoc1430zI5kTfmOX8d9E3Q4QoiyZQvWZmPtBqARcAPWXom1Rc4UFMnPTBIv4Eby3Q9sAD7ANd0yix+vSCRKKdrceylf9rueJXe+THq/TiRXqhB0WEKIBOJN/P0y0BQ3QO4+YA3wMbDce9hzxph38hWN7Jb5FWvfL+5z+zkGlW6tG3FhLdZangMOKe4TicSU1rIRTS85gZ0/refH/34UdDhCiMRzHrDJW4liEPA0bqDDY8aYTO+SPzkBVESpRijVBEj2rjeOmOU8Kj8n6n4NDAU+sZbOStELeNJauhfjBcZUdnaODctksWGwZ8t2Rre8gJxdezjNvEpq/UODDkkIUXqKPFFXa10NN9n3Nq11bdy0ReMAjeuFW45by+/As5KV+gnILWT/FmubRQ3MR4LqgevmawYYoC5uJonp0XYeLzKTROytem0cC296jgZnZNLp6etjsk+Z9SD2pE7DIyyff78zSWit03BrOb2I6+pbYIyZo7UeBtQ0xtwY69j8dPH9gBuNcSRwOdA8yOQk4qPxucdQ/YhmrH13Cpu/NUGHI4RIIFrrRsBk4HVjzFvAB95cquDGJUSdtqgk/CSoGcC7uBbUAmvZHY9ARLBUcjJt77sUgEXDRmBzcwOOSAiRCLTWdYDxwM3GmJe9zeO01nmHeQYAcwosfJD8nAfVHHgCGAJ8rxRPKEXHeAQjglW7Z1vqn3IUW79bzpp3JgcdjhAiMdwK1ARu11pP0VpPAf4JPOFd74Mb2RdzUY9B7X+gohJwBvAgLrGtBa6xllI/gUaOQcXPrrVZTO5zDRWqpZI541kqpKWWeF9yvCT2pE7DIyyf/1KYzbwicBNuUMW1uAnHH8LavdGK+jlRt69SvIQ7FjUAONdaGuCORxV7XLtIbFUapNP8b6exJ2sLyx8fFXQ4Qojwewaoihuang00x51XFZWfY1APAtMBbS0XW8uXANbyHfBkicIVCe3wq06hSqMMfnzhE7avXBt0OEKIcOuCtbcC+7B2J3Ah+DtM5OcYVG/c0uznKcW1SnFExH3DSxiwSGDJVSrR5q6LsPuyWXLHK0GHI4QIN+t18+UdmjmUA2eZKJSfLr6zgbFAG1wf4idKcVHJ4hRhUXdwL2r3OYINX3zLholxGaAjhCgfngC+AOqi1BPAt8Djfgr66eK7GehuLX+zluuArkDMT8gSiUUp5YadJyWx+PaXyN27L+iQhBBhZO3ruCWa7seNZTgJa2N2DCrZWvbPPutdl5NkyoHqbZrS5KLj2bFyHT++9GnQ4QghwkipCkAT4HfcYrcdUeoCP0X9JKiFSjFcKVp7l+HAwpJHK8JE/+tsKtRMY/mjo9izQRZSFkIU27vAXbhR4P28S6afgn4S1F+9x72FWwkxGbiqBEGKEKpYMw39r7PJ3raTZQ+8HnQ4QojwaYW1PbD2Iqy92Ltc4qegn1F8O6zln95Kuh2s5R/W8vvBxyzCovEFx5HWugk/vz2JLd+tCDocIUS4rPS7vEZ+hSYopdinFHsLuOxTiqhnAIuyIyklmbb3XQbWsnjYi7I8vBAiOqUmo9Qk3PGnhSj1JUpN2n/xoagVdSvHJEhRJhx65BHUPbEX6z+ZwdrRU2l4embQIQkhEttdB7uDQltQ1pJjLTm4KSoexi1SNQO4HZB1wcuhNndeRFLliiy99zWyd+wKOhwhRCKzdirWTgVO33/9j22xOQYFvIEbGHEpbnDEobgFq0Q5k9q4DodffQp71v/GiidHBx2OECKRKTXC68q78ICuPaW+xOf6UUV18eVpZi0rzL5gAAAgAElEQVQnR9yeoxSLSxKvCL/Drz2Vn9+eyA/Pf0ijc46hatO6QYckhEhM9wFNcXO23h2xPRtY6mcHflpQS5WiV94NpWgLyFCuciqlamVa33ERuXv2seQumadPCFEIa3/C2ilY2yFfF980rP3Nzy78JKjDga+VYqFSfAfMA7opxXKl+P5g4hfhVP+UI6nVsw2/jp1F1tT5QYcjhCij/HTxnVGSHWutk3HHqjSQA1wMKGAkbibbRcA1xphcrfWdwGBc0+96Y8zskjynKB1unr7L+OrYG1h8+wiOnvg4SRX8vJWEEMI/Py2o1UBLoEfkxVpWWsvKIsqdBGCM6QPcATzmXW4zxhyFS1ZDtNadgb7efs/CLW4lElyNI5rR+Lxj2W5+ZtXIz4MORwiRyJSqjlKNUKrx/osPfhLUp8AtwKCIy/HRChljxuBW3QV3otavQBdgqrdtLHAMcCQw3hhjjTGrgRStdbqf4EWw9L/PJaV6Kt//53/s2bg16HCEEIlIqVuBNcCXuO//qcAUP0X99MvUtZb2JYnLGJOttX4V+AtwOnCiMSZvGoJtQA2gOrApolje9iwKUbNmKikpySUJqdSlp6cFHUL8pKfR+a6LmP3PZ1n95Lv0fv4fQUdUYmX6/xQQqVOpA8+lwOFYW+h3emH8JKgpSpFprb+Ml58x5kKt9c3ALKBKxF1puKnXf/eu599eqM2bd5YklFKXnp5GVta2oMOIq0OH9qfa8x9hXvyUjDP7U6Nds4h7w/PhDM//Seo0LMLy+S+FJLoa8DVqLz8/XXzLgUlKkV2cufi01udrrW/xbu7ErSH1rdY609s2CPgKt5z8cVrrJK11YyDJGLOx2K9EBCKpQgpt773Um6dvhMzTJ4TIbznwNUo9gFJ37L/44CdBXQU0ByrhWkCVObAlVJj3gU5a6y+BccD1wDXA3VrrGUBF4D1jzBxcopoBjPYeI0IkPbMTdY7rzm8zl/DLh9OCDkcIkVjWAp8De3CD4/IuUalov3iVYhJworUkTL9aVta2UPxMD0sTPxZ2/PQLU4+6jorph9Dv62dITq1ERkZ4uqM2bAjH/0nqNDzC8vlPT0/zlSyC4KcFtQFYoBQjleKFvEu8AxPhUrVpPQ67Ygi7125kxTPvBx2OECJoSs31/uaiVE7Exd32wc8giQneRYgitbj+dNaMmsTKpz+g0VkDCNMBfSFEjFnb2fvrpyFUoKIWLKzr9s1L+S/IXHyiACnVqtD69gvI3b2XpfeMDDocIUTIFZXZPsu7ohSj8t33eHzCEWHX4LS+HNJF88tH09F8F3Q4QogQKypBRR44a1HEfULsp5KSaHf/ZQCcwzMk4aurWQgh/qSoY1C2kOsF3RZiv0M6tXDHoN6eyNH2U6YcsJyYECJMtNYVgJdxaztVwq3ztIQCJv4ucAdKVQRuwk0cfi3ulKOHsDbq+bQlPnglRFH0sPPYZVM5lZepyu9BhyOEKLnzgE3eJN+DgKcpYOLvIso/A1QFOuNWrGiOS3hRFZWg6irFrUpxa+R1pRgG1PGzc1F+Vc6oycecRzW1jSG8GnQ4QoiSexe4PeJ2NgVP/F2YLlh7K7APa3cCFwId/TxxUV18L/HHjBGR18Fn9osXmSw2HD7ZcypjjviUY3/4iKfnnUjNdocFHVIRwvF/CtdMUuGo03gqC59/Y8x2AK11GvAecBswvICJvwtjvW6+vMcfis/DRIUmKGsPyJgJRSaLDYeMjDTaczXXq2E8cMQzDOc/JOr4mrDMeiAzSYRHWD7/fpKo1roR8AHwrDHmLa31IxF3R5vg+wngC6AuSj2BW93ibj+xyTEoEVcL6MkC2502ah6d+TrocIQQxaS1rgOMB242xuT1ns0rYOLvgln7OnAlcD/wA3AS1vrqhZN1ukXcvc3VtLFzOJPnWUAPsqkYdEhCCP9uBWoCt2ut83rW/g78n9a6IrAU1/VXMKUu8K7lNSc7oFRLYBnWLirqiSVBibhbTyO+4FSOV+9yvH2XTzg36JCEED4ZY/6OS0j59fW5iyFAJ1wXoQJOxM1wXg2l3sLaQid+KDRBKcVyCj6QpQBrLS19BicEH3MevewEBvMW0xjIZtKDDkkIUTrqAp2x1h2nUuou4GOgFzCHImYmKqoFdXzs4hPl3S6qMZrLuEQN53T7Ii9ya9AhCSFKRzp/dO8B7AJqYW02ShU5mq+oUXwrAZSiInAcUA3XekoGDgPuOcigRTkzjePoZz+ml5rIZHsyK2gXdEhCAPEcHRnb/YZ0ZORoYBJKjcINzDsNGOMdm/qlqIJ+RvG9BfwL+A9wCjAcaH9Q4YpyyZLEW96CyefwNErm6ROi7LP2Flz+aIlr3DyCtbcD3wPnFFXUT4LqBByNW8L9AaAP0Phg4hXl10raMt0eQ1O1nCP5POhwhBClYy1upN8YYBdKXYK1M7F2c1GFfK2oay0WWAYcYS0rcBMGClEi73EZu21lTuNlqrA96HCEEPGk1IvAKFxyegA3QOJMP0X9JKjFSvEEbt6lfyjFjT7LCVGgLaTzKedQXW3hZF4LOhwhRHwdA7TBzel3OZDJgVPnFcpPorkSGGMti4F7cX2IciKLOCjjOIMNth4DGEM9VgUdjhAiftZh7T7cCb3tsfZbip67bz8/CWq4tUwBsJYPrOUa3HoeQpRYNhV5hytJUTmcxXPIEmNClFlrUeoWYDpwBUqdhc/DREWdqPsCboGqHkrRJl8ZOctSHLR59GGx7cwR6hs62JnMp1fQIQkhYu9SYDDWfoNS7wNn43rmoiqqBfUw8Aiwxrued7kb6HdQ4QoBgOJ/XEOOTeIsniOFqAtsCiHC51qsfRsAa5/C2iHAQD8Fo52ouxJoqxStcAe2UoCp1rLxYCMWAmAdTZnEEI5VH3CMfZ/POSvokIQQsaDUQ0AGcDJKtYi4JwXoCdGnk4l6DEopzsatmNgat6b8x0pxUUniFaIgH3Ih22x1TuYNarAp6HCEELExGjf6e4f3N+8yDhjsZwd+ZjO/GehuLVkASnEPMBkYWfx4hfiznaTxPpdwoXqC0+wIXubmoEMSQhwsa78BvkGpMVi7tSS78JOgkvOSk3tOspQityRPlsjCMhcXhHY+riJ9yQlk2k84Uo1nij2ZH2gddEhCiNg4BaUexa0pBd6KGFibHK2gn2HmC5ViuFK09i7DgYUHEawQf2JJzjdPX5n7DSREeXUHkIm1yd4lyU9yAn8J6q+4jPcW8LZX5qoShypEIZbTnlk2k2ZqGb2YEHQ4QojYWBdt5dzCFHUe1IXW8qq17ABuKO6OtdYVgJdx51JVAu4DluCOXVlgEXCNMSZXa30n7qBZNnC9MWZ2cZ9PlA3vcjkd7QxOZwRzOYrdpAYdkhDi4MxBqfeA8cDu/VutjTrPWVEtqIKW+C2O84BNxpijgEHA08BjwG3eNgUM0Vp3xi0d3AM4C3jmIJ9XhNhv1GEsZ3GI+o0TeSPocIQQB68GbsHCXrhzaPvhTluKys8giZJ6Fze9ep5soAtumCG4oesDAQOMN8ZYYLXWOkVrnW6MyaIQNWumkpLiqwuzTEpPj9eAjsTwOUM50o5lIKP5khPYQMO4P2dZr9MgSJ3GXijr1NqLAVCqZrTlNfIrKkG1VYofCtiuAGstzYrasTFmO4DWOg2XqG4DhnuJCFxGrQFUhwNOfsnbXmiC2rx5Z1FPXULh+cdnZYVlFF/J6nQvlRnFlVyt7uEs+xz/x/0xjuvPynqdBiE8dQphqdd41Gnck55SHYB3gFSU6gl8CQzF2rnRihbVxbeCP5pjkZdMfE51pLVuhDtn6nVjzFtwwNCsNGAL8DsHvjvytoty7FuOZpntQEc1k3bIIUkhQuwp4C/AJqxdhxtk97yfgkUlqL3WsqqwS7Qda63r4A6K3WyMednbPE9rneldHwR8BUwDjtNaJ2mtGwNJxhiZSqncU7zFNeR68/Qlkx10QEKIkknF2qX7b1k7AZ+zmReVoKYdZFC34k7Mul1rPUVrPQXXzXe31noGUBF4zxgzB5eoZuCmxrjmIJ9XlBFrOJwpnEh9tZr+jAk6HCFEyfzmdfO5wztKnQv85qegsjZ86/BkZW2LedDxm0ki9sIyk0Qs6rQqW3mQC0kil1t4lW37T0aPrfJUp6UlLHUK4anXeNRpenqaivlOIyl1OPAq0A3YBSwHzsNaE62oLN0uEtoOajCGi0hVOziVl6MXEEIkFmtXAqcBtYDGwNl+khNIghIhMIWTWGMP4yjG0oTvgw5HCFEcSv0NGIu1O3CHfT5Gqcv9FJUEJRJerjdPX5KynMMzyPLwQoTK5cBRAFi7Cnc+7HV+CkqCEqGwjE58a4+ihVrEv7iBgbxLPVYhyUqIhFcB2BNxey8+P7jxnElCiJj6H1dziN1IKzWfVsznLJ5no63DAnqwkG4spRN7qRJ0mEKIA40BJqHUKFxiOg340E9BGcXnCcsoHgjP6Kh41WkNNtGOb2jPbNryLalqBwD7bAUM7VlIdxbSg/U0xE18El15r9N4CEudQnjqNchRfFrrHsDDxphMbw7Vj3Ej8gCeM8a8U2BBpZJxJ+r2BfYBX2Ktr/NGpAUlQmcrtZnG8UzjeJLI4XC7mCOYzRF8Qzs1h3bM4WyeI8vWYwHdWUh3ltGRvVQOOnQhQklr/S/gfNzy7QCdgceMMY/6KP4N1nbmwLlZfZEEJUItl2SW057ltOd9LuMQu9FrXc2iDXMZoD5kAB+yz1ZgGR1ZSDcW0KNUJqAVogxZCZwKvO7d7gJorfUQXCvqemNMYc279Sh1FDAba/cU8pgChbKLLzs7x5bn2cyFP7n7stkwYwlrPpvFms9ns3nBH3Mfpx1en4bHd6fhCd2pm9mRlCq+Zl4Roizy28XXFHjbGNNTa30xsMAYM0drPQyoaYy5seC9qyygtnfLUowl30PZgorHbOZh6YOG8PTtB1+nKUB77/JXapJFO68rsO2KOWx7ZgxLnxnDXluRBgPakTGgMxkDulD1sHoBx1244OvUv7C8TyE89RqnY1AlKfaBMSZvUu8PcBPCFsza9JI8AYQ0QQlREptJ5ysG8xWDSWYfzb1jV+2ZTcVJc8maNJfFw0ZQtVl90vt3JmNAZ2r3akuytK6EyG+c1vo6b/XzAcCcQh+pVEXgRkDjzn+6HngIa/dGexJJUKJcyqECho4YOvIel7Nq7g9smDiXDZPmsvHL+fw04hN+GvEJSVUqcmifI8gY0IX0/p2p2rRu0KELkQiuAp7WWu8F1uNOxi3MM7j1/brgFq5tAbyMW3W9SKE8BiXDzMPRdRLWOs3du4/fZi1lw6S5bJg4h+3m5/33VT28PhkDupAxoDO1erYluXLFUo0zrHWa6MJSryGdLHYu1nZGqXlY2wmlFLAQa9tFLSoJygnLGxTC88EvK3W68+cNZE2ex4Yv5rDxqwXk7NwNQHKVStQ+8gh37Kp/F1Kb1Il7nGWlThNNWOo1pAlqDtALmOklqnRgEtYeEa2odPEJEUVqowyaXHAcTS44jpw9+9g8awkbJs5xLawJ37JhwrcAVGvR0Dt21YVaPduQXKlCwJELkRCeBL4A6qLUE7iTdu/2U1BaUJ6w/IKC8PwyLQ91unP1r2yYNJesiXNd62qXO80jObUyhx7VnowBnUnv35nURhkxibM81GkQwlKvoWxBASjVBugHJANTsHaBr2KSoJywvEEhPB/88lanObv38tusJWz4wrWudqxYu/++aroRGXmtqx6tSapYstZVeavT0hKWeg1VglIqCbgUaAdMx9qCp0IqaheSoJywvEEhPB/88l6nO35aT9akuWyYOJeN0xaQu8uNqk2uWplDj+7gJazOVGng/zSR8l6n8RKWeg1Zgvov0AH4ChgIjMbae4q1C0lQTljeoBCeD77U6R9ydu9l04zFZE2cw4aJc9nxw7r996W1aky6d5Jwre6tSapQ+KFhqdP4CEu9hixBLQXaYK1Fqdq4gREdirULSVBOWN6gEJ4PvtRp4Xb89Is772riHDZNW0Tubte6SqlWxbWuBnQhfUBnqtSrfUA5qdP4CEu9hixBuWHlf9z+Dms7FmcXMopPiABUbVqPwy4dzGGXDiZn1x42TV+0P2Gt/2wm6z+bCUBa6yb7z7uq2a1VwFELUSz5GxK5xd2BtKA8YfkFBeH5ZSp1WjLbf1hHVl7ravoicvfsAyAlLZWZv3fx1rvqzhYODTjSoiVSnUYTlvdqyFpQmzhwYcIhB9y29pJou5AWlBAJplqz+lRrVp/D/noiOTv3sHH6QjZMnEvWxDl03fYVXfkKgNX28P3rXa2kLbnIDP8iofwz3+2pxd2BtKA8YfkFBeH5ZSp1GlvWWjrU2eotzjgbzXwqKNe62mmrspiuXuuqG1upHWVv8ReGOs0TlvdqqFpQMSAtKCFCQinFehqxnkZM4DQqsovW9rv9Caubmko370fqKtuchXRnAT34gdbSuhKhJAlKiJDaSxXm04v59AIsde3PtGcWRzCbliykiVrBibzFDluNxXRlAd1ZRDd+p1bQoQvhiyQoIcoExXoas57GjOcMKrGL1nae17qaRXc1he5MAeAn29JrXXXnB1phpXUlEpQkKCHKoD1U4Tt68x29AUt9u2p/V2BLFtJUfc9JvMF2m8ZiuvItRzOfnmRTusuHiHJAqYuA4UDNvC2U5SXfhRDFoVhHU9bRlHEMpTI7aW3negnrG3qoyfRgMtttGrPpx3SO5Qda475HhDhotwOZWLuouAXjmqC01j2Ah40xmVrr5sBI3Mlbi4BrjDG5Wus7gcG4lRav95YQFkLEyW5SmceRzONIwNLI/kBPvqAXX9BffUR/PmK9bcB0BjKDY9lE/Ne5EmXaupIkJ4hjgtJa/ws4H9jhbXoMuM0YM0Vr/TwwRGu9CugL9AAaAaOBbvGKSQiRn+JnDudnDmc0l9HazqU3E+jM15yqXuFUXmGp7cAMBvItR7GbqkEHLMJnDkq9B4wHdu/fau1r0QrGswW1EjgVeN273YU/TtQai5vd1gDjjTEWWK21TtFapxtjsuIYlxCiALkks5huLKYbldlBV/slvZlAazWf1sznXPt/zOVIpjOQJXSSwRXCrxrANtyqunksEFyCMsaM1lo3jdikvEQELtgaQHVgU8Rj8rYXmaBq1kwlJaX8fjjS08NxUmGYSJ0eaDdV+ZpBfM0gatv19OILejOeXmoivZjIFlubGQxgOgNZy2EF7kPqNPZCWafWXoxSFQCNyzmLsDbbT9HSHCQROVFgGrAF+N27nn97kTZv3hnbyPY/dThkZYXlDH2p09gr/TrdRF0+4Tw+4VwOt0vozQS6M5lBahSDGMUq25zpDGQm/dm2f6BWmOoUwvJejUedxj3pKdUFd/hmE5AE1EGpv2DtrGhFSzNBzdNaZxpjpgCDgMnACuARrfVwoCGQZIzZWIoxCSF8U6ykLStpy/+4mg52Jr2ZwBHM4mz1LEPt8yykO9MZyHcH9OaIcu7/gDP3JySlegJPAd2jFSzNBHUD8KLWuiKwFHjPGJOjtf4KmIHLrNeUYjxCiBLKpiJzOJo5HE0aW+hhJ9GbCXRUM+nITHbYaiy4qQ8Nh/ajZleNUjJkvRyrdkBrydqZKFXZT0GZLNYTlskiITyTcEqdxl6i12l9fqI3E+jFBGoqd3g5tWldGg7tR8PTM0ltkphD1hO9XvOEcrJYpSYDT2Dth97tU4C/Y22/qEUlQTlheYOCfJnGg9RpbClyWPTudNaMmsz6T2eSs2sPALV6tqHhGf2od3JvKlRPnCHrYanXkCaolrjR3M29LSuB87HWRC0qCcoJyxsU5Ms0HqROYy+vTrO37+KXT2ewZtRkNk1bBNaSVLkidY/vQcOhmRzatyNJAY/KDUu9hjJB5VGqKpCEtb5fhCQoT1jeoCBfpvEgdRp7BdXprjVZrHlvCmtGTWbHynUAVMqoSYPTjqbhGf2o3rZpKUfphKVeQ5WglHoBay/3uvj+/J1tbf+ou5AE5YTlDQryZRoPUqexV1SdWmvZMm85a0ZNZt2Yr9m32T22etumNBzaj/qnHk3ljJqFlo+1sNRryBJUF6ydg1J9C7zf2qgr7EqC8oTlDQryZRoPUqex57dOc/bsY8PEOawZNZkNX8zB7stGJSdxaGZHGg3tT53jupFcpVJcYw1LvYYqQeVR6imsvS7ftlex9sJoRWU2cyFEoJIrVaDeCT2pd0JP9m76nXUffs3P70wma+JcsibOJSUtlXon96Hh0Exq9WgjQ9bDQqkRQDOgK0q1jbgnBTjE1y6kBeWE5RcUyK/9eJA6jb2DrdNt3//M2nensOa9Kexe5w1Zb1yHBkMzaXhGJlWb1otFmEB46jXIFpSf1SkOKKBUU6Ap8CTwt4h7soGlWPtbtOdM8hOYEEKUtrSWjWg17HwGzHmRnu/dQ8Oh/dizcSvLh7/D5B5XMe2kW1j12jj2bd0edKhlnrc6xQgg7wTbvNUpjsItHDakgGK7sXYKcBLwY8TlZ6Can+eVLj4hREJTSUkcelR7Dj2qPe0eupz1n81kzagpbPxqAZtnL2XxbSOoc1x3Gg7tR3pmR5IqyNdaHPhZneKDfGVGACd6j7McuAKmxXX/FSmUXXzZ2Tm2PM9mLoSAHWuyWPnmF6x4bTxbl64GoHLGITQ7ewDNLziWWh2by/Eqf/x28TUF3jbG9NRarzPG1Pe29wcuMcacV/De1YlY+0lJAgvlT414zGYelj5okOMl8SB1Gntxr9NKlal3yYnUvXgwW+evZM2oyaz94EuWPDmaJU+OJq1VYxqe2Z8Gpx5N5bq1itxVWOo1TsegSlKsoNUpCvMwUH4SlBBC5FFKcUjH5hzSsTlt7rqIDZPmsebdyWwY/w1L7x7J0ntfI71vBxqekUndQT1JTo3vkPVyoqDVKQqzEqVeBmYBu/ZvDXhFXSGEKFVJFStQ9/ju1D2+O3s3b2Pdh1+zZtQUsibPI2vyPFKqVaHeSb1pOLQftXq2QSXJOLES+tPqFEU8dhOuG7FnxDZfK+qG8hiUDDOX7qhYkzqNvUSq0+0r17Jm1BTWvjeFXWvcgt1VGqXT8PRMGpyRSbNerQKO0J+Qnqh7LNZOyLftVKx9P2pRSVCOfPBjT+o09qROD47NzWXTjMWsHTWFdR9PI2fHbgBW2DZMZyDf0JcdVA84ysKFKkEpdSZQCbgHuCPinhTgVqxtXmC5yF1IgnLkgx97UqexJ3UaOzk797B+7EzWjJrMr5MXkKRy2WcrMJ+eTGcgC+lOToIdBQlZgroM6AOcDHwUcU828AXWvhN1F5KgHPngx57UaexJncZHy4zd9GQSvRlPQ/UTANtsDWbSn+kMZBUt8DkaO65ClaDyKHUt1j6db1tPrJ0ZtagkKEc++LEndRp7Uqfx8Ue9Whqzgj6MpweTqK7c6Om1tgnTOZYZHMMW0gOLM1QJSqk+QDLuhN1L+SPDpwDPY23LaLtIrParEEIESrGaFqymBaO4gnb2G3ozgY5M5ww1gtPsSyyhM9M5lrkcyV6qBB1wIjsW6AvUwx2HyrMPN49fVNKC8sgv09iTOo09qdP4iFavqWyjG1PozQRaqMUA7LaV+Zajmc5ADB2wpTC1aahaUHmUOh9rX0epCrjpkq4EumFt1Pn4pAUlhBBR7CSNqZzEVE4iw66lFxPozQSOVOM5kvFsshnM4BimM5D1NAo63ETzNUo9CFwM1ATuB87wU1BaUB75ZRp7UqexJ3UaHyWpV0UuzVlEH8bTjalUUW4Kth9sK6ZzLNMZyG5SYxpnqFpQSv0FuALoDIwB3gVexNqmfnchLSghhCgBSxLLac9y2vMm19HJTqM3E2jHtzRTy2hqv+dl/hV0mEEaDYwCemPtCgCUyi2yRD6SoIQQ4iDtoxKz6c9s+lODTXSy0/iB1kGHFbT2uG69r1HqJ+B/FDPnyERUQggRQ1upzRROZjUtgg4lWNYuwtobgIbAQ0A/oA5KfYpSJ/jZhSQoIYQQ8WNtNtaOwdpTcMlqEvCgn6KSoIQQQpQOa7Ow9lGs7eDn4ZKghBBCJCRJUEIIIRKSJCghhBAJKSGGmWutk4BngQ7AHuAyY8yKYKMSQggRpERpQZ0CVDbG9AL+DTwacDxCCCECligJ6kjgcwBjzEyga7DhCCGECFpCdPEB1YGtEbdztNYpxpjsgh4cj7mjwjUlYTjmY5M6jT2p0/gIT72Gp05jIVFaUL9zYM0nFZachBBClA+JkqCmAScAaK17AguDDUcIIUTQEqWL7wPgWK31dNyywBcHHI8QQoiAhXI9KCGEEGVfonTxCSGEEAeQBCWEECIhSYISQgiRkBJlkESZpLXuATxsjMkMOpaw0VpXAF4GmgKVgPuMMR/le8w8/jh/7kdjjAyuKYDW+hbgZKAi8Kwx5qUCHnPAe1Vr3RwYCVhgEfx/e3cfI1dZxXH8u22iFC0vRqFAauRFfzaktKEESBG6FsTwB0V8qVbEbETQUhCxiJVAFBQtRhskBJG3ooBEoDFpG8GWaokllAI2NrT6a2hoi2hK00hooWbNUv84z8hld7ba0Oneu5xPstmZO/funHl25p55nnvveZhle4+m6347qLZbttnelz2oDpF0JXAHsN9Qx9JQXwC22T4VOAu4ufqgpP0AbHeXn0xObUjqBiYDpwBTgLFt1mn3Xp0HXF3avws4p+PBNkybdss228syQXXOBuCTQx1Egz0IXFO53//C7QnA/pKWSPp9uX4uDfRx4rrC3wCLgMVt1mn3Xp0EPFZuPwyc0akAG6x/uw3aZpJ6JM3dh7ENC5mgOsT2AuDfQx1HU9neYXu7pNHAQ8DV/VZ5DfgxsQP+KnCfpByyHui9RG3Lz/BGO72pVNgg79Uu261rULYDB3Y60KZp024D2kzSFEnLiSLYn5e0XNL5+zjUxsoPdKotSWOJb9s3ru4AAAUoSURBVP63AAvLBx1gKZGcnis7hPWStgGHAS8MRaw1tg34q+1ewCWJr5X0ErDU9vWDbFc9djIaeLnDcQ4HA9rM9mNAt6Qe4MO25wxJZA2VCSrVkqRDgSXAJbaXlcXdlcdnAuOBiyUdThQc/se+jrMBVgCXSZpHJPBdwHjbff9ju9WSum0vJ44B/qGzYQ4L2WZ7WSaoVFdXAQcD10hqHYs6y/bOcvtO4G5JK4id7peywPBAthdLOg1YRQzpz/o/khPAbOB2Se8A/kIMs6bdG7TNbN89VEE1WZY6SimlVEt5kkRKKaVaygSVUkqpljJBpZRSqqVMUCmllGopE1RKKaVaytPMU6NIuhkYY/vTlWVnArcCE2xv79DzXg78EBhre2tZ1gU8avv0cn8E8A2ijiBAHzDX9oOdiCml4S57UKlp5gCTJE0DkPQu4GfEdVAdSU5FD1HLrlqUdiRRgLVlLnEx8Wm2JwLnAj+S9NEOxpXSsJXXQaXGkXQGMRXHOOA6YITty8vUB/OAUcBW4CLbmyRNBb5Xlh8IfN32Ikn3lvvHEBdZfgyYSpSsWWD7++X5JgG/Bs4D7gM+aHuXpFuAmcATROWALUQ5m42VWE8EdtheV5LqdcQXw+eAr9jeKulvwC+BM4Fe4AclnmNKrAtKrK8AJxJVM75r+1elHNQdwEHAGOAu29dK+jJwOlGL7yjgt7YvlXQ/sMT2/BLfCuAy28+85X9MSntZ9qBS49h+FPgdMJ/YqV8l6Z3A7cBnbR8P3AT8vGxyKdBTls8kkkTLFtvjAANTbU8ATgXGl4oAEL2mB2w/SfSaWlWqvwb02Z5MJMvXqsmpxLqqJKfDiJqC02wfBzxVYmx50fYJRIXs2eU5eogeY8uRwMnlsRslHUIkzXtsnwRMBK6QdHBZ/2SiF3cc8ClJ44jEfj6ApKOBAzI5pbrKY1CpqWYDm4FP2N4paSLRU1gsCWI+nlFl3RnA2ZJmEDvtd1f+zpPl9wtAn6Q/ElNSXGm7tyS+GbxRB/ABoir40n7xvA78azfxngQ8YXtzuX8bUQ6n5eHyexOwwXafpE1EuaeWu0o5p82SVhLzPN0ATJX0TeBYYlLC/cv6j9veASDpeeA9wDLgttLz+iLwi93EnNKQyh5UaiTbrxAVtjeWRSOB9bYnluM/xwNTyokMjxNz9TxFnOhQnW5iZ/l7vcTw2bXAIcDK0sM4h6hMvUjSRuIEiGmlR1S1FjioFK79L0nnSbqEgZ+1Lt78BbG3cnuwmoLV5SPK/RuBi4HniWHMf1ZeXzVh7iKmg3idGE78HDEFx72DPFdKQy4TVBou1gFjJE0u9y8C7gHeRwyNfQd4hEg4I/tvLOkEonex3PZsYD3wIWJ479u2P1B+jiB6XRcQZ+l1SRph+1ViCO/WMocVko4Crid6SiuBUyS9vxLfnla7ni6pS9KRRMJdQRw3u8H2Q0QP6tB2r6+f+cSw5wbbW/YwhpT2mUxQaVgoVc6nAz+VtIYYlrvQ9ktEolpLJIpRwAGSRvXb/mngGeBZSX8CVgNrgI8QO/SqnwAXEj2VxcCacrxqTtlmlaQ/E7MCX2F7me2/Ez2dhZLWEcNzs/bwZY4GngYWAhfYfpk4oeJ+Sc8SQ4+riYQ8qHKc7MU2ryulWsmz+FJqgHIW3yO239KQXBnyPJzovR1rO2d9TrWVPaiU3l6mE72sb2VySnWXPaiUUkq1lD2olFJKtZQJKqWUUi1lgkoppVRLmaBSSinVUiaolFJKtfQf1VDGHJ+xMHEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Years in Current Role & Attrition Graph \n",
    "graph_df(x = 'YearsAtCompany')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employees who don't fulfill their first year and in their first year in their current role are more likely to leave the company. That might be result of challenge or not satisfied with the current role. \n",
    "\n",
    "Employees who have 2-5 years' experience in that company compose of the maximum attrition percentage and number in the company. \n",
    "\n",
    "Besides that, after years in current role, employees are willing to leave the company. That might be result of looking for better opportunities in other companies. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Years Since Last Promotion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Years in Current Role Groups\n",
    "table_df(x = 'YearsSinceLastPromotion')\n",
    "bins = [0, 1, 2, 3, 4, 7, 8, 16]\n",
    "group_names = ['0', '1', '2', '3', '4-6', '7', '8+']             \n",
    "df2['YearsSinceLastPromotion'] = pd.cut(df2['YearsSinceLastPromotion'], bins, labels = group_names, \n",
    "                                        include_lowest = True, right = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>YearsSinceLastPromotion</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>581</td>\n",
       "      <td>110</td>\n",
       "      <td>18.932874</td>\n",
       "      <td>46.413502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>357</td>\n",
       "      <td>49</td>\n",
       "      <td>13.725490</td>\n",
       "      <td>20.675105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>27</td>\n",
       "      <td>16.981132</td>\n",
       "      <td>11.392405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>17.307692</td>\n",
       "      <td>3.797468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4-6</td>\n",
       "      <td>138</td>\n",
       "      <td>13</td>\n",
       "      <td>9.420290</td>\n",
       "      <td>5.485232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>76</td>\n",
       "      <td>16</td>\n",
       "      <td>21.052632</td>\n",
       "      <td>6.751055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8+</td>\n",
       "      <td>107</td>\n",
       "      <td>13</td>\n",
       "      <td>12.149533</td>\n",
       "      <td>5.485232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearsSinceLastPromotion  Total Employee   Attrition Number  \\\n",
       "0                       0              581               110   \n",
       "1                       1              357                49   \n",
       "2                       2              159                27   \n",
       "3                       3               52                 9   \n",
       "4                     4-6              138                13   \n",
       "5                       7               76                16   \n",
       "6                      8+              107                13   \n",
       "\n",
       "   % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                             18.932874                      46.413502  \n",
       "1                             13.725490                      20.675105  \n",
       "2                             16.981132                      11.392405  \n",
       "3                             17.307692                       3.797468  \n",
       "4                              9.420290                       5.485232  \n",
       "5                             21.052632                       6.751055  \n",
       "6                             12.149533                       5.485232  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Years Since Last Promotion & Attrition Table\n",
    "table_df(x = 'YearsSinceLastPromotion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Years Since Last Promotion & Attrition Graph\n",
    "graph_df(x = 'YearsSinceLastPromotion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employees who don't fulfill his one year since the last promotion in the company are more likely to leave the company(46.4% of all attrition). And employees who have one and two years experience in the current company since the last promotion have the highest attrition number after the above group in the company respectively. 7 years since last promotion has the highest attrition in its individual group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Years with Current Manager**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labeling Years with Current Manager Groups\n",
    "table_df(x = 'YearsWithCurrManager')\n",
    "bins = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 18]\n",
    "group_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10+']             \n",
    "df2['YearsWithCurrManager'] = pd.cut(df2['YearsWithCurrManager'], bins, labels = group_names, \n",
    "                                     include_lowest = True, right = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>YearsWithCurrManager</th>\n",
       "      <th>Total Employee</th>\n",
       "      <th>Attrition Number</th>\n",
       "      <th>% of Attrition in the Relevant Group</th>\n",
       "      <th>% of Attrition in the Company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>263</td>\n",
       "      <td>85</td>\n",
       "      <td>32.319392</td>\n",
       "      <td>35.864979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>11</td>\n",
       "      <td>14.473684</td>\n",
       "      <td>4.641350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>344</td>\n",
       "      <td>50</td>\n",
       "      <td>14.534884</td>\n",
       "      <td>21.097046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>142</td>\n",
       "      <td>19</td>\n",
       "      <td>13.380282</td>\n",
       "      <td>8.016878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>98</td>\n",
       "      <td>11</td>\n",
       "      <td>11.224490</td>\n",
       "      <td>4.641350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>12.903226</td>\n",
       "      <td>1.687764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>13.793103</td>\n",
       "      <td>1.687764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>216</td>\n",
       "      <td>31</td>\n",
       "      <td>14.351852</td>\n",
       "      <td>13.080169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>107</td>\n",
       "      <td>10</td>\n",
       "      <td>9.345794</td>\n",
       "      <td>4.219409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>64</td>\n",
       "      <td>6</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>2.531646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10+</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.531646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsWithCurrManager  Total Employee   Attrition Number  \\\n",
       "0                     0              263                85   \n",
       "1                     1               76                11   \n",
       "2                     2              344                50   \n",
       "3                     3              142                19   \n",
       "4                     4               98                11   \n",
       "5                     5               31                 4   \n",
       "6                     6               29                 4   \n",
       "7                     7              216                31   \n",
       "8                     8              107                10   \n",
       "9                     9               64                 6   \n",
       "10                  10+              100                 6   \n",
       "\n",
       "    % of Attrition in the Relevant Group  % of Attrition in the Company  \n",
       "0                              32.319392                      35.864979  \n",
       "1                              14.473684                       4.641350  \n",
       "2                              14.534884                      21.097046  \n",
       "3                              13.380282                       8.016878  \n",
       "4                              11.224490                       4.641350  \n",
       "5                              12.903226                       1.687764  \n",
       "6                              13.793103                       1.687764  \n",
       "7                              14.351852                      13.080169  \n",
       "8                               9.345794                       4.219409  \n",
       "9                               9.375000                       2.531646  \n",
       "10                              6.000000                       2.531646  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Years with Current Manager & Attrition Table\n",
    "table_df(x = 'YearsWithCurrManager')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Years with Current Manager & Attrition Graph\n",
    "graph_df(x = 'YearsWithCurrManager')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the employee quit the company before completing their first year with their current manager. Other group who leaves the company most is the ones who work two years with current manager."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Other Features**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also checked the 'Employee Number', 'Daily Rate', 'Hourly Rate' and 'Monthly Rate' features as I did the in previous features of dataset. But there is nothing significant to comment or visualize about these features. For that reason, I didn't include them in my notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature/Variable Relationships:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will take a look at how variables related to each other. There are various methods/visualizations for this. I will use correlation matrix (heat map) for this purpose. \n",
    "\n",
    "`Correlation means association - more precisely it is a measure of the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation.\n",
    "A positive correlation is a relationship between two variables in which both variables either increase or decrease at the same time. An example would be height and weight. Taller people tend to be heavier.\n",
    "A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other. An example would be height above sea level and temperature. As you climb the mountain (increase in height) it gets colder (decrease in temperature).\n",
    "A zero correlation exists when there is no relationship between two variables. For example, there is no relationship between the amount of tea drunk and level of intelligence.`"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Capture.JPG](attachment:Capture.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18a98c74710>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df2.corr()\n",
    "f , ax = plt.subplots(figsize=(20,12))\n",
    "sns.heatmap(corr_matrix, vmax=1, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the fact which is given strength of correlation chart, we can identify the features which have strong, moderate, weak and zero correlations between each other. I will just outline the strong and moderate correlations here. \n",
    "\n",
    "**Features which have strong correlations:**\n",
    "\n",
    "Percent Salary Hike and Performance Rating,\n",
    "\n",
    "Total Working Years, Monthly Income and Job Level,\n",
    "\n",
    "Years at Company, Years with Current Manager, and Years in Current Role, \n",
    "\n",
    "**Features which have moderate correlations:**\n",
    "\n",
    "Age has moderate correlation with Total Working Years, Monthly Income, and Job Level,\n",
    "\n",
    "Job Level has moderate correlation with Years at Company and Age,\n",
    "\n",
    "Total Working Years has moderate correlation with Years with Current Manager, Years Since Last Promotion, Years in Current Role, Years at Company, and Age,\n",
    "\n",
    "Years at Company has moderate correlation with Years Since Last Promotion, Total Working Years, Monthly Income, Job Level,\n",
    "\n",
    "Years in Current Role has moderate correlation with Years Since Last Promotion, Total Working Years, \n",
    "\n",
    "Years Since Last Promotion has moderate correlation with Years with Current Manager, Years in Current Role, Years at Company, Total Working Years, \n",
    "\n",
    "Years with Current Manager has moderate correlation with Years Since Last Promotion, Total Working Years.\n",
    "\n",
    "---\n",
    "Generally, for the training model, we don't select features that have a strong correlation because it will have multicollinearity problem. Heatmap is a good way to detect this kind of situation. In this case, YearsAtCompany, YearsInCurrentRole, YearsSinceLastPromotion and YearWithCurrManager have strong correlations with each other which we should keep in our mind in further steps. Besides, that does not mean that is always case. We should try each variable and select the features which will give the best results in the model. "
   ]
  }
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