03_Employee_Retention.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Employee Retention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employee turn-over is a very costly problem for companies. The cost of replacing an employee if often larger than 100K USD, taking into account the time spent to interview and find a replacement, placement fees, sign-on bonuses and the loss of productivity for several months. \n",
    "<br>\n",
    "<br>\n",
    "It is only natural then that data science has started being applied to this area. Understanding\n",
    "why and when employees are most likely to leave can lead to actions to improve employee\n",
    "retention as well as planning new hiring in advance. This application of DS is sometimes calledpeople analytics or people data science (if you see a job title: people data scientist, this is your\n",
    "job).\n",
    "<br>\n",
    "<br>\n",
    "In this challenge, you have a data set with info about the employees and have to predict when employees are going to quit by understanding the main drivers of employee churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got employee data from a few companies. We have data about all employees who joined from 2011/01/24 to 2015/12/13. For each employee, we also know if they are still at the company as of 2015/12/13 or they have quit. Beside that, we have general info about the employee, such as avg salary during her tenure, dept, and yrs of experience.\n",
    "<br>\n",
    "<br>\n",
    "As said above, the goal is to predict employee retention and understand its main drivers. Specifically, you should:\n",
    "* Assume, for each company, that the headcount starts from zero on 2011/01/23. Estimate employee headcount, for each company, on each day, from 2011/01/24 to 2015/12/13. That is, if by 2012/03/02 2000 people have joined company 1 and 1000 of them have already quit, then company headcount on 2012/03/02 for company 1 would be 1000. You should create a table with 3 columns: day, employee_headcount, company_id.\n",
    "* What are the main factors that drive employee churn? Do they make sense? Explain your findings.\n",
    "* If you could add to this data set just one variable that could help explain employee churn, what would that be?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **employee_id** : id of the employee. Unique by employee per company\n",
    "* **company_id** : company id.\n",
    "* **dept** : employee dept\n",
    "* **seniority** : number of yrs of work experience when hired\n",
    "* **salary**: avg yearly salary of the employee during her tenure within the company\n",
    "* **join_date**: when the employee joined the company, it can only be between 2011/01/24 and 2015/12/13\n",
    "* **quit_date**: when the employee left her job (if she is still employed as of 2015/12/13, this field is NA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "==================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('F:\\Data_Science\\ProductSense-ABTesing\\DS take home challenges\\Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary   join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0  2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0  2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0  2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0  2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0  2011-10-17   \n",
       "\n",
       "    quit_date  \n",
       "0  2015-10-30  \n",
       "1  2014-04-04  \n",
       "2         NaN  \n",
       "3  2013-06-07  \n",
       "4  2014-08-22  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('employee_retention.csv')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24702, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24702 entries, 0 to 24701\n",
      "Data columns (total 7 columns):\n",
      "employee_id    24702 non-null float64\n",
      "company_id     24702 non-null int64\n",
      "dept           24702 non-null object\n",
      "seniority      24702 non-null int64\n",
      "salary         24702 non-null float64\n",
      "join_date      24702 non-null object\n",
      "quit_date      13510 non-null object\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Department is string, join_date, quit_date should be converted to date format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13510 people who quit and 11192 people who didn't quit by 2015.\n"
     ]
    }
   ],
   "source": [
    "print('There are 13510 people who quit and ' + \n",
    "      str(24702-13510) +\n",
    "     ' people who didn\\'t quit by 2015.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702</td>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702.000000</td>\n",
       "      <td>24702</td>\n",
       "      <td>13510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>995</td>\n",
       "      <td>664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>2015-05-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>501604.403530</td>\n",
       "      <td>3.426969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.127803</td>\n",
       "      <td>138183.345478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>288909.026101</td>\n",
       "      <td>2.700011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.089520</td>\n",
       "      <td>76058.184573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>250133.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>79000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>500793.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>123000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>753137.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>187000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>999969.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>408000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          employee_id    company_id              dept     seniority  \\\n",
       "count    24702.000000  24702.000000             24702  24702.000000   \n",
       "unique            NaN           NaN                 6           NaN   \n",
       "top               NaN           NaN  customer_service           NaN   \n",
       "freq              NaN           NaN              9180           NaN   \n",
       "mean    501604.403530      3.426969               NaN     14.127803   \n",
       "std     288909.026101      2.700011               NaN      8.089520   \n",
       "min         36.000000      1.000000               NaN      1.000000   \n",
       "25%     250133.750000      1.000000               NaN      7.000000   \n",
       "50%     500793.000000      2.000000               NaN     14.000000   \n",
       "75%     753137.250000      5.000000               NaN     21.000000   \n",
       "max     999969.000000     12.000000               NaN     99.000000   \n",
       "\n",
       "               salary   join_date   quit_date  \n",
       "count    24702.000000       24702       13510  \n",
       "unique            NaN         995         664  \n",
       "top               NaN  2012-01-03  2015-05-08  \n",
       "freq              NaN         105         111  \n",
       "mean    138183.345478         NaN         NaN  \n",
       "std      76058.184573         NaN         NaN  \n",
       "min      17000.000000         NaN         NaN  \n",
       "25%      79000.000000         NaN         NaN  \n",
       "50%     123000.000000         NaN         NaN  \n",
       "75%     187000.000000         NaN         NaN  \n",
       "max     408000.000000         NaN         NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seem to be outliers in 'seniority' and 'salary'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seniority**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
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       "    <tr>\n",
       "      <td>24701</td>\n",
       "      <td>604052.0</td>\n",
       "      <td>1</td>\n",
       "      <td>marketing</td>\n",
       "      <td>99</td>\n",
       "      <td>185000.0</td>\n",
       "      <td>2011-07-26</td>\n",
       "      <td>2013-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24700</td>\n",
       "      <td>97289.0</td>\n",
       "      <td>10</td>\n",
       "      <td>engineer</td>\n",
       "      <td>98</td>\n",
       "      <td>266000.0</td>\n",
       "      <td>2011-12-13</td>\n",
       "      <td>2015-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8436</td>\n",
       "      <td>193109.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>29</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>2014-03-17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2100</td>\n",
       "      <td>639047.0</td>\n",
       "      <td>2</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>29</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>2015-05-12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15613</td>\n",
       "      <td>30208.0</td>\n",
       "      <td>6</td>\n",
       "      <td>engineer</td>\n",
       "      <td>29</td>\n",
       "      <td>206000.0</td>\n",
       "      <td>2013-12-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>7018</td>\n",
       "      <td>330049.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>22</td>\n",
       "      <td>336000.0</td>\n",
       "      <td>2011-03-21</td>\n",
       "      <td>2013-10-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19115</td>\n",
       "      <td>285351.0</td>\n",
       "      <td>2</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>22</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>2015-10-12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13579</td>\n",
       "      <td>130527.0</td>\n",
       "      <td>2</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>22</td>\n",
       "      <td>111000.0</td>\n",
       "      <td>2012-09-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19100</td>\n",
       "      <td>121134.0</td>\n",
       "      <td>1</td>\n",
       "      <td>data_science</td>\n",
       "      <td>22</td>\n",
       "      <td>288000.0</td>\n",
       "      <td>2015-05-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13037</td>\n",
       "      <td>683857.0</td>\n",
       "      <td>3</td>\n",
       "      <td>engineer</td>\n",
       "      <td>22</td>\n",
       "      <td>227000.0</td>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5556 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id  company_id              dept  seniority    salary  \\\n",
       "24701     604052.0           1         marketing         99  185000.0   \n",
       "24700      97289.0          10          engineer         98  266000.0   \n",
       "8436      193109.0           2      data_science         29  275000.0   \n",
       "2100      639047.0           2  customer_service         29  123000.0   \n",
       "15613      30208.0           6          engineer         29  206000.0   \n",
       "...            ...         ...               ...        ...       ...   \n",
       "7018      330049.0           2      data_science         22  336000.0   \n",
       "19115     285351.0           2  customer_service         22  126000.0   \n",
       "13579     130527.0           2  customer_service         22  111000.0   \n",
       "19100     121134.0           1      data_science         22  288000.0   \n",
       "13037     683857.0           3          engineer         22  227000.0   \n",
       "\n",
       "        join_date   quit_date  \n",
       "24701  2011-07-26  2013-12-06  \n",
       "24700  2011-12-13  2015-01-09  \n",
       "8436   2014-03-17         NaN  \n",
       "2100   2015-05-12         NaN  \n",
       "15613  2013-12-16         NaN  \n",
       "...           ...         ...  \n",
       "7018   2011-03-21  2013-10-11  \n",
       "19115  2015-10-12         NaN  \n",
       "13579  2012-09-04         NaN  \n",
       "19100  2015-05-04         NaN  \n",
       "13037  2014-07-08         NaN  \n",
       "\n",
       "[5556 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data['seniority'] > 21].sort_values(by = 'seniority', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "98 and 99 seem impossible, consider removing them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24700, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data[raw_data['seniority'] < 98]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Salary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10689</td>\n",
       "      <td>62540.0</td>\n",
       "      <td>2</td>\n",
       "      <td>engineer</td>\n",
       "      <td>23</td>\n",
       "      <td>408000.0</td>\n",
       "      <td>2013-08-26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7919</td>\n",
       "      <td>517439.0</td>\n",
       "      <td>2</td>\n",
       "      <td>engineer</td>\n",
       "      <td>25</td>\n",
       "      <td>406000.0</td>\n",
       "      <td>2013-09-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3172</td>\n",
       "      <td>965750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>data_science</td>\n",
       "      <td>19</td>\n",
       "      <td>398000.0</td>\n",
       "      <td>2015-05-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4150</td>\n",
       "      <td>707585.0</td>\n",
       "      <td>1</td>\n",
       "      <td>engineer</td>\n",
       "      <td>22</td>\n",
       "      <td>393000.0</td>\n",
       "      <td>2013-09-16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6638</td>\n",
       "      <td>460908.0</td>\n",
       "      <td>1</td>\n",
       "      <td>engineer</td>\n",
       "      <td>24</td>\n",
       "      <td>386000.0</td>\n",
       "      <td>2014-11-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10519</td>\n",
       "      <td>988191.0</td>\n",
       "      <td>7</td>\n",
       "      <td>sales</td>\n",
       "      <td>21</td>\n",
       "      <td>201000.0</td>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>2015-11-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4261</td>\n",
       "      <td>651644.0</td>\n",
       "      <td>1</td>\n",
       "      <td>engineer</td>\n",
       "      <td>11</td>\n",
       "      <td>201000.0</td>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>2015-10-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6820</td>\n",
       "      <td>919576.0</td>\n",
       "      <td>1</td>\n",
       "      <td>design</td>\n",
       "      <td>22</td>\n",
       "      <td>201000.0</td>\n",
       "      <td>2013-05-28</td>\n",
       "      <td>2015-06-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15487</td>\n",
       "      <td>398409.0</td>\n",
       "      <td>2</td>\n",
       "      <td>sales</td>\n",
       "      <td>19</td>\n",
       "      <td>201000.0</td>\n",
       "      <td>2013-12-09</td>\n",
       "      <td>2015-06-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18217</td>\n",
       "      <td>289239.0</td>\n",
       "      <td>2</td>\n",
       "      <td>engineer</td>\n",
       "      <td>6</td>\n",
       "      <td>201000.0</td>\n",
       "      <td>2012-10-22</td>\n",
       "      <td>2014-10-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5095 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id  company_id          dept  seniority    salary   join_date  \\\n",
       "10689      62540.0           2      engineer         23  408000.0  2013-08-26   \n",
       "7919      517439.0           2      engineer         25  406000.0  2013-09-16   \n",
       "3172      965750.0           1  data_science         19  398000.0  2015-05-04   \n",
       "4150      707585.0           1      engineer         22  393000.0  2013-09-16   \n",
       "6638      460908.0           1      engineer         24  386000.0  2014-11-03   \n",
       "...            ...         ...           ...        ...       ...         ...   \n",
       "10519     988191.0           7         sales         21  201000.0  2012-01-30   \n",
       "4261      651644.0           1      engineer         11  201000.0  2014-09-02   \n",
       "6820      919576.0           1        design         22  201000.0  2013-05-28   \n",
       "15487     398409.0           2         sales         19  201000.0  2013-12-09   \n",
       "18217     289239.0           2      engineer          6  201000.0  2012-10-22   \n",
       "\n",
       "        quit_date  \n",
       "10689         NaN  \n",
       "7919          NaN  \n",
       "3172          NaN  \n",
       "4150          NaN  \n",
       "6638          NaN  \n",
       "...           ...  \n",
       "10519  2015-11-27  \n",
       "4261   2015-10-09  \n",
       "6820   2015-06-05  \n",
       "15487  2015-06-12  \n",
       "18217  2014-10-31  \n",
       "\n",
       "[5095 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['salary'] > 200000].sort_values(by = 'salary', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(121)\n",
    "sns.boxplot(data['salary'], orient='v', width=0.3, showmeans = True, palette='Set2',\n",
    "           medianprops = {'color': 'navy', 'linestyle': '--'},\n",
    "           meanprops = {'markerfacecolor': 'yellow', 'marker': 'D'},\n",
    "           flierprops = {'markerfacecolor': 'red', 'marker': 'o', 'markersize': 5}\n",
    "           )\n",
    "plt.subplot(122)\n",
    "sns.distplot(data['salary'], bins=40, norm_hist=True,\n",
    "             kde=True, kde_kws={'linestyle': '--'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The salary is skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert date-related column from string to datetime**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0 2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0 2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0 2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "\n",
       "   quit_date  \n",
       "0 2015-10-30  \n",
       "1 2014-04-04  \n",
       "2        NaT  \n",
       "3 2013-06-07  \n",
       "4 2014-08-22  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['join_date'] = pd.to_datetime(data['join_date'])\n",
    "data['quit_date'] = pd.to_datetime(data['quit_date'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 24700 entries, 0 to 24699\n",
      "Data columns (total 7 columns):\n",
      "employee_id    24700 non-null float64\n",
      "company_id     24700 non-null int64\n",
      "dept           24700 non-null object\n",
      "seniority      24700 non-null int64\n",
      "salary         24700 non-null float64\n",
      "join_date      24700 non-null datetime64[ns]\n",
      "quit_date      13508 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(2), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate employee headcount for each company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>day</th>\n",
       "      <th>headcount</th>\n",
       "      <th>company_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>995</td>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1990</td>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2985</td>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3980</td>\n",
       "      <td>2011-01-24</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7959</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>468</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8954</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>432</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9949</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>385</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10944</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11939</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11940 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             day  headcount  company_id\n",
       "0     2011-01-24         25           1\n",
       "995   2011-01-24         17           2\n",
       "1990  2011-01-24          9           3\n",
       "2985  2011-01-24         12           4\n",
       "3980  2011-01-24          5           5\n",
       "...          ...        ...         ...\n",
       "7959  2015-12-10        468           8\n",
       "8954  2015-12-10        432           9\n",
       "9949  2015-12-10        385          10\n",
       "10944 2015-12-10          4          11\n",
       "11939 2015-12-10         12          12\n",
       "\n",
       "[11940 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Create 3 lists to store unique(join_date), headcount and company_id\n",
    "for each company and each day:\n",
    "    filter the data which has an earlier join_date and quit_date respectively\n",
    "    headcount = len(join_date) - len(quit_date)\n",
    "    append the 3 lists\n",
    "Create a DataFrame with the 3 lists    \n",
    "'''\n",
    "day = []\n",
    "headcount = []\n",
    "company_id = []\n",
    "\n",
    "unique_day = np.sort(pd.unique(data['join_date']))\n",
    "company = np.sort(pd.unique(data['company_id']))\n",
    "\n",
    "for company_idx in company:\n",
    "    for day_idx in unique_day:\n",
    "        date_temp = data[data['company_id'] == company_idx]\n",
    "        joinNum = len(date_temp[date_temp['join_date'] <= day_idx])\n",
    "        quitNum = len(date_temp[date_temp['quit_date'] <= day_idx])\n",
    "        \n",
    "        day.append(day_idx)\n",
    "        headcount.append(joinNum - quitNum)\n",
    "        company_id.append(company_idx)\n",
    "\n",
    "pd.DataFrame({'day': day, \"headcount\": headcount, 'company_id': company_id}).sort_values(['day','company_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# one specific case of company1 to help write the loop\n",
    "date = np.sort(pd.unique(data['join_date']))\n",
    "for i in date:\n",
    "    date_temp = data[data['company_id'] == 1]\n",
    "    joinNum = (len(date_temp[date_temp['join_date'] <= i]))\n",
    "    quitNum = (len(date_temp[date_temp['quit_date'] <= i]))\n",
    "    print(joinNum - quitNum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# employee churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0 2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0 2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0 2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "\n",
       "   quit_date  \n",
       "0 2015-10-30  \n",
       "1 2014-04-04  \n",
       "2        NaT  \n",
       "3 2013-06-07  \n",
       "4 2014-08-22  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:376: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "E:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "data.loc[data['quit_date'].isnull(), 'if_quit'] = 0     # quit_date is null --- not quit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['if_quit'].isnull(), 'if_quit'] = 1      # if_quit is null --- quit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>if_quit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24695</td>\n",
       "      <td>63273.0</td>\n",
       "      <td>1</td>\n",
       "      <td>data_science</td>\n",
       "      <td>5</td>\n",
       "      <td>172000.0</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24696</td>\n",
       "      <td>94644.0</td>\n",
       "      <td>6</td>\n",
       "      <td>marketing</td>\n",
       "      <td>7</td>\n",
       "      <td>88000.0</td>\n",
       "      <td>2012-01-26</td>\n",
       "      <td>2013-02-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24697</td>\n",
       "      <td>549847.0</td>\n",
       "      <td>3</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>16</td>\n",
       "      <td>97000.0</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24698</td>\n",
       "      <td>590087.0</td>\n",
       "      <td>4</td>\n",
       "      <td>engineer</td>\n",
       "      <td>10</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24699</td>\n",
       "      <td>777612.0</td>\n",
       "      <td>4</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>11</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>2013-09-03</td>\n",
       "      <td>2015-10-23</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24700 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id  company_id              dept  seniority    salary  \\\n",
       "0          13021.0           7  customer_service         28   89000.0   \n",
       "1         825355.0           7         marketing         20  183000.0   \n",
       "2         927315.0           4         marketing         14  101000.0   \n",
       "3         662910.0           7  customer_service         20  115000.0   \n",
       "4         256971.0           2      data_science         23  276000.0   \n",
       "...            ...         ...               ...        ...       ...   \n",
       "24695      63273.0           1      data_science          5  172000.0   \n",
       "24696      94644.0           6         marketing          7   88000.0   \n",
       "24697     549847.0           3  customer_service         16   97000.0   \n",
       "24698     590087.0           4          engineer         10  160000.0   \n",
       "24699     777612.0           4  customer_service         11   67000.0   \n",
       "\n",
       "       join_date  quit_date  if_quit  \n",
       "0     2014-03-24 2015-10-30      1.0  \n",
       "1     2013-04-29 2014-04-04      1.0  \n",
       "2     2014-10-13        NaT      0.0  \n",
       "3     2012-05-14 2013-06-07      1.0  \n",
       "4     2011-10-17 2014-08-22      1.0  \n",
       "...          ...        ...      ...  \n",
       "24695 2012-05-01 2015-06-26      1.0  \n",
       "24696 2012-01-26 2013-02-01      1.0  \n",
       "24697 2015-02-18        NaT      0.0  \n",
       "24698 2015-10-26        NaT      0.0  \n",
       "24699 2013-09-03 2015-10-23      1.0  \n",
       "\n",
       "[24700 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    13508\n",
       "True     11192\n",
       "Name: quit_date, dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['quit_date'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    13508\n",
       "0.0    11192\n",
       "Name: if_quit, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['if_quit'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data is balanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse the relationship between each feature and average quit rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you see 'start_date' and 'end_date', think of 'duration'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>if_quit</th>\n",
       "      <th>work_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0 2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0 2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0 2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "\n",
       "   quit_date  if_quit  work_days  \n",
       "0 2015-10-30      1.0      585.0  \n",
       "1 2014-04-04      1.0      340.0  \n",
       "2        NaT      0.0        NaN  \n",
       "3 2013-06-07      1.0      389.0  \n",
       "4 2014-08-22      1.0     1040.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['work_days'] = (data['quit_date'] - data['join_date']) / np.timedelta64(1, 'D')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_kws = {'histtype': 'bar','edgecolor': 'black', 'alpha': 0.2}\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.distplot(data['work_days'].dropna(), bins=50, hist_kws=hist_kws, kde=False)\n",
    "plt.xlabel('Work days', fontsize = 12)\n",
    "plt.ylabel('Frequency', fontsize = 12)\n",
    "plt.title('Histogram of working days', fontsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a pattern. Most people who quit the job worked in the company for about a year. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People might get bored in the same place for too long\\\n",
    "Very interesting, there are **peaks around each employee year anniversary**!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JFgCXAD+tqpcn2RX4BrA1cCnwhqp6MMlmwEnAc4A7gNdW1Y2jrFWSJEmjc9HSy1i1es2U+2+5xebs+5y9h1jR4EYarIG3AVcDv9ZufxT4VFV9I8nngTcDn2vv76qq3ZMc1vZ77YhrlSRJ0oisWr2GRbvtOeX+K66/aojVTM/IpoIk2Qn4feBL7XaAA4FvtV1OBA5tHx/SbtM+f1DbX5IkSRpLoxyx/jTw34AntdvbAHdX1cPt9nJgx/bxjsAtAFX1cJJVbf+f9+4wyVHAUQC77LLLUIuXJM2NP9VK0rCMJFgneTmwsqqWJjlgonmSrjWF59Y2VB0PHA+wZMmSdZ6XJHVrLvypVpKGZVQj1s8DXpHkYODxNHOsPw1slWRhO2q9E3Br2385sDOwPMlCYEvgzhHVKkmSJA1sJHOsq+qdVbVTVS0GDgPOqarDgXOBV7XdjgBObx+f0W7TPn9OVTkiLUmSpLE10+tYHwO8I8kymjnUX27bvwxs07a/Azh2huqTJEmSpmTUy+1RVecB57WPbwD2maTP/cCrR1qYJEmStBFmesRakiRJmhMM1pIkSVIHDNaSJElSBwzWkiRJUgcM1pIkSVIHDNaSJElSBwzWkiRJUgcM1pIkSVIHDNaSJElSBwzWkiRJUgcM1pIkSVIHDNaSJElSBwzWkiRJUgcM1pIkSVIHDNaSJElSBwzWkiRJUgcM1pIkSVIHDNaSJElSBxbOdAGSpueipZexavWaKfffcovN2fc5ew+xIkmS5jeDtTRLrVq9hkW77Tnl/iuuv2qI1UiSJKeCSJIkSR0wWEuSJEkdMFhLkiRJHTBYS5IkSR0wWEuSJEkdMFhLkiRJHTBYS5IkSR0wWEuSJEkdMFhLkiRJHfDKi5Im5SXTJUkajMFa0qS8ZLokSYNxKogkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUgYUzXYAkSZI2zkVLL2PV6jUDvWbLLTZn3+fsPaSK5ieDtSRJ0iy3avUaFu2250CvWXH9VUOqZv4yWEuSJA3ZoCPKjibPTgZrSZKkIRt0RNnR5NnJYC1JkuY9R5TVhZEE6ySPB74HbNa+57eq6n1JdgW+AWwNXAq8oaoeTLIZcBLwHOAO4LVVdeMoapUkSfOPI8rqwqiW23sAOLCqngXsBbw0yX7AR4FPVdUewF3Am9v+bwbuqqrdgU+1/SRJkqSxNZJgXY3V7eam7a2AA4Fvte0nAoe2jw9pt2mfPyhJRlGrJEmSNB0ju0BMkgVJfgCsBM4CrgfurqqH2y7LgR3bxzsCtwC0z68CthlVrZIkSdKgRnbyYlU9AuyVZCvgVOCZk3Vr7ycbna7+hiRHAUcB7LLLLh1VKkmSxoknFmq2GPmqIFV1d5LzgP2ArZIsbEeldwJubbstB3YGlidZCGwJ3DnJvo4HjgdYsmTJOsFbkiTNfp5YqNliJFNBkmzXjlSTZHPghcDVwLnAq9puRwCnt4/PaLdpnz+nqgzOkiRJGltTDtZJ/jLJttN8n0XAuUkuBy4GzqqqbwPHAO9IsoxmDvWX2/5fBrZp298BHDvN95UkSZJGYpCpIC8EPtRO4/gacFpVPTCVF1bV5cA6k52q6gZgn0na7wdePUBtkiRJI3PNtcsG67/s+oGms2h2mnKwrqpXJNkGOAw4Gvh8klOAk6rqe8MqUJIkadzc98BDAwXlS6+4eojVjIYnkT62gU5erKo7gOOA45L8Ns3I9ZuS3AJ8EfhMz3rVkiRJmiM8ifSxDbwqSJKDgNfTXMTlEuBjwM3A24B/AX63ywIlSZLUPaezdG/KwTrJJ2imgawCTgL+pqp+2vP8hTSXJZckSdKYm4/TWYZtkBHrxwOvrKqLJ3uyqh5KsqSbsiTNdYPO1YP5OV9PkjR7DBKsPwzc19uQ5MnA5lV1K0BVXdNhbZLmsEHn6sH8nK+nx+YJVZLGxSDB+jTgj3n0dI+dgC8B+3ZZlCRJU+UJVZLGxSBXXnx6VV3R29BuP6PbkiRJkqTZZ5AR65VJdq+qX51CmmR34I7uy5IkSZqeQVe7AFe8UDcGCdYnAKckeTdwA7Ab8N9ppoJIksaA842lwVe7AFe8UDcGCdYfAR4CPgHsDNxCE6o/OYS65rRBv0n7D5+kqXK+8ezjlyFNxjWmZ6dBLmn+S+Dj7U0bYdBv0v7D99j8h2nm+Y+AND3j+GXI36kzzzWmZ6eBrryY5OnAs4Atetur6oQui5IGNY7/MM03/iMgzR3+TpWmZ5ArL74LeC/wQx69nnXRzL+WJEmS5q1BRqyPBvapqsuHVYwkaW7xnJLxMOjUDqdqSdMzSLBeA3hlRUnSlHlOyXgYdGqHU7Wk6RnkAjHvAf7fJIuSbNJ7G1ZxkiRJ0mwxyIj1V9v7P+lpC80c6wVdFSSBf7aUpNnEKT9SY5BgvevQqpD6+GdLSZo9nPIjNQZZx/omgHbqxw5VtWJoVUnSDHDt3vnB0VVJwzLIcntbAf8AvIrmCoxPTPIKmpVC/mZI9UnSyLh27/ww6Ojquf9yul+4JE3JIFNBPg/cBfwG8KO27T+AvwMM1mPEUTdJ6s6wg7jniEhzxyDB+iDgKVX1UJICqKrbk2w/nNI0XY66SaPhlAJNxquQSvPXIMF6FbAt8Ku51Ul26d2WpGEatyDrCVuSpF6DBOsvAackeTewSZLnAh+imSKiIRo0TPhnRc1VBllJ0jgbJFh/FLgfOA7YFDgB+ALwmSHUpR7+WVGaGwb9kgxOH5Gk2WSQ5fYK+HR70xwybn9el+aqQb8kw/wbdZ/Olw//SidpXAyy3N6B63uuqs7pphzNBP+8LmlcTOfLh3+lkzQuBpkK8uW+7e2AxwHLgad2VpEkSZrTPHdIc9UgU0EedUnzJAto1q++t+uiJEnS3OW5Q5qrBhmxfpSqeiTJB2lGrD/ZXUmSpAmO7EnS7DHtYN16EfDLLgqRRskTNjVbOLInSbPHICcv3gJUT9MTgMcDb+m6KGnYPGFzfnC0V5I0SoOMWL++b/sXwLVVdU+H9UhSZxztlSSN0iAnL54/zEIkSZKk2WyQqSBf49FTQSZVVW/cqIqkMeScbEmS9FgGmQpyN3AEcCZwE7AL8AfAicAd3ZcmjQ/nZEsaF16dUhpfgwTrpwG/X1UXTDQkeT7wnqp6SeeVSZKkdXh1Sml8bTJA3/2AC/vaLgKe2105kiRJ0uw0yIj1ZcCHkry3qtYk2Rz4APCD4ZSmueSipZexavWaKff3z5aSJGm2GSRYHwn8E7AqyV3Ak4FLgMOHUJfmmFWr17jsmSRJmtMGWW7vRuA/J9kZeAqwoqpuHlZhkiRJ0mwyyBxrkmwDHADsX1U3J3lKkp2GUpkkSZI0i0w5WCfZH/gxzdSP97TNewCfG0JdkiRJ0qwyyIj1p4HXVtVLgYfbtouAfTqvSpIkSZplBgnWi6vq7PbxxBUYH2SwEyAlSZKkOWmQYP2jJP0XgnkhcEWH9UiSJEmz0iCjzX8FfDvJ/wQ2T/IFmkuaHzKUyiRJkqRZZMoj1lV1IfDbwFXACcBPgH2q6uIh1SZJkiTNGlMasU6yADgbeElVfWy4JUmSJEmzz5SCdVU9kmRXBlz3WnPTNdcuG/w1XqJckiTNcYPMsf4A8Lkk7wOWs3ZlEKrql10XpvF13wMPDRySvUS5JEma6wYJ1l9q79/I2lCd9vGCLouSJEmSZpvHDNZJfr2qfgbsOoJ6JEmSpFlpKiPW1wK/VlU3AST556r6w+GWJc1u05mHvuUWm7Pvc/YeQjWSJGkUphKs07d9wBDqkOaU6cxDX3H9VUOqRpIkjcJUVvmox+6yYUl2TnJukquTXJXkbW371knOSnJde//ktj1J/j7JsiSXJ3n2xtYgSZIkDdNURqwXJnkBa0eu+7epqnMeYx8PA39VVZcmeRKwNMlZwJHA2VX1kSTHAscCxwAvA/Zob/sCn2vvJUmSpLE0lWC9kuZKixPu6Nsu4Kkb2kFVrQBWtI/vTXI1sCPN5dAPaLudCJxHE6wPAU6qqgIuTLJVkkXtfiRJkqSx85jBuqoWd/mGSRYDewMXATtMhOWqWpFk+7bbjsAtPS9b3rY9KlgnOQo4CmCXXXbpskxJkiRpIIOsY73RkmwBnAIcXVX3JP3nRa7tOknbOnO9q+p44HiAJUuWbPRccGkmDbqSiFezlCRpvIwsWCfZlCZU/2NV/XPbfNvEFI8ki2imnUAzQr1zz8t3Am4dVa3STBh0JRGvZilJ0niZyqogGy3N0PSXgaur6pM9T50BHNE+PgI4vaf9je3qIPsBq5xfLUmSpHE2qhHr5wFvAK5I8oO27V3AR4CTk7wZuBl4dfvcd4CDgWXAfcCbRlSnJEmSNC0jCdZV9W9MPm8a4KBJ+hfw1qEWJUmSJHVoJFNBJEmSpLnOYC1JkiR1wGAtSZIkdcBgLUmSJHVgpBeIkSRJ0vwwHy98ZrCWJElS5+bjhc+cCiJJkiR1wGAtSZIkdcBgLUmSJHXAYC1JkiR1wGAtSZIkdcBgLUmSJHXAYC1JkiR1wGAtSZIkdcBgLUmSJHXAYC1JkiR1wGAtSZIkdcBgLUmSJHXAYC1JkiR1wGAtSZIkdcBgLUmSJHXAYC1JkiR1wGAtSZIkdcBgLUmSJHXAYC1JkiR1YOFMFyBpNK65dtlg/Zddz6Ld9hxSNZIkzT0Ga2meuO+BhwYKypdecfUQq5Ekae5xKogkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1IGRBOskJyRZmeTKnratk5yV5Lr2/slte5L8fZJlSS5P8uxR1ChJkiRtjFGNWH8VeGlf27HA2VW1B3B2uw3wMmCP9nYU8LkR1ShJkiRN20iCdVV9D7izr/kQ4MT28YnAoT3tJ1XjQmCrJItGUackSZI0XTM5x3qHqloB0N5v37bvCNzS029527aOJEcluSTJJbfffvtQi5UkSZI2ZBxPXswkbTVZx6o6vqqWVNWS7bbbbshlSZIkSes3k8H6tokpHu39yrZ9ObBzT7+dgFtHXJskSZI0kJkM1mcAR7SPjwBO72l/Y7s6yH7AqokpI5IkSdK4WjiKN0nydeAAYNsky4H3AR8BTk7yZuBm4NVt9+8ABwPLgPuAN42iRkmSJGljjCRYV9Xr1vPUQZP0LeCtw61IkiRJ6tY4nrwoSZIkzTojGbGWJI2na65dNlj/ZdezaLc9h1SNJM1uBmtJmsfue+ChgYLypVdcPcRqJGl2cyqIJEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1AGDtSRJktQBg7UkSZLUAYO1JEmS1IGxDdZJXprkx0mWJTl2puuRJEmSNkfUl1UAAAhTSURBVGQsg3WSBcBxwMuA3wRel+Q3Z7YqSZIkaf3GMlgD+wDLquqGqnoQ+AZwyAzXJEmSJK3XuAbrHYFberaXt22SJEnSWEpVzXQN60jyauAlVfUn7fYbgH2q6i/6+h0FHNVuPh34cUclbAv8vKN9abx4bOcuj+3c5vGduzy2c9dcPra/UVXb9TcunIlKpmA5sHPP9k7Arf2dqup44Piu3zzJJVW1pOv9auZ5bOcuj+3c5vGduzy2c9d8PLbjOhXkYmCPJLsmeRxwGHDGDNckSZIkrddYjlhX1cNJ/hz4LrAAOKGqrprhsiRJkqT1GstgDVBV3wG+M0Nv3/n0Eo0Nj+3c5bGd2zy+c5fHdu6ad8d2LE9elCRJkmabcZ1jLUmSJM0qBuseXkZ9bklyQpKVSa7sads6yVlJrmvvnzyTNWp6kuyc5NwkVye5Ksnb2naP7yyX5PFJvp/kh+2x/UDbvmuSi9pj+832xHbNQkkWJLksybfbbY/tHJHkxiRXJPlBkkvatnn1e9lg3fIy6nPSV4GX9rUdC5xdVXsAZ7fbmn0eBv6qqp4J7Ae8tf3/1eM7+z0AHFhVzwL2Al6aZD/go8Cn2mN7F/DmGaxRG+dtwNU92x7bueUFVbVXzzJ78+r3ssF6LS+jPsdU1feAO/uaDwFObB+fCBw60qLUiapaUVWXto/vpflHekc8vrNeNVa3m5u2twIOBL7VtntsZ6kkOwG/D3yp3Q4e27luXv1eNliv5WXU54cdqmoFNOEM2H6G69FGSrIY2Bu4CI/vnNBOFfgBsBI4C7geuLuqHm67+Pt59vo08N+AX7bb2+CxnUsK+N9JlrZXx4Z59nt5bJfbmwGZpM0lU6QxlmQL4BTg6Kq6pxn80mxXVY8AeyXZCjgVeOZk3UZblTZWkpcDK6tqaZIDJpon6eqxnb2eV1W3JtkeOCvJNTNd0Kg5Yr3WlC6jrlnvtiSLANr7lTNcj6YpyaY0ofofq+qf22aP7xxSVXcD59HMo98qycRgkL+fZ6fnAa9IciPNdMsDaUawPbZzRFXd2t6vpPlSvA/z7PeywXotL6M+P5wBHNE+PgI4fQZr0TS18zK/DFxdVZ/secrjO8sl2a4dqSbJ5sALaebQnwu8qu3msZ2FquqdVbVTVS2m+Tf2nKo6HI/tnJDkiUmeNPEYeDFwJfPs97IXiOmR5GCab88Tl1H/4AyXpI2Q5OvAAcC2wG3A+4DTgJOBXYCbgVdXVf8JjhpzSZ4PXABcwdq5mu+imWft8Z3Fkvw2zQlOC2gGf06uqr9N8lSaUc6tgcuA11fVAzNXqTZGOxXkr6vq5R7buaE9jqe2mwuBf6qqDybZhnn0e9lgLUmSJHXAqSCSJElSBwzWkiRJUgcM1pIkSVIHDNaSJElSBwzWkiRJUgcM1pI0ByQ5IMnyjvb1yiS3JFmdZO8u9ilJ84HBWpLU7xPAn1fVFlV12UwXI0mzhcFaktTvN4CrZrKAnktcS9KsYbCWpCFL8qYkZ/ZsL0tycs/2LUn2ah8/I8lZSe5M8uMkr+npt1mSTyS5OcltST7fXvZ7svf8yyQ/SrLTJM9tkuRvktyUZGWSk5Js2e5/Nc1VD3+Y5PpJXntckr/razszydHt46ckOSXJ7Ul+kuQve/rtk+Q/ktydZEWSzyZ5XM/zleStSa4DrpvSD1eSxojBWpKG73zgd9tAuwjYFHge/OoywFsAlyd5InAW8E/A9sDrgH9Isme7n48CTwP2AnYHdgTe2/9mSd4DHAnsX1WTzbs+sr29AJh4/89W1QNVtUXb51lVtdskrz0ReF2STdr32hY4CPh623Ym8MO2toOAo5O8pH3tI8DbgW2B57bPv6Vv/4cC+wK/Ocl7S9JYM1hL0pBV1Q3AvTSBeH/gu8BPkzyj3b6gqn4JvBy4saq+UlUPV9WlwCnAq5IE+FPg7VV1Z1XdC3wIOKznrZLkk8BLgBdU1e3rKelw4JNVdUNVrQbeCRw2lekXVfV9YBVNKKZ9//Oq6jbgd4Dtqupvq+rB9nN/caLGqlpaVRe2n+1G4Avt5+/14fbzrXmsWiRp3DiHTZJG43zgAJqR5vOBu2lC5XPbbWjmNu+b5O6e1y0EvgZsBzwBWNpkbABCM21jwlbAUcBrq2rVBmp5CnBTz/ZN7fvsAPx0Cp/lROD1NKPrrwc+01P/U/rqXwBcAJDkacAngSXtZ1kILO3b9y1TeH9JGksGa0kajfOBPwB2pRlpvptm5Pi5wGfbPrcA51fVi/pf3E6zWAPsWVXrC7930QTdk5O8sqr+fT39bqUJwRN2AR4GbpviZ/kfwJVJngU8Ezitp/6fVNUe63nd54DLgNdV1b3tvOxX9fWpKdYgSWPHqSCSNBrn08xp3ryd93wB8FJgG5qwCfBt4GlJ3pBk0/b2O0me2U4V+SLwqSTbAyTZsWf+MgBVdR5NYD81yb7rqeXrwNuT7JpkC5qg/82qengqH6St/2KakfRTeqZtfB+4J8kxSTZPsiDJbyX5nfb5JwH3AKvbaTB/NpX3k6TZwmAtSSNQVdcCq2mnRVTVPcANwL9X1SNt273Ai2nmJN8K/IzmhMXN2t0cAywDLkxyD/CvwNMnea+zgDcBZyR5ziTlnEATir8H/AS4H/iLAT/SicB/avcz8b6P0IzK79Xu9+fAl4At2y5/DfwRzXzzLwLfHPA9JWmspcq/ukmSBpPk92imhCxuR9Mlad5zxFqSNJAkmwJvA75kqJaktQzWkqQpS/JMmhMvFwGfnuFyJGmsOBVEkiRJ6oAj1pIkSVIHDNaSJElSBwzWkiRJUgcM1pIkSVIHDNaSJElSBwzWkiRJUgf+D/GN21uLYh9AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot week of the year\n",
    "quit_week = data['quit_date'].dropna().apply(lambda x: int(x.strftime('%V')))\n",
    "# 需要对每列中的每个元素进行函数操作时，使用apply+lambda\n",
    "\n",
    "hist_kws = {'histtype': 'bar','edgecolor': 'black', 'alpha': 0.2}\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.distplot(quit_week, bins=50, hist_kws=hist_kws, kde=False)\n",
    "plt.xlabel('week of year', fontsize = 12)\n",
    "plt.ylabel('Frequency', fontsize = 12)\n",
    "plt.title('Histogram of quit_week', fontsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also **peaks around the new year**. Makes sense, companies have much more money to hire at the\n",
    "beginning of the year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are my analysis, not included in the provided answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compnay_id?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_company = data.groupby('company_id')['if_quit'].mean()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "sns.countplot(x = 'company_id', data = data, hue = 'if_quit', palette = 'Set2', ax = ax[0])\n",
    "ax[0].set_title('Company_count', fontsize = 15)\n",
    "ax[0].set_yscale('log')    \n",
    "# total count of company 11 and 12 are smaller than other company, if not log y, they won't appear on the graph\n",
    "sns.barplot(x = by_company.index, y = by_company.values, palette='Set2', ax = ax[1])\n",
    "ax[1].set_title('Average quit rate across company', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average quit rate in company 11 is much higher than that in other companies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_dept = data.groupby('dept')['if_quit'].mean()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "sns.countplot(x = 'dept', data = data, hue = 'if_quit', palette = 'Set2', ax = ax[0])\n",
    "ax[0].set_title('dept_count', fontsize = 15)\n",
    "# ax[0].set_yscale('log')    \n",
    "# total count of company 11 and 12 are smaller than other company, if not log y, they won't appear on the graph\n",
    "sns.barplot(x = by_dept.index, y = by_dept.values, palette='Set2', ax = ax[1])\n",
    "ax[1].set_title('Average quit rate by dept', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seniority"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2LASYUqomu77XyXUi30ScD5B1HMuBG8gSaSeSGw2UUlpOdudyM9ndzSvInl5xapOEzYeA13LH+C+yjtuLb/IaG32GbH7+P8iudVg7jyNJ0p7qg8DcpokhyEY7kyV/zsotWt3oJrLRz482HQ2U+0P9GLKFpK8k6xf8B9lIl3k7CiSl9CxZgmUycDvZd/r7yZ6Qlu/HZN/xnyW7QdSXrY95X5c7ViIbAXINWd/nTrInqx3LDkaE5NqdQfagimuAnwA/p8lo69wDNz6eO940sj7Lx3d0fTnfIesD3kLWt3h3G9rkn3cp2WcyBPgL2bV9hu0/21+QTee6JneeC3Ptd6bP+UjuuD8hGwXzHM1M20spvUE2IuehlFJrfbDNZDf6Pk/2Of6W7Hfn7Smlqtzx/gmcQDZ18Hqy/uyXyfqbO7MOECml2WRrbg0i+yy+Q9bPPLudh2zt5/tnsumJTUfcSR0iWn64jyTtPrl1AV4Frkkp/XtnxyNJkgpDRFxFdsNon110/FuBASmlKbvi+HuTiOhHNpLp4pTS1Z0dz54kIh4jG3X/0c6ORXsnp5VJ6lQRUQYcSnaHrz/ZnTlJkqQOFxETyRbDfphsGtk7yUYcfaUz4yp0uXUhJ5CNAlpP2x9MsteLiEnAyWRPlvtcJ4ejvZjJIUmdbRjZ+gLLgE83Xcso9/SJFqfA+hhPSZL0JmwkW7flYqAn2ajlr5BNm1fnORK4l+zn8bGdXDNzbzOTbO2sr6WUZnZ2MNp7Oa1M0h4tt1DjeTuoMqYNTzCRJEmSJLXA5JCkPVpEjCZb2LklPgVMkiRJknaCySFJkiRJkqQCtsetOTRgwIA0evTozg5DkiTtQo8//viKlNLAzo5DW9kHkyRp77aj/tcelxwaPXo0s2bN6uwwJEnSLhQRr3Z2DNqWfTBJkvZuO+p/tfgEIEmSJEmSJO39TA5JkiRJkiQVMJNDkiRJkiRJBczkkCRJkiRJUgEzOSRJkiRJklTATA5JkiRJkiQVMJNDkiRJkiRJBczkkCRJkiRJUgEzOSRJkiRJklTATA5JkiRJkiQVMJNDkiRJXUxETI2IFyNiXkR8tYU6H4iI5yNidkT8bnfHKEmSuo42JYda64BERLeIuDm3f0ZEjM6Vfzginsp7NUTEYR17CZIkSYUjIoqBK4B3AhOAD0bEhCZ1xgFfA45LKR0EfGG3BypJkrqMVpNDbemAAJ8AVqeU9gN+DHwPIKX0vymlw1JKhwEfBRaklJ7qyAuQJEkqMEcD81JK81NKNcBNwJlN6nwKuCKltBogpbRsN8coSZK6kJI21NnSAQGIiMYOyPN5dc4ELsu9vxX4eURESinl1fkgcONORyztRjMef5K1G6ra1bayogeTjzy8gyOSJInhwOt52wuByU3qjAeIiIeAYuCylNIduyc8tWTxY6e1q92wo6d3cCSSJG2rLcmhtnRAttRJKdVFxFqgP7Air845bH9XS9qjrd1QxdCxB7Wr7ZKXZ3dwNJIkARDNlKUm2yXAOGAKMAJ4MCImppTWbHOgiAuBCwFGjRrV8ZFKkqQuoS1rDrWlA7LDOhExGdiUUnqu2RNEXBgRsyJi1vLly9sQkiRJUsFaCIzM2x4BLG6mzl9SSrUppVeAF8mSRdtIKV2ZUpqUUpo0cODAXRawJEnas7UlOdTWDshIgIgoASqBVXn7z2UHU8rsmEiSJLXZTGBcRIyJiDKyfta0JnX+DJwEEBEDyKaZzd+tUUqSpC6jLcmhtnRApgHn5d6fDdzTuN5QRBQB7ydbLFGSJEk7IaVUB1wM3Am8ANySUpodEd+KiDNy1e4EVkbE88C9wKUppZWdE7EkSdrTtbrmUG4NocYOSDFwTWMHBJiVUpoGXA1cHxHzyEYMnZt3iBOAhY0LWkuSJGnnpJSmA9OblH0j730CvpR7SZIk7VBbFqRuSwdkM9nooOba3gcc0/4QJUmSJGnXm/fL+9vVbr+LTuzgSCRp92rLtDJJkiRJkiTtpUwOSZIkSZIkFTCTQ5IkSZIkSQWsTWsOSZIkSdp73H777e1qd/rpp3dwJJKun3Fhu9p9dPKVHRyJCpkjhyRJkiRJkgqYySFJkiRJkqQCZnJIkiRJkiSpgJkckiRJkiRJKmAmhyRJkiRJkgqYTyuTJEmSJLXIp9tJez+TQ5IkSVIrfNS0JGlv5rQySZIkSZKkAubIIUmSJEmdZtkvv9yudoMuuryDI5GkwmVySFKnm/H4k6zdUNWutpUVPZh85OEdHJEkSZKkznTJ365rV7sfvvNjHRxJYTA5JKnTrd1QxdCxB7Wr7ZKXZ3dwNJIkSZJUWEwOSZIkSVIHcgFzSV2NC1JLkiRJkiQVMEcOSZIkSeryfnHDP9vV7rMfOb6DI9mzzHvw2na12++tF3RwJHufeb+8v13t9rvoxA6ORNp5JockSZIktYsLxu46ix87rV3thh09vYMjkVQITA5JkiRJXYgjQSRJHc01hyRJkiRJkgqYI4e6slntu2sEwCTvHEmSJEmStnJk4q6zp69RZXJIkiRJklRQXMBc2pbTyiRJkiRJkgqYySFJkiRJkqQCZnJIkiRJkiSpgJkckiRJkiRJKmAuSC1JkiRJ2uUu+dt17Wr3w3d+rIMj0a50++23t6vd6aef3sGR6M0wOSRJkiRJktSCQni6nckhSZIkdahlv/xyu9oNuujyDo5EkiS1hckhSZIkSZK6sMWPndaudsOOnt7BkairckFqSZIkSZKkAmZySJIkSZIkqYA5rUySJEmS1CW4ppm0azhySJIkSZIkqYC1aeRQREwFfgoUA1ellL7bZH834DrgSGAlcE5KaUFu3yHA/wC9gQbgqJTS5o66AEnaoVnX7lz7SRd0TBySJEmStIdqNTkUEcXAFcCpwEJgZkRMSyk9n1ftE8DqlNJ+EXEu8D3gnIgoAW4APppSejoi+gO1HX4VkiRJkiRJeZyG2HZtmVZ2NDAvpTQ/pVQD3ASc2aTOmcBvc+9vBU6JiADeDjyTUnoaIKW0MqVU3zGhS5IkSZIkaWe1JTk0HHg9b3thrqzZOimlOmAt0B8YD6SIuDMinoiI9qXtJEmSJEmStEu0Zc2haKYstbFOCXA8cBSwCbg7Ih5PKd29TeOIC4ELAUaNGtWGkCRJkiRJktQR2jJyaCEwMm97BLC4pTq5dYYqgVW58vtTSitSSpuA6cARTU+QUroypTQppTRp4MCBb/4qJEmSJEmS1C5tSQ7NBMZFxJiIKAPOBaY1qTMNOC/3/mzgnpRSAu4EDomI8lzS6ETgeSRJkiRJkrRHaHVaWUqpLiIuJkv0FAPXpJRmR8S3gFkppWnA1cD1ETGPbMTQubm2qyPiR2QJpgRMTyn9dRddiyRJUkGIiKnAT8n6ZlellL7bZP/5wPeBRbmin6eUrtqtQXaAX9zwz3a1++xHju/gSCRJ2ru1Zc0hUkrTyaaE5Zd9I+/9ZuD9LbS9gexx9upg8197narqmna13RBPMvnIwzs4IkmStKtFRDFwBXAq2RT+mRExLaXUdHT2zSmli3d7gJIkqctpU3JIe6aq6hoqBw5rV9tFG6o6OBpJkrSbHA3MSynNB4iIm4Azceq+JElqJ5NDUqGYde3OtZ90QcfEIUnaWcOB1/O2FwKTm6n3vog4AXgJ+GJK6fWmFXxirCRJApND6opMckiSCls0U5aabN8G3JhSqo6IzwC/BU7erlFKVwJXAkyaNCkBXPK369oV1A/f+bF2tZMkSZ2vLU8rkyRJ0p5jITAyb3sEsDi/QkppZUqpOrf5a+DI3RSbJEnqgkwOSZIkdS0zgXERMSYiysieEjstv0JEDM3bPAN4YTfGJ0mSuhinlUmSJHUhKaW6iLgYuJPsUfbXpJRmR8S3gFkppWnAv0TEGUAdsAo4v9MCliRJezyTQ5IkSV1MSmk6ML1J2Tfy3n8N+NrujkuSJO1a18+4sF3tPjr5yh3ud1qZJEmSJElSATM5JEmSJEmSVMBMDkmSJEmSJBUwk0OSJEmSJEkFzOSQJEmSJElSATM5JEmSJEmSVMBMDkmSJEmSJBUwk0OSJEmSJEkFzOSQJEmSJElSASvp7AAkSZKkXWXeL+9vV7v9LjqxgyORJGnP5cghSZIkSZKkAmZySJIkSZIkqYCZHJIkSZIkSSpgJockSZIkSZIKmMkhSZIkSZKkAmZySJIkSZIkqYCZHJIkSZIkSSpgJockSZIkSZIKmMkhSZIkSZKkAlbS2QFIkna9GY8/ydoNVe1qW1nRg8lHHt7BEUmSJEnaU5gckqQCsHZDFUPHHtSutktent3B0UiSJEnakzitTJIkSZIkqYCZHJIkSZIkSSpgJockSZIkSZIKmMkhSZIkSZKkAmZySJIkSZIkqYD5tDJJ0nb6zvnfLe+Lli+GWS++uQNMuqCDI5IkSZK0qzhySJIkSZIkqYC1KTkUEVMj4sWImBcRX21mf7eIuDm3f0ZEjM6Vj46Iqoh4Kvf6VceGL0mSJEmSpJ3R6rSyiCgGrgBOBRYCMyNiWkrp+bxqnwBWp5T2i4hzge8B5+T2vZxSOqyD45YkSZIkSVIHaMvIoaOBeSml+SmlGuAm4Mwmdc4Efpt7fytwSkREx4UpSZIkSZKkXaEtyaHhwOt52wtzZc3WSSnVAWuB/rl9YyLiyYi4PyLeupPxSpIkSZIkqQO15WllzY0ASm2sswQYlVJaGRFHAn+OiINSSuu2aRxxIXAhwKhRo9oQkiRJkiRJkjpCW0YOLQRG5m2PABa3VCciSoBKYFVKqTqltBIgpfQ48DIwvukJUkpXppQmpZQmDRw48M1fhSRJkiRJktqlLcmhmcC4iBgTEWXAucC0JnWmAefl3p8N3JNSShExMLegNRGxLzAOmN8xoUuSJEmSJGlntTqtLKVUFxEXA3cCxcA1KaXZEfEtYFZKaRpwNXB9RMwDVpElkABOAL4VEXVAPfCZlNKqXXEhkiRJkiRJevPasuYQKaXpwPQmZd/Ie78ZeH8z7f4A/GEnY5QkSZIkSdIu0pZpZZIkSZIkSdpLmRySJEmSJEkqYCaHJEmSJEmSCpjJIUmSJEmSpAJmckiSJEmSJKmAmRySJEmSJEkqYCaHJEmSupiImBoRL0bEvIj46g7qnR0RKSIm7c74JElS12JySJIkqQuJiGLgCuCdwATggxExoZl6vYB/AWbs3gglSVJXY3JIkiSpazkamJdSmp9SqgFuAs5spt63gcuBzbszOEmS1PWYHJIkSepahgOv520vzJVtERGHAyNTSrfv6EARcWFEzIqIWcuXL+/4SCVJUpdgckiSJKlriWbK0padEUXAj4FLWjtQSunKlNKklNKkgQMHdmCIkiSpKzE5JEmS1LUsBEbmbY8AFudt9wImAvdFxALgGGCai1JLkqSWmBySJEnqWmYC4yJiTESUAecC0xp3ppTWppQGpJRGp5RGA48CZ6SUZnVOuJIkaU9nckiSJKkLSSnVARcDdwIvALeklGZHxLci4ozOjU6SJHVFJZ0dgCRJkt6clNJ0YHqTsm+0UHfK7ohJkiR1XY4ckiRJkiRJKmAmhyRJkiRJkgqYySFJkiRJkqQCZnJIkiRJkiSpgJkckiRJkiRJKmAmhyRJkiRJkgqYySFJkiRJkqQCZnJIkiRJkiSpgJkckiRJkiRJKmAmhyRJkiRJkgqYySFJkiRJkqQCZnJIkiRJkiSpgJV0dgCSJO0xZl27c+0nXdAxcUiSJEm7kSOHJEmSJEmSCpjJIUmSJEmSpAJmckiSJEmSJKmAmRySJEmSJEkqYCaHJEmSJEmSCpjJIUmSJEmSpAJmckiSJEmSJKmAmRySJEmSJEkqYG1KDkXE1Ih4MSLmRcRXm9nfLSJuzu2fERGjm+wfFREbIuJfOyZsaefU1jewfnPtlu0X31jP84vXMX/5BhatqWLlhmrq6hs6MUJJkiRJknaPktYqREQxcAVwKrAQmBkR01JKz+dV+wSwOqW0X0ScC3wPOCdv/4+Bv3Vc2FLrGhIURfb+u8/2ZPaaEpZsKmbl9LtYvamWdx0ylCs+dAQAZ//yYdZX123T/qwjhjN1MKSU+M5fX2Bgr24M69OD4X16MLp/Of16lhERu/uyJEmSJEnqUJpbbzAAACAASURBVK0mh4CjgXkppfkAEXETcCaQnxw6E7gs9/5W4OcRESmlFBHvAeYDGzssaqkZm+vhwaVlPLysjFkrS6ksTdxwwhoAZq0opbohGNe7jmNHDqN/RRmHjuizpe2PzzmMuoYGqusa2FxbT1VNPfsOrGDzkrnUNSRWbazhucVr2VRTv6XNOUeN5COT96G2voGnX1/D2EEV9C0v2+3XLUmSJEnSzmhLcmg48Hre9kJgckt1Ukp1EbEW6B8RVcBXyEYdtTilLCIuBC4EGDVqVJuDlxr97PlyrnypnA11RXQvThzer5ZjB9Vs2X/rSWu2Vp40cbv2b5swuNnj3rVkLqXFRfz4nMNIKbGmqpbFa6qYv3wj4wZXAPDKio38x+1ZrnRARTfGDapg3KAKDuhW3+wxJUmSJEnak7QlOdTcvJnUxjr/Afw4pbRhR9NvUkpXAlcCTJo0qemxpe2srQlufqU7Hx67mZ4liUE9Gjh9ZDXvGrGZyQNrKdsFS61HBH3Ly+hbXsZBwyq3lO/Tv5zvnnUwc5dtYO7SDcxdtp5H5q/kS0dnyaNHXl7JjY+9xiEjKjloWCX7DuzJoF7dOmxK2vrNtazeWMuaqhrWVtWyqaae8rJi3jpuIAALVmwkAvrXBj1LEs6Ek3Zs/muvU1Vd03rFZmyIJ5l85OEdHJEkSZK0a7UlObQQGJm3PQJY3EKdhRFRAlQCq8hGGJ0dEZcDfYCGiNicUvr5TkeugrRyQzVXPFXBTa90Z1N9ESN7NvDOEdWcO2Yz547Z3CkxdSsp5qBhldskjDZsrmP163MAWLZ+MzMXrGLa01v/sykvK+buS05kaGUP/v78Uh57ZSV9yssoKQqKi4KSouD848YAcPszi5kxfxVrq2pZU1XL2k01JGDaxccDcOnvn+GO2W9sE9Owyu48/LVTAPj3vzzHg3NXAAMpL25gv971TB5Qy78dugGAugYo8bmF0hZV1TVUDhzWrraLNlR1cDSSJEnSrteW5NBMYFxEjAEWAecCH2pSZxpwHvAIcDZwT0opAW9trBARlwEbTAypPeobEtc+9Ao/vXsum6p7cObIzXxyfBUT+tS13rgTVHQvYX1uNewzDxvOmYcNZ9n6zcxZsp4FKzfyyoqNDKzoBsCzi9Zy3SOvUl237dPRPnzMPpQWF/HEq2u4/ZnFVPYopbK8jD7lZfTvWZZXbxRvmzCYyh6l9CkvpUdpMSXFW4cHff6UcZx52HBWzHmIN6qKmLeuhGWbt2aDTr+7L8UBRw2o5S2DanjLwFoqSh3AJ0mSJEmFotXkUG4NoYuBO4Fi4JqU0uyI+BYwK6U0DbgauD4i5pGNGDp3VwatwlMUcNfzSzl8VF/+ffQcxvXueuv5DOrVnUG9unMCA7cp/9Kp4/nSqeOprqunviFR15Cor0+U5JJL33j3BL7x7gktHrdx+lhLJo3ux6TR/SBt2m5fSvCOYTXMXFHKza/04DfzyimJxOcO2MQXD8rWkM9/6pskSZIkae/TlpFDpJSmA9OblH0j7/1m4P2tHOOydsSnPUlDA6QGKC6B5/8Cj/06yy6QoKgY+u8Hx30e+o7ukNNtqqnjJ/+YyyePH8Og3t255vyj6FlWTDw+u0OOv6fpVlK8288ZwZYkUHU9PL6ylAeXlnFw31oAXttQxHvv7cfxg2o4IRby1nEDGNS7+26PU5IkSZK067QpOaQCt2kVPPhDePIGeN9VMO5U6D08SxQBEFC7GZ79Q5YcAnjyf+GFaXDA6bD/adCz/5s65ROvreaSW55mwcqN7Deogg9MGklFN39dd6VuxfCWQbW8ZVDtlrK6FJwwuIYHl5bxl98/DcABQ3rxg/cfysThlaSUOmxhbUmSJElS5/CvbbWstgpm/Aoe/DFUr4OJZ0HP3BSmEZPggunb1k9569Q01MLS5+GlOyCKYL9T4ehPwX5vY0ePy0opceUD87n8zhcZ0rs7v/vkMRw79s0lltRx9u1Vz4+PXkdDgueHncUDc5fzwEvLGVKZjR66+p+vcONjr3HoiD4cOLQ3Bw7tzQFDe9G/Z5lJI0mSJEnqIkwOqXn1dXDlSbD8BRg/FU75Jgxued0bYNukz5HnwxHnwZKnsyloT94A93wnSw4B1FVDSbftDvHrB+fz33+bw2kHD+G77zuE3t1LO+6a1G5FAROHVzJxeCWfnbLflvIRfcvZp39PHnp5BX98chEAJUXBnG9PpaQ4uPGx15i3bAMDe3VjUK9uDOzVjcG9uzN+cK/OuhRJkiRJUhMmh9S84hI47XIgYMxbW63erAgYdlj2mvI1WL84K6taDT87IptudvQnYdjhW6YnnXPUKHp2K+FDR49y5EkXMHXiEKZOHALAyg3VzHljPYtWV1FSnD0N7cnXVnPb00uoqt26gPjIfj148MsnA/DFm59i3rINULOJIa/OpW95GSP79mDK/oMAqK1voLS4CEmSJEnSrmNySNt67o+wcQVMvhDGnNBxxy0p27pQdV0NTDgTnrkFnrqBdf0P5XfpHZz/qS9S2aOcD0/ep+POq92mf0U3jttv29Fgl599KJeffSgbq+tYvr6aZeurqa1v2LJ/n/7lrN5Uw8tLNrBowWrWVNVw4NDeW5JDX7rlKdZvrmNkv3L2G1jBgUN7ccCQ3vTu4YgySZIkSeooJoe01axr4fYvwj5vgaM+kT2BbFfoNRje/RPS2y5jxp+vYOAL1/OZostZufR0uo8+NEselZTtmnOrU/TsVkLPbiWMHtBzm/IvvG08AHfd/zBDxx5EfUOium7rKKNTDhzM/OUbeHXVJv701CJufSJxwriBXPqO/QGYvXgtvRsSkiRJkqT2MzmkzKuPwF8vyZ5E9oHrdl1iKKe2voFv3vE6v3v6MN5+4Nv52YmJ/qMPzXb+8VOwbjEceg4cdBaU99ulsWynag2sXgCrFzD61fvpt/wPLD3iSzSUVdB3zu/o9+LNREMt0VALqYFUXMb8d91CQ1kv+sz9IxUL76e+WyU9NtVB9+ey+A96b/aZ1m7O1lpyylyziouC8rKt/1t6z2HDt7yvrqtn7tINdC/NfjffWLuZr/7xWboVw18XP8G7Dx3GSQcMpFvJrv3dlSRJkqS9jckhZdPIbv049BmVPaq+tMcuP+U3/vIcNz72OhdNGculb9+foqK8ZMmoY+CJ67Jk1fQvw4ij4PCPwBEf7ZiT19fBuoVbEkCsXgBHfxp6D4XHfg3T/3VL1fFAfUk5Kw76OA1lFTSU9qS+W29SlJCKSiGCaKjL3gMlm5bSc9ksiqvXMqBmHSy4AaIYJr4vO+DfLs2m0/UeBr2HZ6++o+Gkr2X71y7KPv8efdudQJrx+JOs3VC1XfnwxS+32rZHtzL2HTWyXefd1bqVFDNxeOWW7b49S/n3dx3Ifc+8zKPzV/LXZ5fQq3sJV31sEpP39Ql3kiRJktRWJocEL9+bLRL9iTuhe2Xr9TvAp966L0eN7sdZR4zYfucxF8Hkz8Abz2ZPOnv57iyBA1CzCR78IfQaAhVDoHsvKO2ZJVt69M2egrZuMaT6rG71OqheD0MPy5I/S5+H7wzK9jcqKoX9Ts32jzoGTv12lrDpO5p7nl/KwP2P3lJ17dgzWTv2zBava8WhF7Hi0IsAeGPu05x61IGwee3WRM/4qdlnvHYRrFsErz4EC2duTQ7d9i8w7x9Q0iOLp/dwGHoovOM/s/2vPwbFpVl5+QAo2n6x5rUbqhg69qDtyitrn2ox7i1tly9utc6eIOqrKa/bxHEDNrHPgaVMOfEEZj3zDHMev5+DVyyD5VXMXrCINatX0WfkBCYM6k6sfAkWPwVB9jMvLss+y32Og7Ke2e9JQ1328wkXwZYkSZJUOEwOCQ55f7b4dK/Bu/Q0D7+8grtmL+Wb757AvgMr2HdgRcuVI2DoIdnrlH+HlFtXZvMaKCuH5S9lSZVGE8+G0cfDxuXw8E+bHgwqBmXJlopBcPwXcsmfMdm/vYdtnUY35ODslVP30sPtvt5UVAIVA7NXowPelb22qZi3Zs6xF8PYU7Inu61rfC3aun/av8DyF7L3RaXZNY2fCqd9Pyub8T+MWPQqPYteo67HgOzVvR8N3XZP0m879XXZv8UlUFsFq16Bmo1QsyH378YsIQeUrZ1Pvzk3UlS7kaK6jRTVbqKodiNLj/oKVQMPpderf2fEA5dk+xtqt5xi7RE/oqR4CsekZzlm4TdgYVZ+EFCbijnt1f+mprwf/9ZnHSeve5aSSFBfC/U1QIJRx2YNFvwT5t6ZJY0qhmQJyMrhcOi5u2U0nSRJkiR1FpNDhWzhrOyP9H2n7NLEUEqJax9awH9Of4F9B/RkXVUdleVv8mlTjSNveg+DydnIHGo35RIMm6BHn6ysfEA2RayoGErLoVsvKKvYmvzpOQAmXdAxF9ZR8qePjT0pe7XkrCthzWuwfkmWNFq7CHoO2rr/3v9iwuY18NLWotX7ncXCKT8BYPCsH5CKSkkl3Wko7k4qKmFzvwPZNPgIaKhnyLIHoG42FOX+19BQDwPGw8D9sxFQd387G3VVV5N9/rVVcNiHYMIZWeLnhrOyssZ99TXw7p/Bkedlo7auOnn7a3rf1cAwSjcupf8L11Ff0pOG0nIaSnvSUFJONGQJptqKYazd9wzqS3vSUJpN8Wso7UlVw5DsOPufBp95CLpVQFkvKOvJxprgk9P/yG2vN/CZRe9k0oC3ccuUNQAsqwoGldVu/d0Yeih07w3rl8KGN2D5HHjjmSwJB/DE9dmIogPetfX3TZIkSZL2AiaHClVqgOmXZiNt/uWpbGTHLlBVU8/X/vgMf35qMadOGMyPPnAovbq37zHkjWvpNL92zorcC6DxSWfVudeKPXotnTelcTRVSy59mfvuvoPhg/pSUrWCks0rqanITd1LDdT0Hk1RXRVRv5nimrVEQz21Fdmiz9FQR5+1L8Dahmx6FZElQ4pLs+RQXQ08d2u2hlJxWTaCq7Q8S9BBNjVr2BHZKJvS8q37h+YWGu+/L7z/N7nETXlWv6wiG6HzyNNsHHYss89/scVL29z/IBYf953tymtenp296dk/e+XpUwrnjNnMOWM2s3xzsLI6my62ujp4y/QB7F9Zx0lDajhhcA2H9x9Gae9hTQ6+Yet/G7P/CC/fA7eXZaO7Dj4bDnx3tsC4JEmSJHVhJocK1JBlD8DiJ+A9v9pliaGUEh//zUwefWUll5w6ns+dtN+2C0+/SY1r6bRl7Zzt2naRtXR2WnEJNd36sXnA9msOEUWsHn92i01TSTfmjPs0B40b23yFioHwlQUtn7tiEJx9dcv7e/TNntrWSQZ2Twzsnq01VVwEXz14A3cs6sYvXyzn53N6UlHSwM8mr+PkoTVsqoPahqCyLG/q40f+CIseh+f+CLP/BC/9DQ45F876n066IkmFLCKmAj8FioGrUkrfbbL/M8DngHpgA3BhSun53R6oJEnqEkwOFaKGWsbNvzZbW+eQczr88Cm3hk5E8JkpY7nwxH05af9BrbSSdp/epYlPjq/ik+OrWFsTPLK8jPvfKGNUzyx5dOeibnxxZiXDetQz7pnHGD+4grEDKzjtkEPpPXUSvP07MP9eKM+NVFr5MjzwfTjhUujfQnJNkjpIRBQDVwCnkq20NjMipjVJ/vwupfSrXP0zgB8BU3d7sJIkqUswOVSAKhY/Qo/Ny+Dtv272aVc7Y+2mWr7+52eZOKySi6aM5cTxA1tvJHWiyrLE1OHVTB1evaXs4L51XDpxA3PXlfDS+moenb+S6roGTjpgEL27l/KrB1/hdzOKGNWvmpH9nuXE+kc5dc6fKHrmFuKwD2ZJor6jO++iJO3tjgbmpZTmA0TETcCZwJbkUEppXV79nkBCkiSpBSaHClBDSQ8WDz6ZYftO6dDj3jX7Db45bTbL11dzyPBOejqW1AH2613Pfr03ZRuT3kd9Q2LxmioGVmTrC43u35PDRvbhtVWbuGv2G9y4cRhDi3/MQ8c/Q8y6hvqnbuSunmfy+P7/yoThlUzapx8j+/Ugov3TKiUpz3Dg9bzthcDkppUi4nPAl8gW42vmiQCSJEkZk0MFaNOQo3h+5GEMa71qmyxcvYnLps3mHy8sY//BvfjVR47k0JE+zUl7j+KiYGS/8i3bUycOYerEIVu2N1bXsWTtZooGfQiO+xfm3PR/qV9ZzXUzXqOmroGggYnD+3Lb/zkegM219XQvLd7t1yFpr9Fcpnm7kUEppSuAKyLiQ8D/Bc7b7kARFwIXAowaNaqDw5QkSV2FyaECUlSzju5r5rFpwA6edtVWs67d8nbVqhIenduHrx+8kQvGLaN06cuwtJX2e9rj5KWd0LNbCfsNyi1e3XsYB114DQelxNSGxKJn76Py75fwyD5fAbI1ud7xkwfoU17GqQcO4t2HDmOf/j07MXpJXdBCIP8RnCOAHT154Sbgl83tSCldCVwJMGnSJKeeSZJUoDp2wRnt0SqWzKDvS7dSXL1mp45TU9fAdfN68J9PZ38MH9KvjofftZIL96+i1N8oKRNBSXER+/TpRp+yBt75xKfh9xdQu3oR7z9yBAA/uOslTvz+fZz9y4d54KXlnRywpC5kJjAuIsZERBlwLjAtv0JEjMvbfBcwdzfGJ0mSuhhHDhWKhjp6vvEYm/vuT32PAe07REPitmcW88O7XuK1Vb04dmANtQ1QWpQ9/UlSM0YfB5+dAQ/9BB78EWVz7+Lit13GxZ/7FEvWVvGnJxfxh8cXsqkme1La8vXVrNxYzQFDendu3JL2WCmluoi4GLiT7FH216SUZkfEt4BZKaVpwMUR8TagFlhNM1PKJEmSGpkcKhDlK56huHYDG4Yf1672zy9exyW/f5oXlqzjwKG9ufa4NUwZUoPr60ptUNodpnwVDjkH/vYVqK0CYGhlDz47ZT8uOnEsKZdfve6RBfy/e+YxeUw/LjxhX04+YJALWUvaTkppOjC9Sdk38t5/frcHJUmSuiyTQ4UgJSoWP0xtj0FUV459U00bGhJFRUG/nmXUNzTw03MP492HDKPoid+0K5T5r71OVXUNizY+/Kbbzpn3MkPHHtSu80p7hH5j4MO3QENDtv3srfDSncTbv0P0GgzAx48bQ89uJVz/yKt84rezOGBILy4+eT9OP6SjlpCXJEmSpG2ZHCoARXWbiIY6Ngx7C20d6lNVU8/P753Ls4vW8dsLjmJIZXfu/MIJOz2Coaq6hsqBw2hoR5LniWdf2KlzS3uMotziXBuWwfN/hpfugJO+Dkd9ir49y/jMiWP5xPFjuO3pxfzivpeZ/uySLcmh+oZEcZEjiSRJkiR1HJNDBaChtCdLD/880NCm+rMWrOILNz/FwtVVnHXEcKrrGuheWuzUFqmjHftZGP8OmH4p3PFVePIGePdPYcQkSouLOOuIEbznsOGs31wHwLxl6znvmpl84vgxfPDoUfQoK+7kC5AkSZK0NzA5tJeLuiogSCXdydas3LGbZ77G//3zcwzr04MbP3UMx47tv8tj3FvNeWleu9tWVvRg8pGHd2A02mP1Hwsf+QO8cBvc8TVYtwiYtGV3UVFQWV4KQHVdA8P79uBbtz/PFffO4xNvHcNHj9mHXt1LOyl4SZIkSXsDk0N7uV6LHqRi8SMsOeoruQRRyzbV1PGzu+dxzL79+fkHj9jyB6naZ1N1bbvXSFry8uwOjkZ7tAiYcAbs9zYo7ZGVPfADWL0ATvwy9BkFwEHDKrnl08cyc8Eqfn7PPC6/40V++/ACHvjySXQrcRSRJEmSpPYxObQ3Sw2UL3uSmt6jdpgYqq1voKQoKC8r4ZbPHMvgXt0oKS7ajYFKAqCsfOv7ump4+iZ4+kaYeDYc93kYPAGAo0b347cfP5pnF67lhTfW0a2kmJQSv3l4Ae86ZCiDeu04ESxJkiRJ+UwO7cXK1r9GSfUa1u3z9hbrNDQkvnTL03QvKeLysw9heJ8euzFCtWRnpqT5VLe9xMn/BkeeB49cAY//Bp65CU79VpYkyjl4RCUHj6gEYN6yDXz79uf577/N4QOTRvDx48aw78CKTgpekiRJUldicmgvVr78aRqKSqnqN6HFOj/6+0vc9vRivjx1/716wek3li7f8n7Rxofb3K6zEi07MyWts57qNuPxJ1m7oapdbU1otaByBEz9bzjhUnjsShhxdFa+bE42ouiQc2DQgRDBuMG9uOeSKfzyvpe5ZeZCbnj0NU45YBDfee9Ehlaa9JUkSZLUMpNDe6tUT4/lz7K534Gkkm7NVrll1uv8/N55nDNpJBedOHY3B7h71dTVUzkwexR4w5tIQnRWoqUrWruhqssltLqM8n4w5atbtxc+Bg//P3joJ9l6ROOnwvh3MHrMFL539iFc8o7x3PDoa/zt2SX0LS8DYMHaOio217p4tSRJkqTtmBzaaxWxYuInSC2MBlr50j/59/kTOH5QLd8Z9Tjx+OO7OT5J7XbEx7KE0IvT4cU74Inr4ckb4KuvAzDotTv40qDNfPGc/YmGKlLqyVVPbWT1jMd4y9gBnLz/IA4d2Yfior13tKAkSZKktjM5tLeKoLZiaIu7q+qLObJ/Lb84di2lrj2tnPzpd021Nh2v0KaG7cw0usqKHkw+8vCdC6BiEBx5fvaqrYKls6E497/0Gb+C1x6hMfUTvYdzfd8J/LTf17n/pWWsmDuTm7r34e3HHsnbJrT8/wlJkiRJhcHk0F4o6mvoM/821g87jrqeQ5qtM7lyNR+ftGY3R6Y9Xf70u6Zam45XaFPDdmYa3ZKXZ3dsMKU9YMSkrdvn3Q4r58KKubDiJVgxl7LVdVx04lg+cdwYxt/4eXpVv0Hdo2XUPb8P63qM5O/1h9Nw+HlMHNabbrVrISXYi9chkyRJkrSVyaG9UPdVc+i5dBabBh5OXZN98zeU8ujKHryjx5JOiU3SblBcki1UPejALUVz73+YoUBZSRHLT/kJa9e9Qre1Cyhbt4CSFfOoXt+Db/3lOfp0g1nxMeqKu9NQMZj6imGkhp6weQB0r+y8a5IkSZK0y5gc2guVL3+KurLeVFeO3qa8IcH3X+jHa5tKeev44s4Jbg/Qd87/trnuoZufoO+c6nadp7m2qw/4cLuOJXWkjcPewsZhb9mm7ODaer7++hqemLuI77/2EfatWcAZdfOoWPIIvRrq2NCzlIoDTsmmsC2aBf3HQcVgRxdJkiRJe4E2JYciYirwU6AYuCql9N0m+7sB1wFHAiuBc1JKCyLiaODKxmrAZSmlP3VU8Npe1FXRffVLbBj2FohtFxO6fXEFz67tztcnrKB3SdMxRZIKWffSYo7dtz/H7tufuvqJrHz8/7d35/FRVff/x1+fyZ5AFiDsYUcWF1ZBBBTFDbVQq7aotVq1tra2X2vbb3e7+qt+bautbb9+VbTWutUdF+qOiIqyiEJkkSVAgISwk32Z8/vjDhjTJMxMJrmZ5P18POYxd+7ce/K+mZOZmzPnnvMkezKrIVjHbUvKeH91X3rtyOTqzI18ruhJb6eUTMgdCb2O9e6bmBlRRERERETat6MORWxmCcBfgVnAaOASMxvdYLOrgX3OuWHA7cCtofWrgYnOubHAOcD/mZl6K7WitD0fY66O8h7Hf2b9wZoA//tJNmOzK5nVp8yndCISDxITAozMrPYeBBI4q3+Qy45NwTn4dsFJTK+6nYe7XAndh0LxKlh+P5Tv8bYv2w0V+3zLLiIiIiIikQunoWYSsME5twnAzB4F5gAf19tmDvDL0PITwF/MzJxz5fW2SQVcixNL81yQqq551HTp/5nVT27ryqHaBG4YUayrQOJUuJfDteRSuJb8/OZ+bmteTrd2/YZmn++3Y2OTz6WlJDNkQF7zP2DZ/U2Wm1WzstldO8plhIPSyjlveDnXjypnV0WAV3em0TN1EvQdy/7KIL99czd52wZy/oBqhm58GQrfh9VPwcjz4NgLoOdIvw9BRERERESaEU7jUD9gW73HhcDkprZxztWa2QGgO7DbzCYD9wEDgcudc/9xPZOZXQtcCzBgwIBIj0HqKe99IuW9T/yP9WNzKvnq4P0M61rjQyqR1lNeVdPsrGHNNeAcKNnRGpE6tJ5pQS4dUnnk8e7qJLalHMOTa5K4fY1xZuaFXNUjj4mJhSS9eSu8eQsMnQmXP+VjahERERERaU44jUON9TNp2AOoyW2cc+8Bx5rZKOABM1vgnKv8zIbO3U1obKKJEyeqd1GUrLYKl5D0H2MNAYzLqWJcTux7k4iEoyW9jqDj9MA57Gi9nZqT1SWNyRPGxTBNywzLrOOxGfspqgjwQmEKz23rzSWFF/LG92cwOOUQZSufIi05ybuGOVgH//wCDJoGx37BuyxNRERERER8F07jUCFQ/7qL/kDDr9sPb1MYGlMoC9hbfwPn3BozKwOOA5ZFnVialLnlJdL2rqVo4vePNBDVBuGBzVnM7ldKbmqdzwk/FcmMYYe11uVSbcnvS8PiVXO/t3j8XR2tt1Nzdm7Mj3Ga2OidFuTq4RVcPbyC7eUB+vXIADL4XsFkVm0/wFcqNzJ3VBJZNZXw+m+9W78JcPwX4fiLIaO734cgIiIiItJpHXVAamApMNzMBptZMjAXmN9gm/nAFaHli4DXnXMutE8igJkNBEYABTFJLp/lHGl711KT0fszPYdeK87g/s3ZrD+U7GM4EelM+qUHjyxfML4f/XPS+N2CtUy+M5+fdPs9W69cBmf9Fuqq4d8/hJ2hS//K90J1eROlioiIiIhIazlqz6HQGELXAy/hTWV/n3Mu38x+DSxzzs0H5gEPmtkGvB5Dc0O7TwN+ZGY1QBD4pnNud2scSGeXWF5MYtU+DuXNOLLOOXioIJPBGdVM6VHhXzgR6bTOPrY3Zx/bmzU7D/LAOwU8sbyQ7hlD+N5Z34aTvw3FH0OPY7yN374Dls6DUZ+DsZd5l59pBH0RERERkVYXn4iVBwAAIABJREFU1rTyzrkXgRcbrLup3nIlcHEj+z0IPNjCjBKGtL1rAajI+XRWoHf3pLGpLJmfHbubgP6/EvkPRcUl0e+7q4Ss3L4xTBOeaMcrWrthY9SXssXCqD6Z3HLhCXzvrBEkJ3i9G1/OL+Kh90r57pmljM3LhpGfg4p9kP8MfPgIdBsCJ34NpnzTt9wiIiIiIp1BWI1D0v6l7l1LdZd+BFMyj6x7bEtXeqbUckavMh+TibRf1bV1UTfwbN1eFOM04Yl2vKIVq9a0QprI5XZNObJcUVPHR4X7+fxf32bmyJ5898xjOG72nXDOrbBmPqz4x6eXnAEUrYZex6o3kYiIiIhIjKlxqIM4OOB0zH06zkedg+4pdUzuUUliIyNLtaTHRFpKMkMG5B19QxGRZswZ24+Zo3rxwDsF3L1oE+ffuZirpg7mps+NhjFzvVtdjbdx0Sq4axr0GQNTrvdmO0vQR5iIiIiISCzozLqDqMo55jOPEwxuOm5Pk9u3pMfEgZKGk9WJiESnS0oi3zptGJdPGcg9izYxuEcGADV1QSpr6uiamuRtmDMYzr8d3vs/eOpr3mxn027wxiZKTGnmJ4iIiIiIyNGEM1uZtHNpJStJKv20wcY52FKmdj8RiR+ZqUl876wRfGF8fwAefm8rp/1+IQ+/t5XauiCkdIGJV8F178LcRyCjB7x8E9RosH0RERERkZZS41C8C9aRs+EZMnYuObJq1YEULnu3H4t2pfkYTEQkeuMGZDO4RwY/eXoV5/15MYvWhy6FDQRg5LlwzWtw3WJIy4ZgEJ64CtY877WOi4iIiIhIRNS9JM4ll24jUFf1mcvKFuzMIDUQZGK3Sh+TiUjO2ocaXT+mcgU5a6uOuv++kZfFOlLcOKF/Nv/6+hT+vbqI3y1Yy1fue59rpg3mZ+eP9jYwg5xB3nJpsTcm0eonYeBUOP8OyD2mybJFREREROSz1HMozqXu24DDqMweCkBVnfF6UQan9iwnPVHfoItI/DIzZh3fh1duPIUfnjOSs47tDUBlTR11wXrvb5l9vMvNzr8divPhrqmw8BaoPXoDnIiIiIiIqHEo7qUc2EBNl364RO8SsrdK0iirCzCrr6avF5GOISUxgetmDGXS4G4A3PrvtVzwt7dZvf3ApxslJHpjEl2/FEbNho8eg3ozOIqIiIiISNN0WVkcs2AtSWVFlPaZcmTdq0UZ9EypZXxO611SVlRcEv2+u0qiniVNRARg4sBuPP/RTmb/ZTFfnTqYG888hoyU0MdZl55w0Tyo2A9JaVBdDh8/C2PmepeiiYiIiIjIf1DjUBxzgUR2TP4ZFqw5su7nx+1mW3kSgVb8H6i6ti7qBp6t24tinEZEOpvzTujDtOE9uO2ltdz39mYWrNrJHXPHHelZBHgDVQN88E9Y8ANY9yLM+QukZvkTWkRERESkHVPjULwLJOICn76MGYmOkZnVPgYSkfai/oDY4Q6CHS+y0pL47eeP54Jx/fnN8x+T2zWl8Q1PvAZqK+G1X8H/nQKXPQk9hrVtWBERERGRdk5jDsWx/tsXkF687MjjP63LYcGODB8TiYi0rQkDc3j6myczuEcGzjl+9Vw+Szbt+XSDQACmfgeufBGqDsF9Z8H2Ff4FFhERERFph9Q4FK8ObCerdAOBmgrvYXWApwq7sq08yedgIiJty0JjCe0rr+H1tbu45J4l/OHldZ+d0WzAZLj6FcgZBCmZ/gQVEREREWmn1DgUrzYtBKAqx7s84p3dadQ545Se5T6GEhHxT7eMZBb813QuGt+fO1/fwGX3LmHXwXqD83cfCte85l1W5hxsX+5fWBERERGRdkRjDsWrTW9Qm5BGTXovAN4qSSc3pZYRXTXekIi0H+8t/4ADpRVR7ZvVJY3JE8ZFtE96ciK3XTyGk4Z052fPrOaye9/jpRtOIXB4lP7DM5ateABe+B5c8igMPzOqfCIiIiIiHYUah+KRc7BpIaXpeWABKuuM9/akcl7fUs3ULCIxV1RcEvE+28veAWDtho2cdvbsqH7uzo35Ue0HcOGE/pzQP4vdpdUEAoZz7sjlZwAc+wVYOg8euxyumA95k6L+WSIiIiIi8U6NQ/Go6iDkTebQgRoM2Fcd4LjsKk7tGd238yIizamurSMrt29E+wSHHgvAilVrWiNSWIb36spwr3Mlf1u4kb1l1fzk3FEkBAxSM+HLT8K8s+Chi+Gql6DnSN+yioiIiIj4SWMOxaPULJj7EAczjwGgT1odfxq/iwndKo+yo4hI5+OcY09pNfMWb+brDy6jvLrWe6JLT7j8aUhMgYcuguoyf4OKiIiIiPhEPYfiUcV+SMsGoM7BwZoAOclBn0OJiMRGztqHAAiU7IBl6yIvYOJXP/PQzLjpc6MZ2D2dXz2Xz5X3L+XvXz2R9ORE6DbY60FUWgzJGbGILyIiIiISd9RzKN4E6+COE+C13wCwen8Kcxb1Z/neVJ+DiYi0b1ecPIjbvzSWZQV7uervSwkenuq+9/Ew7AxvuXyvfwFFRERERHyinkPxpugjqDoAPUfBrpUsKkknMQCjMqv8TiYi0u7NGdsPgKqa4KczmB22/mV447d06Xc+RDjGkkhbM7NzgD8BCcC9zrlbGjx/I3ANUAuUAFc557a0eVARERGJC2ocijcFi737gVNxq1byVkkaE3IqSE90/uYSkZg7fHlVfWMqV5CzVo3BLXG4gQggf8cBhvfsSnJiAAZPhy696LfzZUr6jKYuJcvHlCJNM7ME4K/AmUAhsNTM5jvnPq632QfAROdcuZldB/wP8KW2TysiIiLxQJeVxZuCxdB9GGT2YUd1KjsqkpjSQ7OUiYhEasf+Ci742zv87JlVOOcgKQ0mXIkF6+i27jFwGstN2q1JwAbn3CbnXDXwKDCn/gbOuTecc+Whh0uA/m2cUUREROKIGofiSbAOtrwDA6cCsPygNyj15O6apUxEJFJ9s9P4xilD+NeyQuYt3uyt7NKLol6nkHJwMxnFy/wNKNK0fsC2eo8LQ+uacjWwoLEnzOxaM1tmZstKSkpiGFFERETiiS4riyfBWph1q9dzCDgpay85WV3pl17rczARkfh0wxnH8MmuUv7fi2sYmtuF04D9maPoVlWIBWv8jifSFGtkXaPXl5vZl4GJwKmNPe+cuxu4G2DixIm6Rl1ERKSTUuNQPElMgbGXHnnYM7ma4bllPgYSEYlvgYDxhy+OYetd5Xz7kQ+YPyMBzNgz+gqwxv7/FmkXCoG8eo/7AzsabmRmZwA/BU51zmmwMhEREWmSGofiySevQLch0H0oa4sO8vreHszqZqQm6Is+EWlfDg+m3ZIBtIuKo7vEZXvZO2R1SWPyhHFhbZ+enMg9X5nIMyu3k5e+g/XgNQw5R+qefFxiOlXZQ6LKItJKlgLDzWwwsB2YC1xafwMzGwf8H3COc25X20cUERGReKLGoXgRrIMnr4bRc2D2nTy9YjvzCody9tBCv5OJiLSK6to6sqKYUj449Fh2bsyPaJ++2Wl8c8YwWPYWNcFQjyFXR1bBvzEcReNugISkiLOItAbnXK2ZXQ+8hDeV/X3OuXwz+zWwzDk3H7gN6AI8bl4vuK3Oudm+hRYREZF2TY1D8aJ4NVQegEHTAXhzfQmjMw5pCnsRkRhavS+Ra9aO4zcp+zg+G/YPu4Dc1ffSdfubHBpwht/xRI5wzr0IvNhg3U31llVhRUREJGyarSxeFCz27gdOpfhgJWuLDjG+635/M4mIdDADu9QRwHFzfncq6oyq7KGUdz+OrtvfIlBT6nc8EREREZFWoZ5D8aLgbcgZDFn9WLTMm73WaxzK9jeXiEg7k7P2IQIlO2DZuoj37ZoENw7YyI83juZvn+TwvZF7OTjwTNL25NN120IODDm/FRKLiIiIiPhLjUPxIBiEre/ASO+fkrVFh8jtmsKQ1HLUOCQi8p+iHcwaILe8hC8OyOOxrZmc3buU47J7cihvBjVpPWOYUERERESk/VDjUDwIBOD65VBbAcDPzx/Nd2YOp/CZhf7mEhFpp6IdzBpg6/Yirh6xn9eK03m9OIPjsqs5OPCsGCcUEREREWk/1DgULzK6f+ZhVloSmqdMRKR1pCc67p1URPfkuk9X1tXQpWgJld1GUpuW6184EREREZEY04DU8eDN22DFgwDct3gz33poBXVBzVImItKaeqTUYQbFlQlU1BmBuioyt7xK5pZX/Y4mIiIiIhJTYTUOmdk5ZrbOzDaY2Y8aeT7FzB4LPf+emQ0KrT/TzJab2arQ/emxjd8JBIPw7l9g2xIAXsovomBPGQkB8zmYiEjHt7sqgcve6cvDBZkEk7tQ2m8q6bs/IrGsyO9oIiIiIiIxc9TGITNLAP4KzAJGA5eY2egGm10N7HPODQNuB24Nrd8NfM45dzxwBfBgrIJ3GrvyoXI/DJpORXUdH2zdz9RhPfxOJSLSKfRIqWNqbgUPbcmkqDKBQ/2mEwwk03X7Ir+jiYiIiIjETDg9hyYBG5xzm5xz1cCjwJwG28wBHggtPwHMNDNzzn3gnNsRWp8PpJpZSiyCdxoFi737gVNZtmUv1XVBTh7avfl9REQkZq4bvg+AezZk4xLTKOs9kfSSD0moOuBzMhERERGR2AincagfsK3e48LQuka3cc7VAgeAhi0YFwIfOOeqGv4AM7vWzJaZ2bKSkuinH+6QChZD9kDIzuPtDXtISjAmDe7mdyoRkU6jd2odX+h/iFeKMigsT6S07zSqMgdhtZV+RxMRERERiYlwGocaG9ym4WjIzW5jZsfiXWr29cZ+gHPubufcROfcxNxczQDzGS4IQ72hmvpkpXLh+P6kJ2uSORGRtnTJwIOkJDhW7EulLjWH3cd/jdqMXn7HEhERERGJiXBaGQqBvHqP+wM7mtim0MwSgSxgL4CZ9QeeBr7inNvY4sSdzSWPgPPa2a44eZC/WUREOqnuKUGenLadzKTgkXWB6oMkVuyhOmuwj8lERERERFounJ5DS4HhZjbYzJKBucD8BtvMxxtwGuAi4HXnnDOzbOAF4MfOubdjFbrTCDUKYcb+8mqqa4PNby8iIq3mcMPQnirvo7Pb+ifotu5RCNb6GUtEREREpMWO2jgUGkPoeuAlYA3wL+dcvpn92sxmhzabB3Q3sw3AjcDh6e6vB4YBPzezlaFbz5gfRUf15NXw+FcBuP2V9Zx8y2vUBRte0SciIm3l6cIuXPx2P0oqEyjtO5XE6oOkl3zkdywRERERkRYJa/Aa59yLwIsN1t1Ub7kSuLiR/X4L/LaFGTunYBA2vgEjZgHw9sY9HNs3i4RAY8M7iYhIW5jcvZI7nPHIlky+c8wx1KT3osuOxZT3HAem92cRERERiU/hXFYmfihZCxV7YeBUig9WsmFXKVOHaQp7ERE/9U2r5azeZTy7vQv7ahIo7XsyyWU7ST601e9oIiIiIiJRU+NQe1Ww2LsfNI13Nu4G4OShPXwMJCIiAJcPOkBVMMCzhV0pzx1DMDGN5EOFfscSEREREYmaGofaq4K3IGsA5Azk7Q17yE5PYnSfTL9TiYh0egMyapncvYIFOzMIBlLYeeIPKe031e9YIiIiIiJRC2vMIfHB0NNgwEkAXH7SQE49JpeAxhsSEWkXvjtiL10Tg5iBS0gBwOqqIcnnYCIiIiIiUVDjUHs18aoji2PyshmTl+1jGBERqa9/+menr8/avIDUvR+TP/kBnxKJiIiIiERPl5W1R3s3Q9keAJZv2ccb63YR1BT2IiLtypayRL72fm/WHEimOqM3SRW76bbvQ79jiYiIiIhETI1D7dGrv4C7ZwAwb/EmfvLUKs2QLCLSzvRIqaOgLIkntnWlosdx1CWmk7fjBb9jiYiIiIhETI1D7Y1zUPA2DDyZYNDx7sY9TBnaHVPrkIhIu5KR6DivbymvFWewtyaF8l4Tyd39Lhzc6Xc0EREREZGIqHGovSlZB+W7YdA01hQdZF95DVM1hb2ISLv0hf6HqHXG/O1dKe09iYALwgf/9DuWiIiIiEhE1DjU3hS85d0PmsY7G7xxh6YOU+OQiEh7NCCjlkndK3hmexeqUrrz4egfwYQr/Y4lIiIiIhIRzVbW3hQshsx+kDOID7atYEhuBr2zUv1OJSIiTfjywANsK0/CAcW9ToUuuX5HEhERERGJiBqH2puZN8GBbWDGXy4Zz65DVX4nEhGRZozvVsX4bvXeq1c9AaXFMOVb/oUSEREREYmALitrb7oPhSEzAAgETL2GRETiQFmt8eS2LuyvDMLG1+GN30F1ud+xRERERETCosah9mTzIvjgIQjW8dB7W7jp2dUEg87vVCIichR7qxO4fV133t1eBWMvhepDsPZ5v2OJiIiIiIRFjUPtybL74Y2bwQI8u3IHK7buIxDQFPYiIu1dXnot43IqeWtbNcG8KZA9EFY+5HcsEREREZGwqHGovXDOG4x60DTKa+r4YOs+TWEvIhJHPte3lN0VQZZs3uf1Htr0Juzf5ncsEREREZGjUuNQe7H7EyjbBQOnsrRgHzV1jpM1hb2ISNw4tWcZ6UnGo0u3wZi50OcEKN3ldywRERERkaPSbGXtRcEi737QNN55bzdJCcaJg3L8zSQiImFLSYCT+iazt6yaYNZAAl9f5HckEREREZGwqHGovShZB1l50G0IGSkbOPvY3qQn6+UREYknF49MY9Zpkz9dUXUIqsuga2//QomIiIiIHIVaH9qLc2+D038OZnxn5nC/04iISBQSQpMIHCivISsF+PM4GDELZt/pbzARERERkWZozKH2JDWT8upanNP09SIi8er1tcVMvPkVPi6ugGFnwuqnobrc71giIiIiIk1S41B7sOQueOxyqKvlpmfzmfWnt9RAJCISp8bleePFPbmi0Ju1rPoQrH3e51QiIiIiIk1T41B7sOY52LcZF0hg8Se7GdqzC2bmdyoREYlCTkYyZ4zqxTMfbKcmbwpkD4CVD/sdS0RERESkSWoc8lt1GWx7D4acxoZdpRQdrGS6prAXEYlrF03oz56yahau3wNjLoVNC+FQkd+xREREREQapcYhv215B4I1MPQ0Fn2yG4Bpw9U4JCISz045JpceXVJ4Yvk2mHgVXPe2ZiwTERERkXZLs5X5beMbkJACA6aweNEqhuRm0D8n3e9UIiLSAkkJAf7wxTEM6p4OXTOgay+/I4mIiIiINEmNQ37LGQTjL4ekNC6fMpCyqjq/E4mISAycekzupw8O7oBXfgGTroW8E/0LJSIiIiLSCDUO+W3ytUcWTx+pb5ZFRDqSdzfu4dU1xfz8zDxvxrKkVDUOiYiIiEi7ozGH/FS2G2qrAFiyaQ+rtx/wOZCIiMTSuqKDzFu8mTV7HYyeA/nPQHW537GkAzCzc8xsnZltMLMfNfL8KWa2wsxqzewiPzKKiIhI/FDjkJ9e/hncOQGc4+YX1vCr5/L9TiQiIjE0e2w/khKMJ5cXwthLoeogrH3B71gS58wsAfgrMAsYDVxiZqMbbLYVuBJ4uG3TiYiISDxS45BfnPOmNu5/InvLa1i94wDTh+cedTcREYkf3TKSOX1kT55ZuZ2avJMhawCsfMjvWBL/JgEbnHObnHPVwKPAnPobOOcKnHMfAUE/AoqIiEh8UeOQX4rz4dBOGHoab67fhXMwXVPYi4h0OBdNyGN3aTVvrt8DU74Jfcd6XxCIRK8fsK3e48LQuoiZ2bVmtszMlpWUlMQknIiIiMQfDUjtl/ULvPvhZ/Pac9vp0SWFMf2z/c0kIiIxN2NELuMGZFNVG4STrvM7jnQM1si6qFocnXN3A3cDTJw4Ua2WIiIinZQah/yy7t/QdzzBjJ4s2bSK00fmEgg0dq4nIiLxLCkhwNPfnPrpimAdbHkbBk0H0/u+RKUQyKv3uD+ww6csIiIi0gGoccgvs26FmnICAWPRf8+gtKrW70QiItKKqmuDFB2oZMC2Z+GZb8DVr2pae4nWUmC4mQ0GtgNzgUv9jSQiIiLxLKwxh8KYLjXFzB4LPf+emQ0Kre9uZm+YWamZ/SW20eNc/4kw+BQA0pMT6dk11edAIiLSmq68/32+9fAKGHU+JKVrYGqJmnOuFrgeeAlYA/zLOZdvZr82s9kAZnaimRUCFwP/Z2aaElVERESadNTGoTCnS70a2OecGwbcDtwaWl8J/Bz4fswSdwTL7oeCtwH45kPLeXbldp8DiYhIaztzdC9WbT/A2n0ORs2G1U9BTYXfsSROOededM4d45wb6py7ObTuJufc/NDyUudcf+dchnOuu3PuWH8Ti4iISHsWTs+ho06XGnr8QGj5CWCmmZlzrsw5txivkUgAairhpZ/A6ifZWFLKi6uKOFBR43cqERFpZXPG9iMpwXh8WSGMvQSqDsDaF/yOJSIiIiISVuNQONOlHtkm1NX5ANA9FgE7nIK3oKYcRszi9TW7ADh9ZE+fQ4mISGvrlpHMGaN68fQH26nKmwrZA9U4JCIiIiLtQjiNQ+FMl9qiKVXN7FozW2Zmy0pKSsLdLT6tWwBJGTBoOq+uKWZk7670z0n3O5WIiLSBL52Yx96yahZ9she+8ixceK/fkUREREREwmocCme61CPbmFkikAXsDTeEc+5u59xE59zE3NzccHeLP87B+pdg6GkcqElg2ZZ9zBylXkMiIp3F9OG5PP6NKZwxqid0GwyBBO+zQURERETER+E0Dh2ZLtXMkvGmS53fYJv5wBWh5YuA153T2e5/OLQT6qpgxCz2lVdz6jG5nDm6t9+pRESkjSQEjBMHdcMs1OH2o3/BXdOhttrfYCIiIiLSqSUebQPnXK2ZHZ4uNQG47/B0qcCy0KwY84AHzWwDXo+huYf3N7MCIBNINrPPA2c55z6O/aHEgcy+8L31EKxlUGIy9115ot+JRESkjTnn+NVzH9M7K5Vv9MmC4lWwfgGMbjjXg4iIiIhI2zhq4xB406UCLzZYd1O95Urg4ib2HdSCfB1LMAiBAJV1Cew/UEnvrFS/E4mISBszM7bsKWPB6p1c84PTSczsB8v/rsYhEREREfFNOJeVSSzsWgN3HAdbl7Bw3S6m3PIaK7ft9zuViIj44EsnDqD4YBVvbtgL478CG9+AfQV+xxIRERGRTkqNQ21l1ePemEPdhjD/wx10z0jmuL6ZfqcSEREfzBzVkx5dUnjk/W0w7stgBise9DuWiIiIiHRSahxqC855jUNDZnAoMYfX1uzivOP7kJigX7+ISGeUlBDgogn9eWPdLoroATN/AcPP9DuWiIiIiHRSYY05JC1UuBT2b4UZP+HVNcVU1Qb53Ji+fqcSEREfXTZ5APvKqgk6B9Nu8DuOiIiIiHRi6rrSFlY9DompMPI8nvtwJ/2y0xg/IMfvVCIi4qO8buncetEJ9M1O81bs2Qhv/dHrbSoiIiIi0obUc6gtjDwfug+D1Exu+cLxbN1bTiBgfqcSEZF2YOW2/VTW1HHS7tfhtV/BwKkwYLLfsURERESkE1HPobYw5FSY/HUAemamMnFQN58DiYhIe+Cc44dPfMSvn/sYN+YSSM2GJX/1O5aIiIiIdDJqHGpt+U/DrrUA/G7BGl7OL/I5kIiItBdmxpVTB/HxzoO8v70KJlwJa57zxqkTEREREWkjahxqTdXl8Oz1sOSvFB+s5O5Fm/h450G/U4mISDvy+bH9yE5P4u/vFMCkrwEG79/tdywRERER6UTUONSa1jwH1aVw/Bd5asV2nIPZmqVMRETqSUtOYO6JA3gpv4jCYDcYc4kGpRYRERGRNqXGodbiHLx3F3QfTt2Ak/nnki1MGdKdIbld/E4mIiLtzOVTBpKdnsz64kMw5y9w9s1+RxIRERGRTkSNQ62lcCnsWAGTv87r63azfX8FV5w80O9UIiLSDvXLTuO9n8zk9JG9wEKzWW5dAjWV/gYTERERkU5BjUOtZdfH0KUXjLmEgMH04T04Y1Qvv1OJiEg7lZQQIBh0bNh1CHashPvOhuV/9zuWiIiIiHQCahxqLROuhBtWQUoXZo7qxYNXTyYxQb9uERFp2i3/Xsvn//oO+7JGw6DpsPiP3uQGIiIiIiKtSK0VreFQsXefmMLSgr2UVdX6m0dEROLCheP7U1pVy7zFm+G0n0BpMSyb53csEREREeng1DgUazUV8LeT4JVfUFpVy1fvX8qvn/vY71QiIhIHRvTuynnH9+Hv7xSwP3ciDDkNFt8OVaV+RxMRERGRDkyNQ7G26nGo2AvDzuDpFYWUVtVyyeQBfqcSEZE48Z2Zwz/tPXT6z8AFvXHsRERERERaiRqHYikYhCV3Qa/jqBswlb+/U8AJ/bMYm5ftdzIREYkTh3sPvbpmF8G+E+DGNZA3ye9YIiIiItKBJfodoENZ9Tjsyocv3MMzK3ewsaSMOy8Z53cqERGJM7/5/HF0TU0kEDAIpEGwDra9BwNP9juaiIiIiHRA6jkUK87B+3dDn7Fw3EW8u2kPx/fL4rzj+/idTERE4ky3jGSSEgKUVdWybW85vHMn3H8ubF/hdzQRERER6YDUcyhWzOArz0LZLggEuO2iEzhYWet96ysiIhIh5xyX3rMEM+Opr15JYMn/wvM3wDWvQ4I+vkVEREQkdtRzKBaqDkFdDaR04WB6Hjv2V2BmZKUl+Z1MRETilJlxxcmDWLltP4+tPgjn/A52fghL7/E7moiIiIh0MGocioVXfwl3TYPaau5auJHT/7CQkkNVfqcSEZE4d8G4fkwa3I1b/72WvYPOg2FnwOu/hQPb/Y4mIiIiIh2IGodaqmQ9LLsfBk2nqCzIfW9v5pxje5PbNcXvZCIiEufMjN/MOY5DlbXc8u+1cO7vIWcwlJX4HU1EREREOhANWtASdbXw3HcgOQN36g/55dP51AUd3ztrhN/JRESkgxjRuyvXnjKEjwr3U515PMnfeMsb505EREREJEbUONQSb9wMW9+FL9wIk2FAAAAT00lEQVTLI/kV/Du/iJ+cO5K8bul+JxMRkQ7kxjOPIWBGwuFJDmqr4aWfwKjzYcgMP6OJiIiISAegy8qiVVcDW96G8VfACRezY38FpxyTyzXThvidTEREOpikhAAJAWPXoUr++4kPKa0oh4K34PGvwv6tfscTERERkTinnkPRSkiCK1+AYB0A3z97BLV1QU1dLyIirWZTSRlPLC+krLqOv3zpn9g9p8NjX4arXoKkNL/jiYiIiEicUs+hSNXVwGu/hrI9kJDEnYu2sXzLXgASE/TrFBGR1nPSkO784OyRvPDRTm5fEcRdcJc3vf2/vgI1FX7HExEREZE4pdaMSNRUeifgb/0BNi/kbws38IdX1rNgVZHfyUREpJP4xqlDuHhCf/78+gZu3TwUd97tsOVd2P2J39FEREREJE7psrJwVZXCo5fC5jdxs27jT0XHc8er65gzti8/mjXS73QiItJJmBm3XngCKUkBXs4v4pvXX07m6NmQ0cPboLYKElP8DSkiIiIicUWNQ+Go2A8PfxEKl+Lm/I3biifwt4WfcNGE/tx64Qmfzh4jIiLSBgIB4zdzjuNgRS2ZqUmUVmWTWFNH6ocPwPv3wMV/h9wRfscUERERkTihy8rCUV0KZSVw0f0Ex1xKwZ4yLp08gP9Rw5CIiPjEzMhKTwLgF8/mM/svi9kc7AWHdsJd0+GdO49MmiAiIiIi0hw1DjWlqtQbW6iuBrL6s/CM51jXfSYJAeNPc8dx8+eP08xkIiLSLpw/pg/7y2s48xnjnuMfITj0dHj5Z3D/LNiz0e94IiIiItLOqXGooepyWPEg/O0keO03FK18mRsfW8mV//iQ/124AYCkhABmahgSEZH24bQRPXn5u6dw/gl9uHnRXmZsu5bNp/wRStbCgW3eRnU1/oYUERERkXYrrMYhMzvHzNaZ2QYz+1Ejz6eY2WOh598zs0H1nvtxaP06Mzs7dtFjrLYaFvwQ/jAS5l9PmWXw655/5KTHYf6HO/j26cO49aIT/E4pIiLSqOz0ZO6YO455V0ykd3YaaRMug29/wCfp48nfcQD38s9g3tmw9F7Yt8XvuNJCLTk3ExEREWnoqANSm1kC8FfgTKAQWGpm851zH9fb7Gpgn3NumJnNBW4FvmRmo4G5wLFAX+BVMzvGOeffIAjBIJQWwYFC2PkhrmI/ByfdwMaSMvqueZecITNJmXwN/yzoxYJ3t3DjmQOYe2IePTNTfYssIiISrpmjejFzVK/Qo1R+8fAS3tm4h69lGNcmbCP3he95T/UYAeO+DFO/4z2uKoWULr5klsi05Nys7dOKiIhIPAhntrJJwAbn3CYAM3sUmAPUPwGZA/wytPwE8BfzrruaAzzqnKsCNpvZhlB578YmfoSevxFW/AOCn3at3+z6cfqCUYAR4AfMO3cypw3qyRX96rh6+hASE3TlnYiIxK875o7lzXUlLFzfh5nrZ9Cjahtf6bGeKzM/gd3rueaBpRwsr2FeyZfICNQQSMuG1CwYfwVM+abf8aVxUZ+bOedcWwYVERGR+BBO41A/YFu9x4XA5Ka2cc7VmtkBoHto/ZIG+/aLOm1LDTgJUjMhK4+d9ODPHwVI6TGYn+akk9ctnfEDs+nZ1eshlJqU4FtMERGRWOnZNZWLJ+Zx8cQ8auuCbN1bTmVNEPpmApD9+IeUV1bxWMpFfGlUMl1dGVTu9xqIpL1qybnZ7jZJKCIiInHFjvYFkpldDJztnLsm9PhyYJJz7tv1tskPbVMYerwR71utXwPvOuf+GVo/D3jROfdkg59xLXBt6OEIYF0Mji3e9KBzn7Dp+HX8Ov7OS8ffOY9/oHMu1+8Q8agl52bOuT0Nyor0HCwW9TVWdV5ZlEVZlEVZlMXvMuItS5PnX+H0HCoE8uo97g/saGKbQjNLBLKAvWHui3PubuDuMLJ0WGa2zDk30e8cftHx6/h1/Dp+v3P4pbMfv0SlJedmnxHpOVgs6mus6ryyKIuyKIuyKIvfZXSkLOEMqLMUGG5mg80sGW+A6fkNtpkPXBFavgh4PXRN+3xgbmjGjMHAcOD9aIKKiIiICNCyczMRERGR/3DUnkOh69SvB14CEoD7nHP5ZvZrYJlzbj4wD3gwNOD0XryTFELb/QtvgMRa4Fu+zlQmIiIiEudacm4mIiIi0phwLivDOfci8GKDdTfVW64ELm5i35uBm1uQsbPo1JfVoePX8XduOv7OrbMfv0ShJedmLRSL+hqrOq8srVeOsrReGbEqR1lar4xYlaMsrVdGrMpRlnqOOiC1iIiIiIiIiIh0XOGMOSQiIiIiIiIiIh2UGofaATM7x8zWmdkGM/uR33nampkVmNkqM1tpZsv8ztPazOw+M9tlZqvrretmZq+Y2Seh+xw/M7amJo7/l2a2PVQHVprZuX5mbC1mlmdmb5jZGjPLN7P/Cq3vFK9/M8ffWV7/VDN738w+DB3/r0LrB5vZe6HX/7HQAMMi7Upj791RlNHoe0CEZTT6d9SCTAlm9oGZPR/l/jE5hzGzbDN7wszWhn4/UyLcf0S999CVZnbQzG6IMst3Q7/b1Wb2iJmlRlHGf4X2z48kRyzOkZoo4+JQlqCZhTWLTxPl3BZ6jT4ys6fNLDvKcn4TKmOlmb1sZn0jLaPec983M2dmPaLIEfHnb1NZzOzb5v0/k29m/xNFlsfq5Sgws5XRZDGzsWa25PDfpJlNiqKMMWb2buhv+zkzywwjS4vP8ZopI6L620w5YdffZsqItO42+74fTv1tJkvY9be5HBHW3aayRFR/mykn7PrbTBkR198jnHO6+XjDG0hyIzAESAY+BEb7nauNfwcFQA+/c7Th8Z4CjAdW11v3P8CPQss/Am71O2cbH/8vge/7na0Njr0PMD603BVYD4zuLK9/M8ffWV5/A7qElpOA94CTgH8Bc0Pr7wKu8zurbro1vDX23h1FGY2+B0RYRqN/Ry3IdCPwMPB8lPvH5BwGeAC4JrScDGS3oKwEoAgYGMW+/YDNQFro8b+AKyMs4zhgNZCON77pq8DwaOtZpJ+RTZQxChgBLAQmtiDLWUBiaPnWcD6vmygns97yd4C7Ii0jtD4Pb2D6LUerh03kiPjzt4lyTgu9zimhxz2jOZ56z/8BuCnKLC8Ds0LL5wILoyhjKXBqaPkq4DdhZGnxOV4zZURUf5spJ+z620wZkdbdJt/3w62/zWQJu/42U0akdfeon2Ph1N9m8oRdf5spI+L6e/imnkP+mwRscM5tcs5VA48Cc3zOJK3IObcIb+aY+ubgnRgSuv98m4ZqQ00cf6fgnNvpnFsRWj4ErME7Ee8Ur38zx98pOE9p6GFS6OaA04EnQus77Osv8S0W792xeA9o5u8oYmbWHzgPuDea/WMl9K3uKXgzzOGcq3bO7W9BkTOBjc65LVHunwikmVkiXgPPjgj3HwUscc6VO+dqgTeBC8LZMRbnSI2V4Zxb45xbF06Go5TzcuiYAJYA/aMs52C9hxkcpQ438/d3O/DfR9v/KGVEpIlyrgNucc5VhbbZFW0WMzPgi8AjUWZxwOGeElkcpf42UcYIYFFo+RXgwjCytPgcr6kyIq2/zZQTdv1tpoxI625z7/th1d8YfXY0VUakdbfZLOHW32bKCbv+NlNGxPX3MDUO+a8fsK3e40I60T9LIQ542cyWm9m1fofxSS/n3E7w/tCBnj7n8cP1oW6q9zXX5bajMLNBwDi8b7073evf4Pihk7z+5l3CshLYhfeBvRHYX+9krTN+Bkgn1Mh7QCT7fubvyDkXcRkhd+D9YxKMcn+IzTnMEKAEuN+8S9zuNbOMFmSaSxj/WDfGObcd+D2wFdgJHHDOvRxhMauBU8ysu5ml4337nRdNnpD2+hl5FbAg2p3N7GYz2wZcBtx0tO0b2X82sN0592G0GUJi8fl7DDDdvEuk3zSzE1uQZzpQ7Jz7JMr9bwBuC/1ufw/8OIoyVgOzQ8sXE2H9jcU5XkveI8MsJ+z627CMaOtu/XKirb+xOH9sUEbUdbeJ323E9bdBOVHV3wZlRF1/1TjkP2tkXWebQm6qc248MAv4lpmd4ncgaXP/CwwFxuKdjP7B3zity8y6AE8CNzT4BqZTaOT4O83r75yrc86Nxfu2bhLeN+z/sVnbphJpWy19D2z4d2Rmx0WR4Xxgl3NueaT7NhCLc5hEvMta/tc5Nw4ow7v8JGLmjVk2G3g8yv1z8Ho6DAb6Ahlm9uVIynDOrcG7ZOUV4N94QybUNrtTnDGzn+Id00PRluGc+6lzLi9UxvUR/vx04KdE0ajUQKw+fxOBHLxLpX8A/CvUgyIalxBl42bIdcB3Q7/b7xLqkRehq/D+npfjXa5THe6OsTjHi9V5YlPlRFJ/Gysjmrpbv5zQz464/sbi/LGRMqKqu828RhHV30bKibj+NlJG1PVXjUP+K+SzrXn9ibz7blxzzu0I3e8Cnsb7h6mzKTazPgCh+2a7NHY0zrni0Ml+ELiHDlwHzCwJ7w38IefcU6HVneb1b+z4O9Prf1jokpGFeCcj2aHLN6ATfgZI59LEe2BU6v0dnRPF7lOB2WZWgHdJ/+lm9s8oMsTiHKYQKKzXA+oJvMaiaMwCVjjniqPc/wxgs3OuxDlXAzwFnBxpIc65ec658c65U/Au2Ym2Fwi0s89IM7sCOB+4zDkXi8b8h4ngso+QoXgNeB+G6nB/YIWZ9Y6kkBh+/hYCTznP+3i98ZodILsxoc/CLwCPRZkD4Aq8egteI2nEx+ScW+ucO8s5NwHvH/2N4ewXi3O8WL1HNlVOJPU3jCxh1d1Gyom4/sbi/LGJ44m47jbzu42o/jZRTkT1t4nfS1T1F9Q41B4sBYabN1tNMl5X4Pk+Z2ozZpZhZl0PL+MNlBb1TChxbD7emwGh+2d9zNLmDn9ohlxAB60DoW8i5gFrnHN/rPdUp3j9mzr+TvT651poZhAzS8P7J2wN8AZwUWizDvv6izTzHhhJGY39Ha2NtBzn3I+dc/2dc4Pwzr1ed85F1EMmVucwzrkiYJuZjQitmgl8HGk5IS3tdbEVOMnM0kOv10y896mImFnP0P0AvH+WWpKp3XxGmtk5wA+B2c658haUM7zew9lEWIedc6uccz2dc4NCdbgQb2DaoghzxOrz9xm88fMws2PwBlXfHUU5ZwBrnXOFUeYA7wuWU0PLpxNFw2S9+hsAfoY3WcTR9mnxOV4s3iObKyeS+ttMGRHV3cbKibT+xuL8sZnfbUR19yivUdj1t5lywq6/zfxeIq6/R7gwR67WrfVueNdir8dr1fup33na+NiH4HU3/hDI7wzHj3eCtBOowXszvBroDrwWegN4Dejmd842Pv4HgVXAR3gfon38ztlKxz4N75Khj4CVodu5neX1b+b4O8vrfwLwQeg4VxOaySL0Pvg+sAHvW6IUv7PqplvDW2Pv3VGU0eh7QIRlNPp31MJjm0EUs5XF8hwG77KIZaHjegbIiaKMdGAPkNXC38ev8P7hWx16f474PQl4C6+B60NgZkvqWaSfkU2UcUFouQooBl6KMssGvLFCD9ffZmdqaqacJ0O/34+A5/AG+o2ojAbPF3D02cpicv7VRDnJwD9Dx7QCOD2a4wH+DnyjhfVlGrA8VPfeAyZEUcZ/4f1vth64BbAwsrT4HK+ZMiKqv82UE3b9baaMSOvuUd/3j1Z/m8kSdv1tpoxI626TxxNJ/W0mT9j1t5kyIq6/h28WKlhERERERERERDohXVYmIiIiIiIiItKJqXFIRERERERERKQTU+OQiIiIiIiIiEgnpsYhEREREREREZFOTI1DIiIiIiIiIiKdmBqHRKTNmdlEM/tztPuY2QwzO7l10omIiIh0TDoHE5GmaCp7EWn3zCzROVdb7/EvgVLn3O/9SyUiIiLSsekcTKTzUM8hEYmImWWY2Qtm9qGZrTazL5nZBDN708yWm9lLZtYntO1CM7vVzN43s/VmNj20foaZPR9a7mZmz5jZR2a2xMxOCK3/pZndbWYvA/84vI+ZDQK+AXzXzFaa2XQz22xmSaH9Ms2s4PBjERERkY5A52Ai0poS/Q4gInHnHGCHc+48ADPLAhYAc5xzJWb2JeBm4KrQ9onOuUlmdi7wC+CMBuX9CvjAOfd5Mzsd+AcwNvTcBGCac67CzGYAOOcKzOwu6n1rZWYLgfOAZ4C5wJPOuZpWOHYRERERv+gcTERajRqHRCRSq4Dfm9mtwPPAPuA44BUzA0gAdtbb/qnQ/XJgUCPlTQMuBHDOvW5m3UMnOwDznXMVYWS6F/hvvBOTrwJfi+SAREREROKAzsFEpNWocUhEIuKcW29mE4Bzgd8BrwD5zrkpTexSFbqvo/H3HGvsx4Tuy8LM9LaZDTKzU4EE59zqcPYTERERiRc6BxOR1qQxh0QkImbWFyh3zv0T+D0wGcg1symh55PM7NgIilwEXBbadwaw2zl38Cj7HAK6Nlj3D+AR4P4IfraIiIhIXNA5mIi0JvUcEpFIHQ/cZmZBoAa4DqgF/hzqipwI3AHkh1neL4H7zewjoBy4Iox9ngOeMLM5wLedc28BDwG/xTs5EREREelodA4mIq1GU9mLSIdgZhfhDch4ud9ZRERERDoLnYOJdAzqOSQicc/M7gRm4V2DLyIiIiJtQOdgIh2Heg6JiIiIiIiIiHRiGpBaRERERERERKQTU+OQiIiIiIiIiEgnpsYhEREREREREZFOTI1DIiIiIiIiIiKdmBqHREREREREREQ6MTUOiYiIiIiIiIh0Yv8fzVnLcSXawHAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_seniority = data.groupby('seniority')['if_quit'].mean()\n",
    "hist_kws = {'histtype': 'bar','edgecolor': 'black', 'alpha': 0.2}\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "sns.distplot(data[data['if_quit'] == 1]['seniority'], norm_hist=True, \n",
    "             ax = ax[0], label = 'quit', hist_kws=hist_kws,\n",
    "             kde=True, kde_kws={'linestyle': '--'})\n",
    "sns.distplot(data[data['if_quit'] == 0]['seniority'], norm_hist=True, \n",
    "             ax = ax[0], label = 'not quit', \n",
    "             kde=True, kde_kws={'linestyle': '--'})\n",
    "ax[0].set_title('seniority_count', fontsize = 15)\n",
    "# ax[0].set_yscale('log')    \n",
    "# total count of company 11 and 12 are smaller than other company, if not log y, they won't appear on the graph\n",
    "sns.barplot(x = by_seniority.index, y = by_seniority.values, palette='Set2', ax = ax[1])\n",
    "ax[1].set_title('Average quit rate by seniority', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "seniority doesn't seem to be related to whether an employee will quit or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_salary = data.groupby('salary')['if_quit'].mean()\n",
    "hist_kws = {'histtype': 'bar','edgecolor': 'black', 'alpha': 0.2}\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "sns.distplot(data[data['if_quit'] == 1]['salary'], norm_hist=True, \n",
    "             ax = ax[0], label = 'quit', hist_kws=hist_kws,\n",
    "             kde=True, kde_kws={'linestyle': '--'})\n",
    "sns.distplot(data[data['if_quit'] == 0]['salary'], norm_hist=True, \n",
    "             ax = ax[0], label = 'not quit', \n",
    "             kde=True, kde_kws={'linestyle': '--'})\n",
    "ax[0].legend(loc = 'best', fontsize = 12)\n",
    "ax[0].set_title('salary_count', fontsize = 15)\n",
    "# ax[0].set_yscale('log')    \n",
    "# total count of company 11 and 12 are smaller than other company, if not log y, they won't appear on the graph\n",
    "sns.lineplot(x = by_salary.index, y = by_salary.values, palette='Set2', ax = ax[1])\n",
    "ax[1].set_title('Average quit rate by salary', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More people tend to quit when their salary is around （7k-12k）\n",
    "The distribution of salary is skewed, when salary increases, the average quit rate fluctuates greatly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the characteristics of the people who quit early"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the histogram of employment_length, it looks like we could **define early quitters as those people who quit within 1 yr or so**.\\\n",
    "Let’s create two classes of users : quit within 13 months or not (if they haven’t been in the current\n",
    "company for at least 13 months, we remove them)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>if_quit</th>\n",
       "      <th>work_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>825355.0</td>\n",
       "      <td>7</td>\n",
       "      <td>marketing</td>\n",
       "      <td>20</td>\n",
       "      <td>183000.0</td>\n",
       "      <td>2013-04-29</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>662910.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>20</td>\n",
       "      <td>115000.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "1     825355.0           7         marketing         20  183000.0 2013-04-29   \n",
       "2     927315.0           4         marketing         14  101000.0 2014-10-13   \n",
       "3     662910.0           7  customer_service         20  115000.0 2012-05-14   \n",
       "4     256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "\n",
       "   quit_date  if_quit  work_days  \n",
       "0 2015-10-30      1.0      585.0  \n",
       "1 2014-04-04      1.0      340.0  \n",
       "2        NaT      0.0        NaN  \n",
       "3 2013-06-07      1.0      389.0  \n",
       "4 2014-08-22      1.0     1040.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>company_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>seniority</th>\n",
       "      <th>salary</th>\n",
       "      <th>join_date</th>\n",
       "      <th>quit_date</th>\n",
       "      <th>if_quit</th>\n",
       "      <th>work_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13021.0</td>\n",
       "      <td>7</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>28</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2015-10-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>927315.0</td>\n",
       "      <td>4</td>\n",
       "      <td>marketing</td>\n",
       "      <td>14</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>256971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>data_science</td>\n",
       "      <td>23</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>2011-10-17</td>\n",
       "      <td>2014-08-22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>509529.0</td>\n",
       "      <td>4</td>\n",
       "      <td>data_science</td>\n",
       "      <td>14</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>578.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>88600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>customer_service</td>\n",
       "      <td>21</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>2013-10-21</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  company_id              dept  seniority    salary  join_date  \\\n",
       "0      13021.0           7  customer_service         28   89000.0 2014-03-24   \n",
       "2     927315.0           4         marketing         14  101000.0 2014-10-13   \n",
       "4     256971.0           2      data_science         23  276000.0 2011-10-17   \n",
       "5     509529.0           4      data_science         14  165000.0 2012-01-30   \n",
       "6      88600.0           4  customer_service         21  107000.0 2013-10-21   \n",
       "\n",
       "   quit_date  if_quit  work_days  \n",
       "0 2015-10-30      1.0      585.0  \n",
       "2        NaT      0.0        NaN  \n",
       "4 2014-08-22      1.0     1040.0  \n",
       "5 2013-08-30      1.0      578.0  \n",
       "6        NaT      0.0        NaN  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data[(data['work_days'] > 390) | (data['work_days'].isna())]   \n",
    "# remove those work less than 13 months\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_data.loc[:,['company_id','dept','seniority','salary']]\n",
    "y = new_data.loc[:, 'if_quit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose the best max_depth\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "for i in range(1, 12, 2):\n",
    "    dtc = DecisionTreeClassifier(max_depth=i,\n",
    "                                random_state=42)\n",
    "    dtc.fit(Xtrain, Ytrain)\n",
    "    train_acc.append(dtc.score(Xtrain, Ytrain))\n",
    "    test_acc.append(dtc.score(Xtest, Ytest))\n",
    "\n",
    "plt.plot(range(1, 12, 2), train_acc, c = 'blue')\n",
    "plt.plot(range(1, 12, 2), test_acc, c = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose the best min_sample_split\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "for i in range(2,50,5):\n",
    "    dtc = DecisionTreeClassifier(max_depth=3,\n",
    "                                 min_samples_split=i,\n",
    "                                random_state=42)\n",
    "    dtc.fit(Xtrain, Ytrain)\n",
    "    train_acc.append(dtc.score(Xtrain, Ytrain))\n",
    "    test_acc.append(dtc.score(Xtest, Ytest))\n",
    "\n",
    "plt.plot(range(2,50,5), train_acc, c = 'blue')\n",
    "plt.plot(range(2,50,5), test_acc, c = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose the best min_sample_leaf\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "for i in range(2,50,5):\n",
    "    dtc = DecisionTreeClassifier(max_depth=3,\n",
    "                                 min_samples_leaf=i,\n",
    "                                random_state=42)\n",
    "    dtc.fit(Xtrain, Ytrain)\n",
    "    train_acc.append(dtc.score(Xtrain, Ytrain))\n",
    "    test_acc.append(dtc.score(Xtest, Ytest))\n",
    "\n",
    "plt.plot(range(2,50,5), train_acc, c = 'blue')\n",
    "plt.plot(range(2,50,5), test_acc, c = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc_best = DecisionTreeClassifier(max_depth=3,\n",
    "                                random_state=42).fit(Xtrain, Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "company_id                :  0.0000\n",
      "seniority                 :  0.0642\n",
      "salary                    :  0.9358\n",
      "dept_customer_service     :  0.0000\n",
      "dept_data_science         :  0.0000\n",
      "dept_design               :  0.0000\n",
      "dept_engineer             :  0.0000\n",
      "dept_marketing            :  0.0000\n",
      "dept_sales                :  0.0000\n"
     ]
    }
   ],
   "source": [
    "feature_names = Xtrain.columns\n",
    "for i in range(len(feature_names)):\n",
    "    print('{0:<25s} : {1: .4f}'.format(feature_names[i], dtc_best.feature_importances_[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important feature is salary!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n",
       " -->\r\n",
       "<!-- Title: Tree Pages: 1 -->\r\n",
       "<svg width=\"1157pt\" height=\"433pt\"\r\n",
       " viewBox=\"0.00 0.00 1156.50 433.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 429)\">\r\n",
       "<title>Tree</title>\r\n",
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       "<!-- 0 -->\r\n",
       "<g id=\"node1\" class=\"node\"><title>0</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"616\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">salary &lt;= 246500.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"616\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.485</text>\r\n",
       "<text text-anchor=\"middle\" x=\"616\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13363</text>\r\n",
       "<text text-anchor=\"middle\" x=\"616\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [7828, 5535]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"616\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 1 -->\r\n",
       "<g id=\"node2\" class=\"node\"><title>1</title>\r\n",
       "<path fill=\"#f8deca\" stroke=\"black\" d=\"M521,-306C521,-306 399,-306 399,-306 393,-306 387,-300 387,-294 387,-294 387,-235 387,-235 387,-229 393,-223 399,-223 399,-223 521,-223 521,-223 527,-223 533,-229 533,-235 533,-235 533,-294 533,-294 533,-300 527,-306 521,-306\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">salary &lt;= 89500.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.488</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11773</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6789, 4984]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;1 -->\r\n",
       "<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M561.877,-341.907C549.05,-332.288 535.271,-321.953 522.12,-312.09\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"524.128,-309.221 514.028,-306.021 519.928,-314.821 524.128,-309.221\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"517.563\" y=\"-327.07\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\r\n",
       "</g>\r\n",
       "<!-- 8 -->\r\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
       "<path fill=\"#f3c4a2\" stroke=\"black\" d=\"M822.5,-306C822.5,-306 707.5,-306 707.5,-306 701.5,-306 695.5,-300 695.5,-294 695.5,-294 695.5,-235 695.5,-235 695.5,-229 701.5,-223 707.5,-223 707.5,-223 822.5,-223 822.5,-223 828.5,-223 834.5,-229 834.5,-235 834.5,-235 834.5,-294 834.5,-294 834.5,-300 828.5,-306 822.5,-306\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">salary &lt;= 365500.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.453</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1590</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1039, 551]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>0&#45;&gt;8</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M667.695,-341.907C679.83,-332.379 692.858,-322.148 705.311,-312.37\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"707.693,-314.949 713.397,-306.021 703.37,-309.444 707.693,-314.949\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"710.411\" y=\"-327.142\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\r\n",
       "</g>\r\n",
       "<!-- 2 -->\r\n",
       "<g id=\"node3\" class=\"node\"><title>2</title>\r\n",
       "<path fill=\"#f6d4bb\" stroke=\"black\" d=\"M282,-187C282,-187 160,-187 160,-187 154,-187 148,-181 148,-175 148,-175 148,-116 148,-116 148,-110 154,-104 160,-104 160,-104 282,-104 282,-104 288,-104 294,-110 294,-116 294,-116 294,-175 294,-175 294,-181 288,-187 282,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">salary &lt;= 36500.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.479</text>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4151</text>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2505, 1646]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;2 -->\r\n",
       "<g id=\"edge2\" class=\"edge\"><title>1&#45;&gt;2</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M386.993,-227.76C360.6,-214.84 330.621,-200.164 303.472,-186.874\"/>\r\n",
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       "</g>\r\n",
       "<!-- 5 -->\r\n",
       "<g id=\"node6\" class=\"node\"><title>5</title>\r\n",
       "<path fill=\"#f9e3d3\" stroke=\"black\" d=\"M521,-187C521,-187 399,-187 399,-187 393,-187 387,-181 387,-175 387,-175 387,-116 387,-116 387,-110 393,-104 399,-104 399,-104 521,-104 521,-104 527,-104 533,-110 533,-116 533,-116 533,-175 533,-175 533,-181 527,-187 521,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">salary &lt;= 192500.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.492</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7622</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4284, 3338]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"460\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\"><title>1&#45;&gt;5</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M460,-222.907C460,-214.649 460,-205.864 460,-197.302\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"463.5,-197.021 460,-187.021 456.5,-197.021 463.5,-197.021\"/>\r\n",
       "</g>\r\n",
       "<!-- 3 -->\r\n",
       "<g id=\"node4\" class=\"node\"><title>3</title>\r\n",
       "<path fill=\"#f3c7a7\" stroke=\"black\" d=\"M118,-68C118,-68 12,-68 12,-68 6,-68 0,-62 0,-56 0,-56 0,-12 0,-12 0,-6 6,-0 12,-0 12,-0 118,-0 118,-0 124,-0 130,-6 130,-12 130,-12 130,-56 130,-56 130,-62 124,-68 118,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"65\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.459</text>\r\n",
       "<text text-anchor=\"middle\" x=\"65\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 832</text>\r\n",
       "<text text-anchor=\"middle\" x=\"65\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [535, 297]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"65\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;3 -->\r\n",
       "<g id=\"edge3\" class=\"edge\"><title>2&#45;&gt;3</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M162.911,-103.726C149.138,-94.0582 134.476,-83.767 120.807,-74.172\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"122.636,-71.1801 112.441,-68.2996 118.615,-76.9096 122.636,-71.1801\"/>\r\n",
       "</g>\r\n",
       "<!-- 4 -->\r\n",
       "<g id=\"node5\" class=\"node\"><title>4</title>\r\n",
       "<path fill=\"#f7d7c1\" stroke=\"black\" d=\"M282,-68C282,-68 160,-68 160,-68 154,-68 148,-62 148,-56 148,-56 148,-12 148,-12 148,-6 154,-0 160,-0 160,-0 282,-0 282,-0 288,-0 294,-6 294,-12 294,-12 294,-56 294,-56 294,-62 288,-68 282,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.482</text>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3319</text>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1970, 1349]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"221\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;4 -->\r\n",
       "<g id=\"edge4\" class=\"edge\"><title>2&#45;&gt;4</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M221,-103.726C221,-95.5175 221,-86.8595 221,-78.56\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"224.5,-78.2996 221,-68.2996 217.5,-78.2996 224.5,-78.2996\"/>\r\n",
       "</g>\r\n",
       "<!-- 6 -->\r\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\r\n",
       "<path fill=\"#fae6d8\" stroke=\"black\" d=\"M446,-68C446,-68 324,-68 324,-68 318,-68 312,-62 312,-56 312,-56 312,-12 312,-12 312,-6 318,-0 324,-0 324,-0 446,-0 446,-0 452,-0 458,-6 458,-12 458,-12 458,-56 458,-56 458,-62 452,-68 446,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"385\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.494</text>\r\n",
       "<text text-anchor=\"middle\" x=\"385\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5918</text>\r\n",
       "<text text-anchor=\"middle\" x=\"385\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3280, 2638]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"385\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 5&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\"><title>5&#45;&gt;6</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M432.073,-103.726C426.013,-94.879 419.596,-85.51 413.514,-76.6303\"/>\r\n",
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       "<!-- 7 -->\r\n",
       "<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
       "<path fill=\"#f7d9c3\" stroke=\"black\" d=\"M602,-68C602,-68 488,-68 488,-68 482,-68 476,-62 476,-56 476,-56 476,-12 476,-12 476,-6 482,-0 488,-0 488,-0 602,-0 602,-0 608,-0 614,-6 614,-12 614,-12 614,-56 614,-56 614,-62 608,-68 602,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"545\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.484</text>\r\n",
       "<text text-anchor=\"middle\" x=\"545\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1704</text>\r\n",
       "<text text-anchor=\"middle\" x=\"545\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1004, 700]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"545\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 5&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\"><title>5&#45;&gt;7</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M491.651,-103.726C498.589,-94.7878 505.941,-85.3168 512.897,-76.3558\"/>\r\n",
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       "<!-- 9 -->\r\n",
       "<g id=\"node10\" class=\"node\"><title>9</title>\r\n",
       "<path fill=\"#f3c5a3\" stroke=\"black\" d=\"M822,-187C822,-187 708,-187 708,-187 702,-187 696,-181 696,-175 696,-175 696,-116 696,-116 696,-110 702,-104 708,-104 708,-104 822,-104 822,-104 828,-104 834,-110 834,-116 834,-116 834,-175 834,-175 834,-181 828,-187 822,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">seniority &lt;= 23.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.454</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1576</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1026, 550]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"765\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>8&#45;&gt;9</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M765,-222.907C765,-214.649 765,-205.864 765,-197.302\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"768.5,-197.021 765,-187.021 761.5,-197.021 768.5,-197.021\"/>\r\n",
       "</g>\r\n",
       "<!-- 12 -->\r\n",
       "<g id=\"node13\" class=\"node\"><title>12</title>\r\n",
       "<path fill=\"#e78b48\" stroke=\"black\" d=\"M1030,-187C1030,-187 932,-187 932,-187 926,-187 920,-181 920,-175 920,-175 920,-116 920,-116 920,-110 926,-104 932,-104 932,-104 1030,-104 1030,-104 1036,-104 1042,-110 1042,-116 1042,-116 1042,-175 1042,-175 1042,-181 1036,-187 1030,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">seniority &lt;= 25.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.133</text>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14</text>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [13, 1]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\"><title>8&#45;&gt;12</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M834.533,-225.836C859.027,-212.569 886.557,-197.656 911.083,-184.372\"/>\r\n",
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       "</g>\r\n",
       "<!-- 10 -->\r\n",
       "<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
       "<path fill=\"#f4caac\" stroke=\"black\" d=\"M750,-68C750,-68 644,-68 644,-68 638,-68 632,-62 632,-56 632,-56 632,-12 632,-12 632,-6 638,-0 644,-0 644,-0 750,-0 750,-0 756,-0 762,-6 762,-12 762,-12 762,-56 762,-56 762,-62 756,-68 750,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"697\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.465</text>\r\n",
       "<text text-anchor=\"middle\" x=\"697\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1029</text>\r\n",
       "<text text-anchor=\"middle\" x=\"697\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [651, 378]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"697\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>9&#45;&gt;10</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M739.679,-103.726C734.242,-94.9703 728.487,-85.7032 723.023,-76.9051\"/>\r\n",
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       "<!-- 11 -->\r\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\r\n",
       "<path fill=\"#f1bb94\" stroke=\"black\" d=\"M898,-68C898,-68 792,-68 792,-68 786,-68 780,-62 780,-56 780,-56 780,-12 780,-12 780,-6 786,-0 792,-0 792,-0 898,-0 898,-0 904,-0 910,-6 910,-12 910,-12 910,-56 910,-56 910,-62 904,-68 898,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"845\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.431</text>\r\n",
       "<text text-anchor=\"middle\" x=\"845\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 547</text>\r\n",
       "<text text-anchor=\"middle\" x=\"845\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [375, 172]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"845\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>9&#45;&gt;11</title>\r\n",
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       "<!-- 13 -->\r\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1021.5,-68C1021.5,-68 940.5,-68 940.5,-68 934.5,-68 928.5,-62 928.5,-56 928.5,-56 928.5,-12 928.5,-12 928.5,-6 934.5,-0 940.5,-0 940.5,-0 1021.5,-0 1021.5,-0 1027.5,-0 1033.5,-6 1033.5,-12 1033.5,-12 1033.5,-56 1033.5,-56 1033.5,-62 1027.5,-68 1021.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [10, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"981\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>12&#45;&gt;13</title>\r\n",
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       "<g id=\"node15\" class=\"node\"><title>14</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1100\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1100\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1100\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 1]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1100\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 0</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;14 -->\r\n",
       "<g id=\"edge14\" class=\"edge\"><title>12&#45;&gt;14</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1025.31,-103.726C1035.42,-94.423 1046.16,-84.5428 1056.25,-75.2612\"/>\r\n",
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       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x19a15baa488>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize Decision Tree\n",
    "import graphviz\n",
    "from sklearn import tree\n",
    "dot_data = tree.export_graphviz(dtc_best, \n",
    "                                feature_names=feature_names, \n",
    "                                class_names=['0', '1'],\n",
    "                                filled=True, \n",
    "                                rounded=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People who make a lot of money and very little are not likely to quit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By plotting the proportion of early quitter by salary percentile, this becomes quite clear:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['salary_percentile'] = pd.cut(data['salary'], 50, labels=np.arange(0,100,2))\n",
    "by_salary_percentile = data.groupby('salary_percentile')['if_quit'].mean()\n",
    "sns.lineplot(x = by_salary_percentile.index, y = by_salary_percentile.values.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "跟答案有所出入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Given how important is salary, I would definitely love to have as a variable the **salary the employee\n",
    "who quit was offered in the next job**. Otherwise, things like: **promotions or raises** received during\n",
    "the employee tenure would be interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. The major findings are that employees **quit at year anniversaries or at the beginning of the year**.\n",
    "Both cases make sense. Even if you don’t like your current job, you often stay for 1 yr before\n",
    "quitting + <u>you often get stocks after 1 yr</u> so it makes sense to wait. Also, <u>the beginning of the year is well known to be the best time to change job</u>: companies are hiring more and you often want to <u>stay until end of Dec to get the calendar year bonus</u>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Employees with low and high salaries are less likely to quit**. Probably because employees with high\n",
    "salaries are happy there and employees with low salaries <u>are not that marketable</u>, so they have a\n",
    "hard time finding a new job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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