HR Prediction-Tasks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><font size=\"16\">Human Resource Analysis</font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Companies want to improve and maintain a healthy relationship and environment for their employees. This can be achieved by recognizing and understanding the important factors that are associated with employee turnover and taking care of them might add into the productivity and growth of the employees. These insights can help managers for grabing the opportunity to take corrective steps to build and preserve their successful business. \n",
    "<br><br>\n",
    "In this notebook, we will work on the project that is based on understanding what factors contributed most to employee turnover and to create a model that can predict if a certain employee will leave the company or not.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- satisfacion_level: Showing satisfaction of a particular employee\n",
    "- last_evaluation: Showing last evaluation of a particular employee\n",
    "- number_project: Showing number of projects handled a particular employee\n",
    "- average_montly_hours: Showing the monthly hours that were spent the particular emloyee\n",
    "- time_spend_company: Shows the number of years spent by the particular employee in the company.\n",
    "- Work_accident: Showing an employee has whether been part of the company or not.\n",
    "- left: Tells either and employee has left the company or not. Shows two values 0= not left, 1= left\n",
    "- promotion_last_5years: Shows that the whether the employee has got any promotion in the last 5 years or not.\n",
    "- dept: Shows the departments\n",
    "- salary: Shows the salary type of the employee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangling & EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write code here\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set()\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading Data & Basic Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Task 1**:Load the data and after making a copy of it, find **shape, data types, basic statistics, and null values** from the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "data= pd.read_csv(\"HR_comma_sep.csv\")\n",
    "df=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>dept</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years   dept  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the top 5 rows.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "last_evaluation          float64\n",
       "number_project             int64\n",
       "average_montly_hours       int64\n",
       "time_spend_company         int64\n",
       "Work_accident              int64\n",
       "left                       int64\n",
       "promotion_last_5years      int64\n",
       "dept                      object\n",
       "salary                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the data types of columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "count        14999.000000     14999.000000    14999.000000   \n",
       "mean             0.612834         0.716102        3.803054   \n",
       "std              0.248631         0.171169        1.232592   \n",
       "min              0.090000         0.360000        2.000000   \n",
       "25%              0.440000         0.560000        3.000000   \n",
       "50%              0.640000         0.720000        4.000000   \n",
       "75%              0.820000         0.870000        5.000000   \n",
       "max              1.000000         1.000000        7.000000   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident          left  \\\n",
       "count          14999.000000        14999.000000   14999.000000  14999.000000   \n",
       "mean             201.050337            3.498233       0.144610      0.238083   \n",
       "std               49.943099            1.460136       0.351719      0.425924   \n",
       "min               96.000000            2.000000       0.000000      0.000000   \n",
       "25%              156.000000            3.000000       0.000000      0.000000   \n",
       "50%              200.000000            3.000000       0.000000      0.000000   \n",
       "75%              245.000000            4.000000       0.000000      0.000000   \n",
       "max              310.000000           10.000000       1.000000      1.000000   \n",
       "\n",
       "       promotion_last_5years  \n",
       "count           14999.000000  \n",
       "mean                0.021268  \n",
       "std                 0.144281  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 0.000000  \n",
       "max                 1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the basic statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dept</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4140</td>\n",
       "      <td>7316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         dept salary\n",
       "count   14999  14999\n",
       "unique     10      3\n",
       "top     sales    low\n",
       "freq     4140   7316"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    df.describe(include='O')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "satisfaction_level: left skewed\n",
    "last_evaluation: left skewed\n",
    "number_project: left skewed\n",
    "average_montly_hours: right skewed\n",
    "time_spend_company: right skewed\n",
    "Work_accident: right skewed\n",
    "left: right skewed\n",
    "promotion_last_5years: right skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_evaluation          0\n",
       "number_project           0\n",
       "average_montly_hours     0\n",
       "time_spend_company       0\n",
       "Work_accident            0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "dept                     0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving ahead, let us check the details of different variables in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 2: Find out the how many employees left the company?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac011ed308>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Count of how many employees left the company\n",
    "sns.countplot(x='left', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:What can you interpret from the plot above?** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: More than 3500 less han 4000 employees have left the company asper dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 3: Find out the number of projects being handled.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac019365c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Write code here\n",
    "sns.countplot(x='number_project', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What insights can you infer from the above plot?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: Maximum people around 4500 have 4 projects but around 2800 number of employee have 5 projects similarly around 1200 employee have six projects but there are people around 200 who have 7 projects assigned contrary to the 2400 approx employees assigned 2 projects only \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 4: Find out how number of projects contributed to employee turn-over.**\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>number_project</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_project  left\n",
       "0               2  1567\n",
       "1               3    72\n",
       "2               4   409\n",
       "3               5   612\n",
       "4               6   655\n",
       "5               7   256"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project_left = df.groupby('number_project').agg({'left': 'sum'})\n",
    "project_left = project_left.reset_index()\n",
    "project_left"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 5:** Make a plot of your findings (only turn-over employees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac02a47a48>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(x='number_project', data=project_left, y='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What can you conclude from the above graph? Which people are leaving the company(as per number of projects)? What can be the reasons behind?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: No ofpeople wih 2 projects assigned have the maimum turnout ratio. Maybe the incentive is low from two projects , may be they dont find growth also the personels with higher projects also have leaving trend. On the other hand 7 projects handled by employee may be those who are authorities or Project executives fails this trend. also people with 3 pprojecs are minimum toleavethe company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Time spent at the company**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 6: Find out how time spend at company can lead to employee turn over. Show the following plots.**<br>\n",
    "- Count of Number of years spent by employees.\n",
    "- After how many years are mostly employees leaving the company? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Hint: For the second part do the similar procedure as done in case of 'number_projects' above. Try to find the **percetage** to show that after how much time/years did most of employees exactly leave.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac02bd1c48>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Show the plot for the count of years here\n",
    "sns.countplot(x=\"time_spend_company\", data= df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What is the maximum number of time spend by the employees?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: 3years is the most common duration of employees stay in company  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Hint: For the second part do the similar procedure as done in case of 'number_projects' above. Try to find the **percetage** to show that after how much time/years did most of employees exactly leave.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>time_spend_company</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time_spend_company  left\n",
       "0                   2    53\n",
       "1                   3  1586\n",
       "2                   4   890\n",
       "3                   5   833\n",
       "4                   6   209\n",
       "5                   7     0\n",
       "6                   8     0\n",
       "7                  10     0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_left = df.groupby('time_spend_company').agg({'left': 'sum'})\n",
    "people_left.reset_index()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>left</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_spend_company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>1.484178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1586</td>\n",
       "      <td>44.413330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>890</td>\n",
       "      <td>24.922991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>833</td>\n",
       "      <td>23.326799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>209</td>\n",
       "      <td>5.852702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    left  percentage\n",
       "time_spend_company                  \n",
       "2                     53    1.484178\n",
       "3                   1586   44.413330\n",
       "4                    890   24.922991\n",
       "5                    833   23.326799\n",
       "6                    209    5.852702\n",
       "7                      0    0.000000\n",
       "8                      0    0.000000\n",
       "10                     0    0.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write the code here to get the percentage of employees leaving.\n",
    "people_left[\"percentage\"]= people_left[\"left\"] / df['left'].sum() * 100\n",
    "people_left.reset_index()\n",
    "people_left\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac02cce588>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Write the code here to draw a bar plot showing years after which employees are likely to leave\n",
    "people_left[\"time_spend_company\"]= people_left.index\n",
    "sns.barplot(x= \"time_spend_company\", y=\"left\", data=people_left)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: After what time period are employees most likely to leave the company ?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: after 3 years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Employees engaged in any work accident**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 7: Find out that how many employees were engaged in work accident and how many of them actually left? Use count plots to show your results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>left</th>\n",
       "      <th>not_left</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work_accident</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3402</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               left  not_left\n",
       "Work_accident                \n",
       "0              3402       NaN\n",
       "1               169       NaN"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"left\"].agg({'left'==0: 'count'})\n",
    "work_acc_left[\"not_left\"]= df[df['left']==0].agg({'left': 'count'})\n",
    "work_acc_left\n",
    "agg(lambda x: (x=='X').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac02da2f48>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Number of employees involved in work accident\n",
    "sns.countplot(x=\"Work_accident\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What can you conclude from the graph above?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: There is a majoriy seen from the graph that around 11000 people are not involved in any work accident. But there are aove 2000 and below 3000 involved in work accidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Work_accident  left\n",
       "0              0  3402\n",
       "1              1   169"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of employees involved in work accident and left or not left\n",
    "work_acc_left = df.groupby('Work_accident').agg({'left': 'sum'})\n",
    "work_acc_left.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "scrolled": false
   },
   "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>left</th>\n",
       "      <th>not_left</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work_accident</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3402</td>\n",
       "      <td>9428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               left  not_left\n",
       "Work_accident                \n",
       "0              3402      9428\n",
       "1               169      2000"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "work_acc_left[\"not_left\"] = df.groupby('Work_accident')['left'].agg(lambda x: (x == 0).sum())\n",
    "work_acc_left"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Promotions in last 5 years**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 8: How many number of employees got the promotion in last 5 year and how many of them left?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_employees_got_promote= "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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>left</th>\n",
       "      <th>no_of_promos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3552</td>\n",
       "      <td>14680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       left  no_of_promos\n",
       "promotion_last_5years                    \n",
       "0                      3552         14680\n",
       "1                        19           319"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_employees_got_promote= df.groupby('promotion_last_5years').agg({'left': 'sum', \"promotion_last_5years\": lambda x: pd.Series.value_counts(x)})\n",
    "number_of_employees_got_promote\n",
    "number_of_employees_got_promote = number_of_employees_got_promote.rename(columns={'promotion_last_5years': 'promo_last_5years', 'left': 'left', 'promotion_last_5years': 'no_of_promos'})\n",
    "number_of_employees_got_promote\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: It can be seen clearly that 14680 employees have not been promoted from which 3552 left\n",
    "        and 319 have been promoted from which only 19 left"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Salary trends**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 9: What are the salary trends in the data? Use graphical representation for explanation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ac02ef66c8>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Write code here\n",
    "sns.displot(data=df, x=\"salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: maximum no of people have a very low wages a very few around 1200 have a high salary. wheras around 6400 have a medium salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Quesion: Which type salary holders are most likely to leave? Try to show the percentage of employees who left according to their salaries, using a bar plot or as you like.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "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>salary</th>\n",
       "      <th>left</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high</td>\n",
       "      <td>82</td>\n",
       "      <td>2.296276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low</td>\n",
       "      <td>2172</td>\n",
       "      <td>60.823299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>medium</td>\n",
       "      <td>1317</td>\n",
       "      <td>36.880426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   salary  left    percent\n",
       "0    high    82   2.296276\n",
       "1     low  2172  60.823299\n",
       "2  medium  1317  36.880426"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal_type_left = df.groupby('salary').agg({'left': 'sum'})\n",
    "sal_type_left.reset_index()\n",
    "sal_type_left[\"percent\"]= sal_type_left[\"left\"] / sal_type_left['left'].sum() * 100\n",
    "sal_type_left.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac05752b48>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sal_type_left[\"salary\"]= sal_type_left.index\n",
    "sns.barplot(x= \"salary\", y=\"percent\", data=sal_type_left)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What does the above plot show?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: The 60.8% of employee  have left due to low salary and following the trend of medium which shows 36% of medium salary employee left  and the lowest number of people who leave are those who have high salary are just 2%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Employees per Department**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 10: Find out employees per department and also see which which department has highest number of employees leaving the company.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac7e055048>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write the code here to check employee count in each department. You can use a graphical representation or use simple code to check.\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(15,8)})\n",
    "sns.countplot(x= \"dept\", data= df, width=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: Which department has maximum number of employees?**\n",
    "\n",
    "Sales have higher no of employees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: Which department has highest percentage of turn-over? Use graphical representation to find out.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "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>left</th>\n",
       "      <th>percentage_turn_over</th>\n",
       "      <th>sal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dept</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>273</td>\n",
       "      <td>7.644917</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>121</td>\n",
       "      <td>3.388407</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>204</td>\n",
       "      <td>5.712686</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>215</td>\n",
       "      <td>6.020722</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>91</td>\n",
       "      <td>2.548306</td>\n",
       "      <td>[high, medium]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>203</td>\n",
       "      <td>5.684682</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>198</td>\n",
       "      <td>5.544665</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>1014</td>\n",
       "      <td>28.395407</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>555</td>\n",
       "      <td>15.541865</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>697</td>\n",
       "      <td>19.518342</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             left  percentage_turn_over             sal\n",
       "dept                                                   \n",
       "IT            273              7.644917             low\n",
       "RandD         121              3.388407          medium\n",
       "accounting    204              5.712686             low\n",
       "hr            215              6.020722          medium\n",
       "management     91              2.548306  [high, medium]\n",
       "marketing     203              5.684682             low\n",
       "product_mng   198              5.544665             low\n",
       "sales        1014             28.395407             low\n",
       "support       555             15.541865             low\n",
       "technical     697             19.518342             low"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write code here  which which department has highest number of employees leaving the company.\n",
    "\n",
    "df_dep_left =df.groupby('dept').agg({'left': 'sum'})\n",
    "df_dep_left.reset_index()\n",
    "df_dep_left[\"percentage_turn_over\"]= df_dep_left[\"left\"]/df_dep_left[\"left\"].sum()*100\n",
    "df_dep_left.reset_index()\n",
    "df_dep_left[\"sal\"]= df.groupby('dept')['salary'].agg(pd.Series.mode)\n",
    "df_dep_left\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac03093908>"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write code here to draw the plot\n",
    "df_dep_left[\"dept\"]= df_dep_left.index\n",
    "sns.set(rc = {'figure.figsize':(15,15)})\n",
    "sns.barplot(x=\"dept\", y= \"percentage_turn_over\", data= df_dep_left)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: From sales there is the maximum numer of employee who left the company as the sales men are low wages personnel so they might be having lower wage which could be the turn-out reason. The management on the other hand has medium to high wages which is can be the reason that the minimum amount of people leave from company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Satisfaction Level**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 11: Show the satisfaction level of employees who left the company and those who didn't leave, using a kde plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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f75VLJ1Spt1VrtPt+LwAAcHgEPQCIuHy5KcuKyU5Yfb+XYRiayNnaN8+qHgAAfiLoAUDE7Z2taNUAtm12jeeSjFgAAMBnBD0AiLi9c1WNZfu/bbNrLJOkIQsAAD4j6AFAxO2erQw06I3nbO1mRQ8AAF8R9AAg4nbPVDU+yKCXTWrPLCt6AAD4iaAHABG3d66q8QGe0RvLJTWdr8t1vYHdEwAAHIigBwAR1mh1VKo2NZJODOyeCctUJmVpplgf2D0BAMCBCHoAEGH75qoaz9qKxYyB3nciZ2sv2zcBAPANQQ8AImzvXFXjI4Pbttk1mmXEAgAAfiLoAUCE7ZmtaCwzuG2bXePZpHYzYgEAAN8Q9AAgwhY6bg5+RW9hxAJBDwAAvxD0ACDC9sxVNZ4b3GiFromcrX1ztYHfFwAALCDoAUBEeZ6nqTl/VvTStqVWx1W51hr4vQEAAEEPACJrvtRQIm4qmTAHfm/DMLQqZ2svDVkAAPAFQQ8AImrPXFWrfOi42TWeS2oP5/QAAPAFQQ8AImrvbFVj2cGfz+sayyRpyAIAgE8IegAQUbtnKxrL+Bf0xnM2s/QAAPAJQQ8AImrPTEXjOf+2bk4Q9AAA8A1BDwAiaq9PoxW6RjMJzZfqandc32oAAGBYEfQAIIIazY4q9bZyTsK3GkwzppFMUlPzzNMDAGDQCHoAEEF756oazyYVixm+1jGeTbJ9EwAAHxD0ACCC9s1XNebj+byuXDqhmQIregAADBpBDwAiaDpfU86J+12GRtIJTeUJegAADBpBDwAiaGq+ppG0f+fzukbSSU1zRg8AgIEj6AFABE3naxpJ+9dxs2s0k9BMoe53GQAADB2CHgBE0EyhrpFMEFb0Epot1uV5nt+lAAAwVAh6ABAxruspX24EYutmIm4qbsVUrDT9LgUAgKFC0AOAiMmXG0olLVlmMN7ixzJJTbN9EwCAgQrGpwAAQM9M52sazfh/Pq9rJJ3QDJ03AQAYKIIeAETMTKEeiG2bXdl0nBU9AAAGjKAHABEzna8pG4AZel0j6aSm5qt+lwEAwFAh6AFAxARltELXaDqh6TwregAADBJBDwAiZjpfC8Roha6RTFIzBc7oAQAwSAQ9AIiYoJ3RyzlxFcpNdVzX71IAABgaBD0AiJB2x1Wp2lLOCU7QM82YMqm45ooNv0sBAGBoEPQAIEJmi3VlnYRiMcPvUg4wmkkyYgEAgAEi6AFAhMwU6hoN0Pm8rpF0ghELAAAMEEEPACJkJl8L1Pm8rmw6rmlW9AAAGBiCHgBEyHS+plwAg95IOqGpeYIeAACDQtADgAiZmq8FqhFL10iaEQsAAAwSQQ8AImS6UA/UDL2u0UxCM5zRAwBgYAh6ABAhs4W6RtJJv8s4SCYVV63RVqPV8bsUAACGAkEPACKi0eqo3mwrk7L8LuUghmFoJJNkVQ8AgAEh6AFARCyMVkjKMII1Q69rNJ1glh4AAANC0AOAiJjJ1zSaCd62za5cmnN6AAAMCkEPACJiplBX1on7XcZh5RxGLAAAMCgEPQCIiKCOVugaySQYmg4AwIAQ9AAgIqbztUCOVugaSSc1zSw9AAAGgqAHABExXagFcrRC12gmoVnO6AEAMBAEPQCIiLliXaMBXtGzE6Y8T6rUW36XAgBA5BH0ACACqvW2Oq4nO2H6XcphGYahXDqh+WLD71IAAIg8gh4ARMBcsa6RdHBn6HXlnLjmSmzfBACg3wh6ABABc6WGcgEerdCVdRKaY0UPAIC+I+gBQATMlerKhCDoZVKWZous6AEA0G8EPQCIgLlCXZlU8INe1qHzJgAAg0DQA4AImC3WlU0Ft+NmVzYV1xwregAA9B1BDwAiYK7YUDYEWzez6YTmSpzRAwCg3wh6ABAB86WGck4IVvScuPLlpjzP87sUAAAijaAHACHneZ7my41QNGNJWKbilqFyjaHpAAD0E0EPAEKuUm/LjBlKxoM7LP25cmlGLAAA0G8EPQAIubBs2+zKOQmGpgMA0GcEPQAIubliXdl08LdtdmVScVb0AADoM4IeAITcXKkRitEKXZlUnKHpAAD0mdXPi19//fW6/fbbJUkXXXSR3v/+9x/0/Ztvvlm5XE6S9MY3vlFve9vb+lkSAETOXLGuTKqvb+c9lXXims4T9AAA6Ke+fTK45557dNddd+mWW26RYRh697vfre9+97u6+OKL97/m4Ycf1qc+9Sm95CUv6VcZABB5s4W6xrJJv8tYtKyT0OM78n6XAQBApPVt6+bk5KQ++MEPKpFIKB6P6+STT9bu3bsPeM3DDz+sz33uc3rDG96gj370o2o0OLMBAEs1V6yHYlh6V85JaJ6h6QAA9FXfgt6pp56qjRs3SpK2bdum22+/XRdddNH+71cqFZ1xxhm68sordcstt6hYLOqGG27oVzkAEFnz5YayIeq6mXXiKpSbchmaDgBA3/T9UMeWLVv0nve8R+9///u1YcOG/V9Pp9P6/Oc/v//X73rXu3TVVVfpz//8zxd97YmJTC9LHTqTk1m/S0BA8CyEl+d5KpSbOn7diBIrnKM3Our0qKqjc2xLCTuhsZw9sHti8XhPQBfPAiSeg7Dqa9DbvHmzNm3apKuuukqXXHLJAd/bvXu37rnnHl122WWSFj6sWNbSypmdLct1+Rvh5ZiczGp6uuR3GQgAnoVwK9daMk1D1UpD1RVcZ3TUUT6/kissTSYV15ZtszpxXW5g98Ti8J6ALp4FSDwHQRaLGUdc+Orb1s09e/bofe97n6699tqDQp4k2batT37yk9qxY4c8z9NXvvKVAxq1AACObq5YD9Ww9K6sk9AcIxYAAOibvq3o3XjjjWo0Gvr4xz++/2tvfvOb9YMf/ECbNm3SWWedpY9+9KP64z/+Y7VaLZ199tm6/PLL+1UOAETSXLGhbDqMQY+h6QAA9JPheeE9Dc/WzeVjGR5dPAvh9m8/26mHt87p4nOOX9F1Br1188eP7lMyYerNv37qwO6JxeE9AV08C5B4DoLMt62bAID+my3WlbbDM1qhK+vENcvWTQAA+oagBwAhNlusKxeiGXpdC2f02LoJAEC/EPQAIMTmiuGaodeVc+KaL7GiBwBAvxD0ACDE5ksNZUO4opdxEipVW5yzBgCgTwh6ABBSnucpXw7nip4ZM+TYlvJltm8CANAPBD0ACKlSraW4ZSpuhfOtPOckNFci6AEA0A/h/HQAANB8saGREM7Q68qmGZoOAEC/EPQAIKTmSvVQns/ryqTimmdFDwCAviDoAUBIzRUbyqRCHPRsS7MFVvQAAOgHgh4AhNRssR7qoJdLJxiaDgBAnxD0ACCk5op15ULYcbOLoekAAPQPQQ8AQmquWFcmxGf0sqm4ChWCHgAA/UDQA4CQypebyoZ462bathaGpnsMTQcAoNcIegAQQp7nqVBphvqMnmnGZCdMlaotv0sBACByCHoAEEK1RluxmKFE3PS7lBXJOgnlGbEAAEDPEfQAIITCvm2zK5OylC8T9AAA6DWCHgCEUL4c7hl6XelUnKAHAEAfEPQAIIQiE/TsuObZugkAQM8R9AAghPLlphzb8ruMFSPoAQDQHwQ9AAih+VJdGTv8K3qZFEEPAIB+IOgBQAjNFxuhHpbeleGMHgAAfUHQA4AQmi+He4ZeV9aJq1Bu+l0GAACRQ9ADgBAqRKQZi5O0VG201e64fpcCAECkEPQAIGQ8z1Ox2lQ6Amf0YjFDjm2pWGFVDwCAXiLoAUDIVOptWWZMcSsab+FZJ6E82zcBAOipaHxKAIAhki83lHUSfpfRMzRkAQCg9wh6ABAy+XJD2Qicz+tK2xZBDwCAHiPoAUDI5EtNpVPhH5bexdB0AAB6j6AHACGTLzci0Yili6HpAAD0HkEPAEJmrlRXOkJbNzMpi6AHAECPEfQAIGTypWgMS++iGQsAAL1H0AOAkJkvN5Sxo3NGL5OKq8B4BQAAeoqgBwAhUyg3IrWil0paarQ6arU7fpcCAEBkEPQAIERcz1Op2orUGT3DMJR14gxNBwCghwh6ABAi5VpLybgpy4zW2zfn9AAA6K1ofVIAgIjLlxrKOtFZzevKOAlW9AAA6CGCHgCESL4crY6bXWnbUp4RCwAA9AxBDwBCJF9uROp8XlfatjTP1k0AAHqGoAcAIZIvN5SO0GiFrkwqrvli3e8yAACIDIIeAITIfLGhtB29Fb1MKq55zugBANAzBD0ACJH5iM3Q66LrJgAAvUXQA4AQyZeiG/QKrOgBANAzBD0ACJF8JZpdN5NxU67nqdZo+10KAACRQNADgJBwXU+VWiuSXTcNw1DWiatQYVUPAIBeIOgBQEiUqk3ZSUtmzPC7lL7IpOLM0gMAoEcIegAQEvlyU9kIruZ10ZAFAIDeIegBQEjMlxvKONENemk7rjwNWQAA6AmCHgCERL7cUDoZvWHpXWnb0nyJoekAAPQCQQ8AQiJfasixoxz0WNEDAKBXCHoAEBLzEZ2h15VOWSpwRg8AgJ4g6AFASBQqTaXt6Aa9TIrxCgAA9ApBDwBColBuRnKGXlfajqtYJegBANALBD0ACIlitalMhM/o2QlTzZarVrvjdykAAIQeQQ8AQsDzPBUrTTkR3rppGAbbNwEA6BGCHgCEQLXRVtyKKW5F+207k4qrQOdNAABWLNqfGAAgIgrlZqQ7bnY5tsWKHgAAPUDQA4AQKFSi3YilK22zdRMAgF4g6AFACBTKDaUj3Iily7GZpQcAQC8Q9AAgBAqVppxk9INe2rY0XyLoAQCwUgQ9AAiBfLkhZwhW9Ni6CQBAbxD0ACAECuWm0hEerdBF100AAHqDoAcAIZAvN4ai62batlSosHUTAICVIugBQAgUKs2haMaSTsVVqrbkeZ7fpQAAEGoEPQAIgVK1NRRbNy1zYSh8pd72uxQAAEKNoAcAAdfuuKo22koNQddN6ZlzejRkAQBgRQh6ABBwpWpLTtJSLGb4XcpALDRk4ZweAAArQdADgIArVIajEUuXY1us6AEAsEIEPQAIuEK5qXRqOLZtSs/M0mPEAgAAK0LQA4CAW+i4OUQreklLebZuAgCwIgQ9AAi4QqUpZ0gasUhSOkXQAwBgpQh6ABBw+VJjKGbodWXYugkAwIoR9AAg4ArlhtJD1IwlzXgFAABWjKAHAAGXH7IzemnbUpGgBwDAihD0ACDgipXmUG3dTCUtNVodtdqu36UAABBaBD0ACLhStTlUWzcNw1A6ZalUZVUPAIDlIugBQIDVm225npSwhuvtOmPHlachCwAAyzZcnxwAIGQKlaYyqbgMw/C7lIFaaMjCiAUAAJaLoAcAAVYoD9f5vK60TedNAABWgqAHAAFWrAzX+byuVNJUgaHpAAAsG0EPAAKsMGQdN7vSdlz5Eit6AAAsF0EPAAIsX27ISQ7fil46FVeeFT0AAJaNoAcAAZYvN4ZyRS9jW5zRAwBgBQh6ABBghfJwntFb6LpJ0AMAYLkIegAQYMN8Rq9UbcrzPL9LAQAglAh6ABBgxUpTaXv4VvTiVkymGVOt0fa7FAAAQomgBwAB5XqeyrWWnCFc0ZOkTCqufJntmwAALAdBDwACqlxrKRE3ZZnD+VadYWg6AADLNpyfHgAgBIrlpjJD2Iily7EtFQl6AAAsC0EPAAKqUGkqkxrObZuSlEoS9AAAWC6CHgAEVLHSlJMc3qCXti3lKwxNBwBgOQh6ABBQhUpTqSHsuNnl2JYKNGMBAGBZCHoAEFCFSkNO0vS7DN+kacYCAMCyEfQAIKAK5eGcodeVphkLAADLRtADgIAqVJpDO0NPkhw7rlKVoAcAwHIQ9AAgoIpVVvRK1ZY8z/O7FAAAQoegBwABVao0lR7iFT3LjCluxVRttP0uBQCA0CHoAUAAuZ6ncr091OMVJCmditN5EwCAZSDoAUAAVWotJeMxmeZwv03TkAUAgOUZ7k8QABBQxcpwn8/rSttxFWnIAgDAkhH0ACCAipWm0imCXippMUsPAIBlIOgBQAAVqk2lh/x8niQ5SbZuAgCwHAQ9AAigYqWl1BB33OxK25by5YbfZQAAEDoEPQAIoEKlMfQdN6WFoel03QQAYOkIegAQQIVyUw4renTdBABgmQh6ABBABXfD80QAACAASURBVLpuSnom6NF1EwCAJSPoAUAALYxXYEXPseMq11ryPM/vUgAACBWCHgAEUKnalMOKnuLWwtD4WqPtdykAAIQKQQ8AAsbzPJWqLVb0npGx48zSAwBgiQh6ABAw1UZblhWTZfIWLUnpFA1ZAABYKj5FAEDAFCtNZdi2uZ9jx1WstvwuAwCAUCHoAUDAFCtNpVNs2+xykqYKDE0HAGBJCHoAEDCFCo1YniuVtDijBwDAEhH0ACBgipWmnKTpdxmBkbbjKpQJegAALAVBDwACplBpKpVk62ZX2rZUqLB1EwCApSDoAUDAFCpNpdm6uV/ajqtYoRkLAABLQdADgIAplBvM0HsOx7ZUrLJ1EwCApSDoAUDAFCstmrE8R9qOq1RtyfM8v0sBACA0CHoAEDDFapMVveeIWzHFYlKt0fG7FAAAQoOgBwAB4nmeStWWHILeAbKpONs3AQBYAoIeAARIvdlRzJASFuMVnsux4yoySw8AgEUj6AFAgBQrTaVTnM97vrRtEfQAAFgCgh4ABMjCaAW2bT5fyrZUIOgBALBoBD0ACJAiM/QOyUlaKjI0HQCARSPoAUCAFKtNGrEcgpOMK19mRQ8AgMUi6AFAgBTKTaWSBL3ny6TYugkAwFL0Nehdf/31uuSSS3TJJZfob/7mbw76/mOPPaZLL71Ur33ta/XhD39Y7Xa7n+UAQOAVKg3O6B2CY8cJegAALEHfgt4999yju+66S7fccou+8Y1v6JFHHtF3v/vdA15z5ZVX6pprrtGdd94pz/N000039ascAAiFQrkphzN6B0nblkoEPQAAFq1vQW9yclIf/OAHlUgkFI/HdfLJJ2v37t37v79r1y7V63Vt3LhRknTppZfqjjvu6Fc5ABAKxSpdNw/FsS2Vai15nud3KQAAhELfPk2ceuqp+/9527Ztuv322/XVr351/9empqY0OTm5/9eTk5Pat29fv8oBgFAoVlo0YzmEhGXK0MJAec4wAgBwdH3/03LLli16z3veo/e///3asGHD/q+7rivDMPb/2vO8A369GBMTmV6VOZQmJ7N+l4CA4FkIjnKtpWPW5GQnBh9mRkedgd9zKXKZhOJ2QpOr0n6XEnm8J6CLZwESz0FY9fWTxObNm7Vp0yZdddVVuuSSSw743tq1azU9Pb3/1zMzM1q9evWSrj87W5brso1nOSYns5qeLvldBgKAZyE4Gs2OOq6rWqWhenWw59FGRx3l89WB3nOp7LipbTvmZXmu36VEGu8J6OJZgMRzEGSxmHHEha++ndHbs2eP3ve+9+naa689KORJ0rHHHqtkMqnNmzdLkm699VZdeOGF/SoHAAKvWG0qk4oveXfDsHDsuIoDDsAAAIRV31b0brzxRjUaDX384x/f/7U3v/nN+sEPfqBNmzbprLPO0rXXXqurr75a5XJZZ555pt7+9rf3qxwACLxipak0HTcPy0laKtJ5EwCARelb0Lv66qt19dVXH/T1t7zlLfv/+fTTT9fXvva1fpUAAKFSrDZpxHIEqaRJ0AMAYJH6OjAdALB4pWpLDh0lD8ux48pXGn6XAQBAKBD0ACAgCpUmowOOIG1bKpZZ0QMAYDEIegAQEMVKgxW9I3Bsi2YsAAAsEkEPAAKiUOaM3pGkk3EVKy2/ywAAIBQIegAQEMUKQe9IHNtSqUbQAwBgMQh6ABAQxWqL8QpHYCdMNVsdtTsMTAcA4GgIehhqhXJDe+eqfpcBSJJK1SZn9I7AMIyFc3qMWAAA4Kj4RIGhVao29f9+5WcqVpp66WmT+i8XnKTxnO13WRhSHddVrdmh6+ZRpO24StUW/1sFAOAoWNHDUGo0O/rUTb/QSety+qM3vECu6+maG3+im37wSzVbHb/LwxAq19pKJUzFYobfpQRa2rZUYEUPAICjIuhh6LQ7rq6/5SGNOAld8KJ1shOWLnjRMXrH607T40/P686fPu13iRhCxUqT83mL4NiWSoxYAADgqAh6GCqe5+mfb3tM9WZbF597vAzj2dWTrJPQq88+Vt+7f6dabVb1MFjFalPpFNs2jyaV5IweAACLQdDDUHnoqVk9uauoN7xyg8xDbJFbNZLS2nFHdz+014fqMMyKlSbn8xYhlWTrJgAAi0HQw1C568E9evEpE0pY5mFfc85pq3Xbfdvlut4AK8OwK1XouLkYadtSodzwuwwAAAKPoIehUa239PDWOZ1+wtgRX3fcZFp2wtTPnpgeUGWAVKiyorcYaTvOih4AAItA0MPQuP/xaa1fmz3qh2nDMHTOaav17Xu3y/NY1cNgFMpNOTZB72icpKVSteV3GQAABB5BD0Pjrgf36AXrj7ya13XKsSMq11p6Yke+z1UBC+i6uTiOHVeRrpsAABwVQQ9DYTpf0+7Zik5al1vU62MxQ+ecNqlv3bO9z5UBC4pVzugthmNbqtTbclltBwDgiAh6GAr3PLxXp58wJtNc/CN/5onjempPQfMlGj+g/0rVFls3F8GMGUrGTVVqbN8EAOBICHqIPM/zdPdDe3TmhsVt2+yyzJjWr8np0W1zfaoMWOB5HkFvCdK2pSLn9AAAOCKCHiLvyd1FSdLacWfJP3vCmowe3jrb65KAA9SbHcUMHXHsB56VTsUZmg4AwFEQ9BB5dz+0Ry/YMCbDOHhA+tFsWJvVo9vm6b6JvipWm0qnaMSyWE7SIugBAHAUBD1Emut5+ul/TumM9ePL+vnRTFKWGdOu6UqPKwOeVaq0lGbb5qKlkhadNwEAOAqCHiJt93RFdsLUSDqx7GtsWJvVI5zTQx8VKk05jFZYNFb0AAA4OoIeIm3LzryOm8ys6BonrMnqoac4p4f+KTFaYUkc21KhTNADAOBICHqItMd35HXMRHpF11i/JqMndxXVars9qgo4ULHaVCpJI5bFStuWChXGngAAcCQEPUTalp0FHTe5sqBnJyytGrX1y12FHlUFHChfbrB1cwkcO854BQAAjoKgh8iaLdTVbHU0lk2u+FonrM7oEcYsoE+KlRZbN5cgbVsqcUYPAIAjIughsrbsyuu41ZlljVV4vg1rc3r4KRqyoD+KlQbD0pfASVoq1VqMPQEA4AgIeoisx59e+fm8rmMmHO3L11SusV0MvVesMl5hKRLxhfOMjVbH50oAAAgugh4i64kd+RWfz+syzZhOWJ3Ro4xZQB+Uqy3O6C1R2o4zYgEAgCMg6CGSKvWWZot1rR5zenbN41dn9PBWgh56q91xVW91lErQdXMpMimLhiwAABwBQQ+R9OSugo5dlZYZW/n5vK4TVmf0xI58z64HSFKputCIpRdnSYeJw4oeAABHRNBDJD2xI691PTqf1zUxktJ8qaFGk3NB6J1ipal0ivN5S+UkLRWrBD0AAA6HoIdIenxHXset6m3QM2OGVo+mtGOq3NPrYriVqk2lOZ+3ZKmkyYoeAABHQNBD5LTarnbsK2tdj4OeJK0eS2n7vlLPr4vhVaw2maG3DE7SUr7c8LsMAAACi6CHyNm2t6iJEVvJeO+bW0yOprR1T7Hn18XwKlZaShH0lmzhjB7NWAAAOByCHiLniR15HduH1TxJWjPmaPteVvTQO4VKgxW9ZXBsS8UKK3oAABwOQQ+R88udhZ43YumaHLU1la+p1aYhC3qjWGnKYVj6kqVtxisAAHAkBD1Ezo7pslaPpfpybcuMaSJna+d0pS/Xx/ApVDijtxyOHVeJoAcAwGER9BAptUZbpWpLY5lk3+6xeizF9k30TLHakkPXzSVLJUw1Wh21O67fpQAAEEgEPUTKrpmKJkdsxXo4KP35VtOQBT1UrjaVZuvmkhmGISdpsaoHAMBhEPQQKTuny1o12p9tm11rWNFDj3iep1K1xRm9ZUqnLGbpAQBwGAQ9RMqOfSVN5Oy+3mNyLKU9c1W2jGHFao22LCsmy+SteDnSdlylKkEPAIBD4dMFImXHVFmTo/0NegnL1Ggmod0zNGTByhSrLbZtroCTtFRgRQ8AgEMi6CEyPM/TrpmqJvu8dVN6Zp7ePrZvYmWKlabSNGJZthRn9AAAOCyCHiJjvtRQLKaBfHCeHE1pG+f0sEJFRiusyMKKHkPTAQA4FIIeImPndEVrRp2B3GvNWErb9hD0sDKlKsPSV8KxLRXKbN0EAOBQCHqIjJ3TZU2M9Pd8XteaMUe7Z8pyXW8g90M0FSpNpVjRWzbHtlSkGQsAAIdE0ENkPL2vpFUDCnrJhKl0Kq69c9WB3A/RxNbNlXGSccYrAABwGAQ9RMZCx83+N2LpWktDFqxQocLWzZVI2zRjAQDgcAh6iIR2x9V0vtb3GXrPNTFi62kasmAFipzRWxHHtlSuteR5bKEGAOD5CHqIhL2zVY1mkopbg3ukJ3K2ds0ySw/LV6q0lE4yXmG5LDOmuBVTtdH2uxQAAAKHoIdI2Dk92G2b0sKK3t5Zzuhh+Uq1Fit6K5S2OacHAMChEPQQCTumywPdtilJo5mk8uWGWu3OQO+LaGh3XDVaHdkJ0+9SQs2xLYIeAACHQNBDJDy9r6zJ0cEGPTNmaDST1L752kDvi2goVVtK25YMw/C7lFCjIQsAAIdG0EMk7PJh66a0cE6P7ZtYjmKlqbTN+byVSiUtFVjRAwDgIAQ9hF6l3lK10dZIOjHwe49lk9o9Q0MWLF2Jjps94STZugkAwKEQ9BB6u6YrmhxN+bIFbjyX1C6CHpahwLD0nuCMHgAAh0bQQ+jtmi5r1YAbsXRN5GztYcQClqFUbSlF0Fsxx46zdRMAgEMg6CH09sxWNZZN+nLv8aytffM1uQxsxhIVKg1W9HrASVoqVgl6AAA8H0EPobdnzr+gl0yYshOm5op1X+6P8CpWOKPXC2zdBADg0Ah6CL2p+arGs/5s3ZSkVQxOxzJwRq830ralco3xCgAAPB9BD6HW7riaKzY0khl8x82usWxSewh6WKJitSWH8QorloybarVdtdodv0sBACBQCHoItdlCXbl0Qpbp36M8nrW1a6bs2/0RTuVqU2m2bq6YYRjKpOIqVljVAwDguQh6CLW9c1WN+3Q+r2s8l9TuGVb0sHie59F1s4ccm4YsAAA8H0EPobZvvqaRjL9BbyJna+8cQQ+LV2u0ZZkxxS3egnuBhiwAAByMTxkItT2zFY35eD5PkjKpuJrtDg0hsGjFakvpFKt5vZJOxlnRAwDgeQh6CLV9c1WN+dhxU1o4I7RqJMWqHhatWGkqTSOWnrGTJit6AAA8D0EPobZvvubbDL3nmsgltWem4ncZCIlSldEKveQk2boJAMDzEfQQWs1WR6VqUyNpf7duStJoJqndswQ9LE6x0qQRSw+l7bgKBD0AAA5A0ENoTeVrGs0kFYsZfpeiiZytXazoYZEKlaYcRiv0jGNbBD0AAJ6HoIfQ2jcXjG2b0jOdNxmajkUqVti62UuObalEMxYAAA5A0ENo7ZuvBibojWYSypcbarU7fpeCECiyotdTTjKuEgPTAQA4AEEPobVntqJRn2fodZlmTKOZpPbN1fwuBSFQoBlLTzm2pUqjLdfz/C4FAIDA4JMGQmvvXFXnnLba7zL2G8smtW++puNWZ/wuBQFXqrQYr9BDZsxQMm6qXGsp5/jfnGmYeK26vGZNajfltZuS25GshAwrsfD/kxkZMf5OGQD8QNBDaE3N1zQekK2bkjSSTmg6z4oejq5Ua7F1s8cyKUulSpOg1wee58mrzKszs1Wdqafkzu2UV5qRW5mXOi0ZcVsy45IVl4yY1GlLndZC8Gs3ZNhZGekxxTITio0fr9j4cTLHj5ORm5RhEAIBoF/4pIFQqjXaqjc7yqSCsyoykklqH0PTcRTtjqtGqyM7YfpdSqQ4dlzFakvH+l1IRHjNqto7H1F7+wPq7HxYnttWbPQYxXKrZa7aIOOEF8tIjUiJlAzj8J2PPbcjr16WVy/JqxbkFvaqvesRecUpea26zMmTZK49df//GVZw/vIOAMKOoIdQmpqvaTyXPOIHjEEbzST0yNY5v8tAwJWqLTlJK1DPbhQwNH3lvHZD7W0/0+7v3KP6zicUmzhe5qoNSrz8TTKc0WU9s0bMlOGMSM6INH7cgfdrVOTO75ab36329p/LLexVbGK9rONeKOv4Fyq26kS2fQLAChD0EEr75qsaz9p+l3GA0UxSM4W632Ug4IqVZqBWoqPCsQl6y9WZ2a7mQ99Re9vPFBs/VrlTN0pnXCzD7O9zaiTT+1fypIWg6c7ulDv7tGpb7pbXqMg69kxZJ7xY5vFnKZbK9bWeXvM6LXmVebmVeXmVeXn10sKW1k5bctuSGZeRTMtIODJSOcVG1y07UAPAoRD0EEp7Z6saSQfrLM5IOqH5UkMd15XJ30LjMEpVRiv0QyphqcgsvUXzXFftp3+u5i/ukFeckrl+o+yL3iXDzig16qiRH/w2dMNKylxzssw1Jysuya0V5U49pdbj/6H63V9a2DZ6wosVP+HFik2eKCMWjO3PnufJK06pM7NNndmn5c7tlDu/W15lXoadkZHKykhmF7a5xkwpZkqx2ELjmlZDXqshr1mRW5qRPFexkXUy15wi6/gXylx3GttZASwbnzYQSnvngjNDr8syY8qk4potNrR6NOV3OQioIqMV+sKxLeXLDb/LCDzPddV+6sdq3P8NGaYlc8PZMl/6O4EJTc8VS+UUW79RWr9RntuRO7dL7vRTqv3w8/KqRZnrTpN1/Fky152u2Ni6gTR22d+YZvopdaaelDv1lDoz22XEk4qNrJWRnZQ5eaKsE8+RkR5b8u+r16jKLU3Lndulxk9ullvYK3Nyg+KnvkrWSefKSPBnC4DF49MGQmnPXFUnHRO8bTxj2aSm52sEPRxWsdJSiqDXc2nb0p7Zit9lBJbnuWo/+UzAsxKKn/Griq1aH5ptgkbMlLnqBJmrTlBcklcvqzOzXe2nf6HmA9+S12rIXHOKzLW/InPiBMUmjl/xNkiv05Zb3Cd3frc6M9vlTm+VO/u0PM9daEwzskbm8Wcp/sKLZSTTvfn3TDoyk+tlrlov6ZXy2k2501vV2nK36vf+q6zjX6T4C14tc93poflvB8A/fNpAKE0HbLRC10gmoal8TWf6XQgCq1BpsKLXB44dV7HS8ruMQGrvfESN+74qeZ7iZ1yk2KoNoQ8Jhp2RddyZ0nEL77ZeraTO/ML5vva2zXKLU5KkWHZSscyEjOwqxdJjUtyWEU9KVlKSJ7Vbz4yCaMitFuRV5hbO1ZVm5JXnZDgjimVWyciuWlhBPP3ChXERA/r9M6yEzHWnyVx3mrxmVZ1dj6n+7zfKSDhKbLxkYeUwgKuxAIKBTxsInXKtJdfzArkqMuIkNDXPiAUcXrHS1GgmeH9JEXZpmzN6z9eZ26XGfV+VO79b8dMuUGzdaaEPeIdjpLKyUmdIx5whaWGLpRoVudW8vFpRXq2k9tST+4OdOi3JMKSYtXBmzrQWmqLYWcVG1slycjKcMRlmcP6cMRKOrBNfKnPD2XL3/VLNB76txo9vUvKcS2Wd8go6lAI4SHDewYBFmpqvaSwbrNEKXaPZpJ7eV/a7DARYodLUMat6s80Lz3JsS+VaS57nBfK9YZC8RkWNn35drSfvU/yUVyj+otcN3aqPYRiSnZFpZ/wupecMw9jfrbQzu0PNB+9Q84FvK/my35d5wsahf/4BPIugh9CZKdQ0EtAVkdFMUpsfn/a7DARYsdqSk2S8Qq8lrIUgU292ArnaPwie66r1n/+uxv03y1z7K7Iv+kOad0ScOXG8Yq94i9ypJ1W/96syHrhN9vlvlzlxvN+lAQiA4fzTEKE2U6gr5wTzg/JoJqGZQo1VBRxWqdJUmvEKfZFJxVWsNocy6HWmt6r+o3+R5Cl57mWKjazxuyQMiGEYMtecotjqk9R5+kFVv/VxxU95uZLnXNqzJjEAwmn4/jRE6E3NB2+GXpedsBSLGSpVW8oFtEb4x/M8lWst5uj1Sdq2VKq0tGbM70oGx2tU1PjJ19Teer+s0y6QedwL+UumIWUYMVnrN8pc9ytqPX6XKv/fh5R85VtknfxynglgSPFpA6Ezna/rzA3B/SQ3lrU1la8R9HCQaqMty4rJMmma0A+OHVehMhwNWTzPWxiXcO+/Krb6FCUvfJeMhO13WQgAI+EocdZr5B63W437v6HWE3fLvuCdimVX+V0agAHj0wZCZ6ZQ00g6mGf0pIXtm9PzNb/LQAAVK01l7GBuO44CJzkcnTfd4pRqt31Sjc23KHH27yhx1sWEPBwkNnaMkuf/gYzMhCo3X6PGQ9+R57l+lwVggFjRQ6i4nqf5UiPQq2Uj6YT2MWIBh1CsNNm22UeppKlShFf0PLezv8OidfLLlHzxbw5dN00sjREzFT/l5TLXnqrWg3eq/dRPlXr1FYrlVvtdGoAB4BMHQqVQbiqVsBS3grsYPZJOaooVPRxCqdqiEUsfOXZc+XLD7zL6ojOzXfV//ycpZin5qj9QLD3qd0kIkVhmQolXvFntrT9T5esfUeKcS5U489dkGMH9sxTAyvGJA6Eyna9pJBPc1TxJGs0m9MTOvN9lIIAKleHsCDkoadvSjqlozbH0Oi01Nt+q1mP/pvjpF9FsBctmGDHFTzpH5uoT1frFHeps2yz7V9+tWGbC79IA9Al/lYNQCfIMva6xTFLTeVb0cLBipSmHoNc3jh2tM3qd2adV/fpH5O59QvYF75R1/FmEPKzYwureW2TkVqty8/9Qa8s98jzP77IA9AGfOBAq0/laYGfodWVScdUabTWaHSUTnJ/BswqVBmf0+iidjKtYafldxop5bkfNX9ym5oN3KH76r8o87kwCHnrKiMUWzu5NnqjG5m+otfV+pS58lww743dpAHqIFT2EytR8PdCNWKSF4bWjrOrhEIqVlhy6bvaNY1sq18K9oudW86p+6xNqb92s5Plvl3U8WzXRP7GRNUq+6g9kxCxVvvZhtXc85HdJAHqIoIdQmSnUNBrg0QpdY9mkpgh6eJ4CWzf7yk6YarZctdrhbCHf3vWoqjdfo9jIGiVe9vuKpXJ+l4QhYJiW4i94teIver3qP/wn1e/6orx2NJsaAcOGoIdQmSnUA9+MRZJy6QQrejhIqdqk62YfGYYhx7ZUCtk5Pc/z1Nh8q+rf/3vFX/x6xU99Jd0QMXDmqvVKXvhOucV9qtx8jToz2/wuCcAK8ScJQqPdcVWsNJV1gh/0RtMJ7Ztjlh4OVKq2OKPXZ5lUPFQNWbxOW/V/+0e1n/qJkuf/gcxVG/wuCUPMiNtKbPwtWSedp+q3P6n5u2+W54ZzhRwAzVgQInOlhrJOXGYs+OdVRjNJPbp93u8yECDNVkftjqtknAY9/eQkLRVDMjTda9ZU+85nJbetxMvfJMPk/CaCwTr2BYqNH6fKI3eq/eiPlfq19yqWm/S7LABLxIoeQmMmH/zRCl2jWZqx4EDFalOZVJzGGn3m2JYKIQh6bjWv6q0fk+Ipxc/+XUIeAieWymn81/9AsVXrVbnlI2o+/h+MYQBChhU9hMZMoa6RgHfc7BpJJzRfash1PcVCsAKJ/itVW5zPGwAnaalUDfaIBa9eVvX/fFzm2lNlnfIKwj8Ca2HI+rkyV61X8+f/R+3tP2cMAxAirOghNMIwQ6/LMmNybEv5Mp3LsKBQaXI+bwBSSUuFAP/vzmvVVb3tWpmrNjzTdIWQh+CL5VYzhgEIIYIeQmNqvhb4GXrPNZJOaKZQ97sMBESp0mSG3gCk7bjy5WBu3fQ6LdXu/IwMZ0TW6Rf6XQ6wJIxhAMJnUUHvT//0T3XPPff0uxbgiGYKNY2EYIZeVy6d0CxBD88oVptKJWjE0m+ObQWy66bnuar/4B8l11X8ha9hJQ+hZa5ar+QF75RbeGYMw/Q2v0sCcBiLCnoXX3yxbrjhBr32ta/VjTfeqHw+3++6gIPMFOoaDcEMva6ck9BMkaCHBYUyK3qD4NjB7LrZfPBOufndir/kt2TE2EyDcDMSthIveWYMw22fVONnt8pzO36XBeB5FvWnzW//9m/ry1/+sm644QbNzs7qsssu05VXXqkHH3yw3/UBkhZa09cabWVS4fmgnHUYmo5nFSpNOUnO6PVb2o4HrhlLZ3qbmg98ayHkmTwDiA7r2Bcoef7b1d72c1W/+TG5xSm/SwLwHIv+a0XXdbV9+3Zt27ZNnU5HExMT+shHPqLPfvaz/awPkCTNFusaSSdDtd1pJJ3QDEEPzyjSjGUgnKSlaqMt1w1GG3ivWVPte3+n+Jm/rpgz6nc5QM/FUjklXvZGmas2qHLLX6n52A8ZwwAExKI+dXz605/W17/+dR1//PF661vfqs985jOKx+OqVqt69atfrU2bNvW7Tgy56XxdIyHatiktBL1Ztm7iGcVqk/EKAxCLGbL/f/buNEiy86wT/f89W54lt9qru9WbFncLW7Yly7INtvHAgMwE3Au+XIblsg76wBcIBxgzwtgEgcMReAFHMAxccbkE2DcGIbjjIa6xEQgzIK8II3dbW+/d1bXmepbMPJl5zrkfUtVo6aWWzDzb//dJqqqufNQ6lXWe8z6LKsPtDhIxvKn35J9CmjkI5eDdcYdCNDFCCCi3vxnSwjH0//WzGF78F+jf/p8gmZW4QyPKtR3ddTQaDTzyyCM4efLkyz5umiY+/vGPTyQwopeqtbsom/HftO1G2dLQdPoIowhSik4iaTKczoA9elNiGSpsrx97ojc4+2UEay+g8PYfjzUOommRSgsofNv/geELT6Lz2K+h8I6fhHr8TXGHRZRbOyrdDILgVUne9ine29/+9vFHRfQKW80uyla6bpJVRUJBlRI5GIKmKwwjdPwhe/SmxErA5M2o34X/pf8H2hu+B0JJ10Mqov0Qkgz15Duh3vd98L/4aXT//v9E1O/EHRZRLt30ruNDH/oQNjY28NRTT6HRaFz7+HA4xJUrVyYeHNG2rVYXMPSfKQAAIABJREFUhxaKcYexa9ViAfV2D9VietZC0Pi53QF0VYYk8WR3GpIwedP/1/8P0txRSDMHY42DKC7y7G2Q3vGTGDz7D/D+/Fehv+shKIe+Je6wiHLlponeD/7gD+LMmTN4/vnn8eCDD177uCzLeOMb3zjx4Ii2bbV7eO3x2bjD2LXyi0vT7zjEPoU8szt9WCmaGJt2RiHeRC90ahg88wT0d/xkbDEQJYFQNGj3fBeCzfPoPfEHUI7fh8Jb/iOEqscdGlEu3DTRu+eee3DPPffg277t27C0tDStmIhepW73UtejBwAlU+VAFoLtcRDLNJkFBe0YSzf9r/wZlGP3QRjl2GIgShJ58XZI7/gpDJ55At5jHxid7h04EXdYRJl30zuPX/iFX8AnP/lJ/OzP/ux1P/9Xf/VXEwmK6KX8foD+IEjlaPqyqWGryRULeWd3uFphmkxdQduNJ9EL1s8gWHsehW//T7G8PlFSCU2H9sb/gGD9DHp/+1+gHL8fhbf8EE/3iCbopnceDz30EADg137t16YSDNH1NJz07dDbVrY0PHe5GXcYFDPbG8DgIJapsXQVK1ve1F83ikL0vvhpKCfewQEsRDcgL98FafY2DJ79ArxHH4b+zp+GcvieuMMiyqSbTt183eteBwB44IEHcODAATzwwAPodDr42te+hrvv5k4gmo663Yt9TPpeVSwN9TZLN/PO9nxO3JwiS1fgxNCjF1w5hWjQg3zotVN/baI0EZoB7Q3fA/V1/x69f/i/0H3iDxB27bjDIsqcHa1X+OAHP4hHHnkE586dwwc+8AGsrKzg4YcfnnRsRACAhu2jZKZzkEXZ0tBwfERRFHcoFKO222eiN0VmIZ71Cv2n/xrK8ftTWX1AFAd54TgK7/xpIAzgPfow+s88gSgK4w6LKDN2lOidPn0av/7rv47HH38cP/ADP4CPfOQjuHr16qRjIwIA1NtdlFI6sbCgylBkAac7iDsUilHb63NZ+hSZugq3O5jqA5agfhlhcxXywZO3/mIiukYoGtRv+XcovOV/x+CZJ9D5f38Dweb5uMMiyoQdJXpRFEGSJDz55JN461vfCgDo9ViORtNRa/dQSuHEzW3bu/Qov+wOp25Ok6pIkCUJXT+Y2mv2v/HXkI/dCyHJU3tNoiyRyovQ3vajkA/eje7nfhvdv/uvCN163GERpdqOEr0jR47goYcewsrKCh544AH84i/+Ik6c4Fhcmo56u4eyld7TkLLJPr28czoDTt2cMsuYXvlm6DUxvPSvUI68YSqvR5RVQggoR16Pwrt+FpAUeI/9GnpfeRRRz407NKJU2tGdx0c+8hE8/vjjeNOb3gRVVXH//ffj+7//+ycdGxEAoOH4qdyht6304tJ0yqcoipjoxcDSVdheH8uz5sRfq3/6byEf/BYIzZj4axHlgVA0qCfeDuXI6zE4+2W4/+2XoZ78dmhv+B5I3E9JtGM7uvMwTRP3338/2u02vvnNb+L1r389zp8/j9e+lpPFaLLCKELL9VNdulk2VGy1uEsvr3r9AJIANIUlfdNk6grsKUzejAY+Bs99AYW3/ejEX4sob4RRhnbPdyO8860YnvsqvD97P5TbH4D22u+EPHck7vAAAFEwQOTUETpbiPodIBgiCocAAKm0AKm8CFGchRA7KqIjGqsdJXqf/OQn8Ud/9EeYm5u79jEhBP7u7/5uYoERAYDj9VFQZahKet8gK0UNZ6624w6DYmJ3+rBSOkwozUxdQXsKid7ghX+CPHMIUnF24q9FlFeSUYb2un+P6M63YHjlFLqf/ThEcRbat3wHlGP3QRSsiccQRSGi9gaC2iUE9UsIa5cQNlcR9RwIowJhViE0HRAyIElAFCHq2oi8BqJ+B/LC7VBPfvtoMq9amHi8RMAOE73PfOYz+Ju/+RssLS1NOh6il6nbPiop3aG3jT16+eZ4Aw5iiYFZUNB2/Ym/zuCZJ6CcePvEX4eIAKGXoN71rVDueCvCzXMYPP9P6D35KcgLx6Acvx/yba+FVFne9+lZFA4RttYR1i8jqF1CuHUeQf0KhGpAqixBlBYgHzgJ5cQ7IPQyhHTz14uGfYRbF9B/9gvoffFTUI6/GYUHfpBlqDRxO7r7OHDgwJ6TPNd18cM//MP4/d//fdx2220v+9zv/u7v4i/+4i9QLo8u9B/6oR/Cj/3Yj+3pdSibGnYPpZQnepWihoY92qXH/Vr5w9UK8TAL6sRP9ILGVUQ9B9Lc0Ym+DhG9nJAkyMt3QV6+a5RE1S5ieOUU+k9/FlG/C3n+KKTF20elk9YshDUzOvUT0ui0DQIYdBH1XES+h7DTQtjeQNheR9TeQGhvQhiVUdllaR7y0Xuhvv579tyHKxQN8oETkA+cQNRzMLzwFDp//gHo3/4zUI6+cbx/OUQvsaNE721vext+67d+C9/5nd8JXdevffxWPXpPP/00PvCBD+DixYvX/fzp06fxiU98Avfee+/OI6Zcqdu91O7Q21ZQR71ZHX8Iizf8uWN3uCw9DpauYKPZmehrDM58EfLBu/kAhyhGQtEgL78G8vJrAACR30HYWkPY3sCwtTZK5nouMOgCUQRE4ejBq1KA0ExA0yE0E8KsQJo5BOm210EU5yCUyTxkFnoJ6t3vgrR4B3r/+MeQL70B+tt+lOWcNBE7uvv4y7/8SwDA5z73uWsf20mP3qOPPooPfehD+OVf/uXrfv706dP4gz/4A1y9ehVvfvOb8f73vx+FAi90+je1djfVg1iA0c9Ktaih1urBWmailzdt1+fEzRhYxmRP9KIowvDsl6Dd979M7DWIaPdEwYS8dAfkpTviDuWm5LnDkN7xUxic/lt0/uojML/vPzPZo7Hb0d3HE088sadv/uEPf/iGn/M8D3fffTfe97734ejRo/iVX/kV/N7v/R7e+9737vj7z80V9xQXjSwslOIO4Zac7hB33FZFtTr5EemTNFc1MIBI7N95UuPKAj+IMDdjpuIaTkOMOxVKErzu5Yld270rz6Gnqpg5ciyTJ3pZuhZof3gtTJKJ6F0/iPaX/weCf/ivWP6h/wwhJ/PBIO8T0mlHV5Pnefj4xz+Oc+fO4ZOf/CQ+8YlP4P3vfz8sa+9TjizLwiOPPHLt33/mZ34GDz/88K4SvXrdRRhGe44hzxYWStjacuIO45bWah5O3FZBqzXZEqxJ01UZ5680cedy8h5OpOVaSKuNmoejS8XEX8PVqpn4GHdjOAzQcvvY3LQnkoh1v/Y4sHQC7Xb2Vqdk7VqgveO1MB3Rie9A/6n/jit/8Uno73oocQ+PeJ+QXJIkbnrwtaOxRL/5m7+JUqmEer2OQqEA13XxwQ9+cF+Bra6u4rHHHrv271EUQVGS+RSD4tN00r1Db1vZVFHjLr1c4jCWeGiKDEka7TEctygcYnj+a5AP3T32701E+SMkGdq934ewdhH+Vx+79R8g2qEdJXrPPvss3vve90JRFBiGgY997GN49tln9/XCuq7jox/9KK5cuYIoivDpT38a3/Vd37Wv70nZ0h8E6PWHmRhNX7E0Lk3PKafTz8Q1nEZFQ53I0vTgymlI1iwkszr2701E+SQUDdr9/xuGZ7+I4aWvxx0OZcSOEj3pFftBgiB41cd26qGHHsKpU6cwOzuL3/iN38DP/dzP4d3vfjeiKMJP//RP7+l7UjY1HB9lS0tcCcNelC0NdWfyO70oeUaJHk/04mDpkxnIMnjhScgHT479+xJRvomCCfWe70bvn/4U0YD3DLR/O3rM/OY3vxkf/ehH0ev18I//+I/41Kc+hbe85S07fpGXDnN5aV/egw8+iAcffHAX4VKe1O0eyhko2wSAkqmhxUQvd/xBgCCMoKn7W95Le2PqythP9KJ+F8OVU9D/3UNj/b5ERAAgzx9DMHMA/lP/Hfpb/2Pc4VDK7eju45d+6ZdgmiZKpRJ+53d+BydPnrzhygSicWm0s5PoWbqCrj9EfzD+fiFKLtvro2iomTiVTiNTV8Z+ojdcOQVp5tBo/xYR0QSoJ9+FwfP/E0HjatyhUMrdMtF7/PHH8eM//uP4wz/8Q6ysrKBUKuG+++7jvjuauLrdQ9HMRsmbEAJlS0ODp3q5Ynss24yTWVBge+P9mRte+lfIC8fH+j2JiF5K6EWod30rev/4fyOKOF2e9u6mpZt//dd/jd/+7d/Gz//8z+PkyZMQQuDUqVP48Ic/DN/38d3f/d3TipNyqNbuoZSRRA8YDWRp2D0sz/IkIC9sr89l6TEyCypa7vhO9KIoRHDlFLS3/ejYvicR0fXIR9+IYOWbGL7wT1BPvCPucCilbnoH8id/8if44z/+Yxw8ePDax+644w684Q1vwMMPP8xEjyaq3u7h0PzedzUmTclUUbd7cYdBU9TuMNGLk6Ur2GiObwdYWLsEqDoki9M2iWiyhJCgvvY74X/tL6Hc9TYIib9LaPduWrrped7Lkrxtx48fh++zBI0mq+H0ULay0aMHjEa9N5jo5Yrt9WEW+Ms5LpYx3qmbw8tPs2yTiKZGmjkIYZQxPP/PcYdCKXXTRE+W5Rt+jjXDNElRFKHp9DNVulkyNdTaTPTypOX4TPRiZOkKnHEmepe+DmmRiR4RTY9y/E3oP/1Z3nfTnnDmNyWS0xlAUyRoyo0fNqRN2VRRZ6KXK22vD8vIzsOKtDF1BXZnMJYbpLBrI2xvQJo9PIbIiIh2Rlq6E5HvIVh/Ie5QKIVu+qj5+eefx3333feqj0dRhH5//EtoibbV7R4qxeyUbQLg1M0c4jCWeGmKDEkCev0Axj5PVoMrpyDPH4OQsvPwiYiSTwgB5djoVE85cCLucChlbvqb7/HHH59WHEQv07B7KGVkh962kqmh6fiIooh71XLC7nC9QtyKhgrb6+870Rte+jqkhWPjCYqIaBfkw6/D4IknEbbXIVWW4w6HUuSmv/kOHTo0rTiIXqZu+5nqzwOAgipDlgS83hBFlvPlgtMZwOKJXqwsfTSQZWkfa02iMMDw6jPQ3/lT4wuMiGiHhKxCOfIG+N/4HIx3/FTc4VCKsEePEqne7qKUwWRoe5ceZd9gGGAwDFFQWeoXJ1NXYO9zIEuwcRbCKEPopTFFRUS0O8rRezE8+xVEPTfuUChFmOhRItUzWLoJACVL4y69nGh7fRQNlWW6MTN1Zd8rFoaXn4a8ePuYIiIi2j2hFyEv3Y7BuS/HHQqlCBM9SqSG7aOcsdJNACgZKho2B7Lkge2xbDMJzIIC29vfz1xw+RuQuD+PiGImHziJwVkmerRzTPQokZqOn80TPUPliV5O2F4fZgbLj9PGLKhouXs/0Yt8D6GzCal6YIxRERHtnrRwHGFzFaHbiDsUSgkmepQ4QRjC7Q4yuX+sZGrcpZcTdqfPZekJYO2zdDNYPwNp5hDXKhBR7IQkQ16+C4PzX407FEoJJnqUOG13tHtMlrLX21Rmj15utD0meklgGeq+Er3h6nOQZjiBmoiSQT5wAsOzX4o7DEoJJnqUOA3HRzmDZZsAUDJVTt3MibbrM9FLAEtX4OznRG/tWUizh8cYERHR3klzRxE6NYT2VtyhUAow0aPEaTo+SlZGEz1DhdMZIAjDuEOhCds+maZ4mboCpztAFEW7/rPRoIewuQpphv15RJQMQpIgL5/A4NxX4g6FUoCJHiVOw+6hmNEbZFmWRuPe9zEcgtKh7fmw9Oz1maaNpsgQAuj1g13/2WDjLKTqAQiZ/x+JKDlG5Zucvkm3xkSPEqdh91DM4CCWbaOl6VyxkHW2N4BlZPOBRdoUDXVPS9OHq89Bmr1tAhEREe2dNHcbom4bQWs17lAo4ZjoUeLU7WyuVthWMjmQJQ+cbp8neglh6XsbyBKsPstEj4gSRwgJ0oETGJ5l+SbdHBM9SpyG3UMpg8vSt3EgS/YNhiH6gxC6xpH8SWDqyq5P9KJhH2HjCiduElEiyQdOYMg1C3QLTPQocVputk/0ioaKGnfpZZrTGQ1iESJ7K0LSyNzDLr1g8zyk0gKEkt33IiJKL2nmIMJOC6HXjDsUSjAmepQoQRjC6Qwy3aNXZulm5rW9fqav4bQxCwpsb3d9scEa+/OIKLmEkCDPH0Nw9Zm4Q6EEY6JHiZLlZenbypbG0s2Msz325yWJWVDR2uWkW/bnEVHSSXNHMLzyjbjDoARjokeJ0szwsvRtJVNFw+HUzSyzvT6XpSeItcvSzSgYIti6yESPiBJNWhid6EURd/PS9THRo0RpOH6mB7EAozKy/iCAP9j9Xi9KB7vTh1HgIJaksIzdTd0MaxchirMQqj7BqIiI9kcyq4CiIWysxB0KJRQTPUqUpt2DlfHeJiEEyzczruX6MFm6mRiWrsDZRaIXbJyDVD04wYiIiMZDmj+G4ZXTcYdBCcVEjxKlbvdQyniiB3BpetZt95pSMli6CrszQBRFO/r6YOMspOryhKMiIto/ef4IgpVTcYdBCcVEjxIl68vSt3FperaNhrEw0UsKVZEgSwK9/s7KpYOt85CqByYcFRHR/klzRxFsnkc03N3AKcoHJnqUKE0n28vStxUNLk3PsjanbiZO0VDRcm99ih72HES+B2HNTiEqIqL9EWoBUnkRwfoLcYdCCcREjxKl6eTkRM9QeaKXYU5nwBO9hLEMBfYO+vTCzQuQqge47J6IUkOaP8o+PbouJnqUGNeWpefgBrlkquzRy6hhEKI3CKBr2b+O06So72zyZrB5DlKF/XlElB7S/FH26dF1MdGjxGi7fRgFBbKc/cuyZHLqZlZt9+dJEk+EksTQlR0tTQ82z3EQCxGlilQ9iNCtI+y04g6FEib7d9SUGk3HR8XKftkmAJRNDS3X3/EUQEqPttdHMQeTY9PGKii37NGLogjB1gUOYiGiVBGSBHn+KILVZ+MOhRKGiR4lRtPxUczBIBYA0FQJEYCuz6XpWdP2+pnfBZlGlqGifatEz9mCkGQIvTSlqIiIxkNUDyJYPxN3GJQwTPQoMRp2LzcnIUKI0S49h+WbWWN7fVgF9ucljaWrtyzdDDbPc1E6EaWSNMNEj16NiR4lRt32c5PoAaPyTQ5kyZ6263NZegJZhoK2d/Oft2DzPERlaUoRERGNj1RZRtheRzTgfQX9GyZ6lBh1u4dSjhK9oqGiyRO9zGm6Pkzu0EscS1dhe4Obfk2weZb9eUSUSkJWIFWWEGydjzsUShAmepQYTaeHck6GsQBcmp5VbbfPHXoJZBYUdPwhgjC87uejcIiwscLVCkSUWqJ6AMHG2bjDoARhokeJ0XT8XJ3olUwNtTZLLLKm7fVh8UQvcSRJwCoocDrXP9ULG1chjAqEWphyZERE4yFzIAu9AhM9SoQwjEbL0nOV6PFEL4tsrw/L4IleEo0mb15/IAvXKhBR2kkzBxFsnuPqJrqGiR4lQtvLz7L0bSVTRdPhiV7W2DzRS6ybDWQJNs5C4iAWIkoxYZQhJAWRvRl3KJQQ+bmrpkRr2PnqzwNGpZtNLk3PlF5/iAiApvCtNYks/SYnepvneaJHRKknzR5inx5dw7sRSoS89ecBQEGVIUsCXm8Ydyg0Jm2vj6KhQggRdyh0HWZBQct7daIXDfuInE2I0kIMURERjY9UWcZw/YW4w6CEYKJHidBwfFg5S/SA0S49lm9mR9vt56rPNG0sXUHrOj9vYXMVojgHIbO3kojSTZo5hJAnevQiJnqUCM2c7dDbVrI4kCVLRv15TBaSajSM5dWJXlC/BKm8GENERETjJSpLCO1NRP1u3KFQAjDRo0So2z0UzRwmeoaGBk/0MqPt9WEy0UssS1evW7oZ1i5BYtkmEWWAkGRI1QMIti7EHQolABM9SoSG46OUw0SPS9OzpeX6MAv5u47TwjIU2NdJ9ILaJQie6BFRRkjVA9ynRwCY6FFCtFwfJSNfUzeB0YqFepuJXla0XJ+lmwlm6eqrEr0oChE2Vli6SUSZIVUPINhgokdM9CgBwijK7RALLk3Plrbbz+VQobTQFAkRRmswtkX2FoRmQGhGfIEREY2RNHMIwdYFrm8iJnoUP7czQEGVoeZw91jJZI9elrQ5jCXRhBAoGi/fpRfUL/M0j4gyRehFCCEQeY24Q6GY5e/OmhKn6fgoWfk8BSmZKlpun0/dMmI0dTOf13JaFA0V7ZeUbwa1S9yfR0SZIyrLCGuX4w6DYsZEj2LXdHyUzPz15wGApshQFQG3O4g7FNqnMIrgdgecuplw1isSvbB2EVKFJ3pElC1SaQFB/VLcYVDMmOhR7JpOD8Ucn4KULQ0Nm+Wbaed1B9BUGYrMt9UkMwvKy3bphfUrEOWlGCMiIho/UV5AsHUx7jAoZrwjodg1HB+Wkd9TkLKpoeFwIEva5XWgUNqYBQWtFxO9sGsjGvoQRjnmqIiIxksqLyGss3Qz75joUewadi+XqxW2FQ0VTQ5kSb2210cxxw8s0qJojPpiASCsX4ZUWYYQIuaoiIjGS1gziHwPke/FHQrFiIkexa5h53NZ+raioaLOFQup1/Z8mDkuQU4LS3/JiV79MkSZg1iIKHuEEJAqSwh4qpdrTPQodqNhLPm9QebS9Gxoe32YBZ7oJZ1l/NvS9GDrIiRO3CSijBLlBYQ1DmTJMyZ6FKsoitBy/VyXbpYMDmPJgpbT58TNFLB05drUzaB+GVKFg1iIKJukEgey5B0TPYpV1w8AAWhqfi/FksUevSxouX6up8emhamr8HpDBP0eIrcOUZyLOyQiookYlW7yRC/P8nt3TYnQdHoom1quhyGUDA1tz0fIpemp1vb6sHiil3iyJGBoMty1SxClOQhJjjskIqKJEMV5RM4WomH/1l9MmcREj2LVdPO7LH2bqkgoqDKcDpemp5nt9WFxvUIqFA0V3fULkEpclE5E2SVkBaI4j7B5Ne5QKCZ8/Eyxato+Srw5RsnU0LB7qFj5TnrTzOaJXmpYhoqgfgWizLJNov2IolFlzoVVB5utLqIownZtykypgNsPlLE0ayLHRTuxk8qLCGqXIC8cjzsUigHvSihWzZwvS99WMkd9escPxB0J7cVgGMIfBDA4dTMVLF2B1L4K6dADcYdClEotx8dXntnA+TUbQRjhwKyJmZIOIYDtnK7W6uL0+QZ6/SGOLZfwwN1LWJ4zY407j6TSPILaxbjDoJjwroRiVbd7KPJED0WTA1nSzOn0YRlKrntN08TUZBTq61ytQLRLXT/Ak6dW8c2LTdx1qIJ33HMQZUvFzY7sOt0BrtY8PPYP53B0qYR3vfEgSqxemRpRWUJw9stxh0ExYaJHsWo4Pk4crsYdRuxKOpemp1nb63PiZorMaINRzVnBijsUolSIIuCfn9vEl765jsOLJXzPW45A13Z2C2kaKu46XMWxAyU8d7mFP/rsc3jTaxbwrfcsQ5L4cGzSpPISwuZVRGEIIXE0R94w0aNYNR2e6AGjHr3NVjfuMGiP2i4HsaTJIhpoiwrKPIEluqXBMMRnv3QJNbuL77jvNpT3eBqnKjLuuX0Odx4s46vPbWK14eF/fftxFFROvp0koRYgChYiewOiyv6QvGFqT7FquX2UTN4gl0wVDYcnemnV9nwuS0+R2aiGVlSMOwyixGu7Pj79Ny+gPwzwHffuPcl7KUNX8Y7XH4QqS/jU37wA2+Po/0mTyksIatynl0dM9Cg2/UEAvx/A5AALlEyNPXop1vb6vI5TpNLfQm3Isk2im1mvd/B7jz2Ng/Mm3nL3EmR5fLeMkiTwptcs4MhiEX/6+eexXu+M7XvTq4nSPILGlbjDoBgw0aPYbO/Q4wCL0Yle2+1zaXpKtRwfFnv0UsPqbWDNN8AfN6Lr22p18edfOIe3vu4ATh6dvemwlT0TAieOzOC+1yzgsS+cQ4N96hMjFecQNlbiDoNiwESPYtNy/NG0LoIiS9A1GQ5LWFKp5fZZupkWUQi1s4lmVEJ/EMQdDVHitN0+/vzvz+LeO+dw9EB54q93aKGIe26fxaN/fw5ebzjx18sjUZpH2FyNOwyKARM9ik3D8VE0OGJ5W9nS0GD5Ziq1PJ9DhVJC7jYRyQXIBQNudxB3OESJ0ukN8WdPnMVrDldxZHnySd624wcrOLxYxGNfOIfBMJza6+aFsGYQdZqIhnyYnDdM9Cg2TcdHkcvSrymZGho2E700art9JnopobprGBpzMAsKEz2il+gPAjz6xFkcWrDwmsMzU3/9e47PwizI+B9PXkDEXG+shCRDWLMIW2txh0JTxkSPYtOwe9w99hJFQ0WTkzdTJ4oi2F6fPXopobjrCIwZ6AWZiR7Ri6II+PxXL6NoKLjn+Gw8QQiBN59YhNsZ4slTTEjGTSrNI2xejTsMmjImehSbhu1ztcJLFA2FS9NTyOsNoSoSVIVvp2mgOlcxNGahazI8JnpEAIDT5+tYrXXwphOLkxm8skOSLOGtr13Cv5ytYa3GSZzjJKxZBEz0cod3JhSbhtNDyWSP3raSqaHeZqKXNi2XDyzSRHXWEBhzMDQFdof9KkQNu4cnvn4V3/q65bGuUNgro6Dgvrvm8VdfvMh+vTGSSvOcvJlD8f9EU2612Nf0MqOl6ezRS5uWy6FCqREGULp1DI0ZGJoCp8MJf5RvwyDEZ/7pAu65fQ6VYiHucK45slRCtajhC1/nCdS4iOIcJ2/mEBM9isUwCOF1B7CY6F1T5tL0VGq7fVhcrZAKSmcLgVYGJAVGQYHb5Yke5dvf/8tV6JqCOw9Ob8LmTt33mgU8f6WFi2tO3KFkAidv5hMTPYqF7Y32jskSl6VvKxoqbI9L09Om5fpM9FJCddcwNEeDJvSCzJ1dlGtXNlw8f6WFN5+Mty/vRjRVxgMnF/HZL1+E3+fOy/0aTd6c4+TNnGGiR7FoOj7K7M97GUWWoBcU2FyanipNx+fEzZRQnFUEehUAYLw4jIXPVSiPgjDC5792GffeOQ9NleMO54aW5ywszpj44un1uEODEv/zAAAgAElEQVTJBKk0x8mbOcNEj2LRdHwUOcDiVSqmyl16KdNyfJYgp4TqrGJozAEAFEWGJAv4A54UUP587bkN6KqMw4vFuEO5pXtun8M3ztfRYmvDvnHyZv4w0aNYNByfg1iuo2hq3KWXMk0OFUoN1V1H8GKiBwCmxqXplD9tr4+vPLOJ+16zkMiSzVcyCgpOHK7i7zmYZd84eTN/mOhRLJpcln5dJZ7opU7b40OLVAj6kH0bgV659qHRQBYmepQvf/vPV/Ca2yoopqh94sThKtbqHq5suHGHkmqcvJk/TPQoFnW7x9LN6yjqKhpcmp4aURTB9vqwDA5jSTrV28JQrwLi337tGQUZboeJHuXH2attbLW6OHlkJu5QdkWWJbz+9nn87VMriLhab884eTN/mOhRLJoOl0xfT8lUUWOilxodfwhZlqApyR1mQCOKu45Af/nNbUGT4fWY6FE+BGGEJ/55BffdtZCIxei7dWSpCCDC6Qv1uENJLU7ezJ/0/aRTJjRdHyUumX6VEnfppUrL8VFi2WYqjBK96ss+NlqazifblA+nztVh6AqW56y4Q9kbIXDvnfP4n0+vYhjwWG+vpNI8J2/mCBM9mrowitDmAIvrKpkqE70UaXm8jtNCddcwNGZf9rFRoscTPcq+4TDEk6fW8Lrjs7f+4gSbrRiYKRVw6hxP9fZKWDOcvJkjTPRo6tzOAAVVhqrw8nul0vbS9JDLvdKgxemxqaG6GwiMl5duchgL5cW/nNnCTLmAuYoRdyj7dvfRGXz5mQ0E/D25J5y8mS+806apazo+ShZvjq9HliUYBQVtLk1PhbbXh6lzEEvihUPIfgtB4RWlmwUZLnv0KOP8QYCvPLOJe47P3fqLU2CuYqBoqHjmQiPuUFJJlOY5eTNHmOjR1I0GsbA/70bKloYGd+mlQtPpweKakMRTvC0EhTIgvXxojl6Q0ekOEfFggDLsa89uYnnWQKVYiDuUsbn76Ay+eHqdEzj3QJhVTt7MESZ6NHVNhzv0bqZkqGhyl14qNB326KWB6q1jqL+6N0mRZciyQK8fxBAV0eR1e0M89fwWXnss3b15r7RYNVBQZTx7mad6uyUkGcKcQWhvxR0KTQETPZq6huOjyL1jN1Q0VTQ4kCUVWq7PHXopoLgbr5q4uc3QFHhdPtmmbPrqsxs4slRM1XL0HRECdx+dwZdOr/NEfg8kaxZhmysW8oCJHk1dw+6hyNUKN1Q0uDQ9LTg9Nh1Ud+1Vg1i2mboCtzucckREk+cPAvzruTpOHLn+Q460OzBnAgDOrLRjjiR9hFVF2F6POwyaAiZ6NHUNLku/qZKhot5mopd0URTB7vRZhpwCiruO4Q0SPV2TOXmTMunpszUsz5jZfbAqBE4encVXnmHCslvCmuFAlpxgokdT12Sid1McxpIOXX8ISQhoqnzrL6b4hAGUbgOBfrNEj6WblC1BGOGfn9vM7GnettvmLdheHxuNbtyhpIqwZhG2mCDnARM9mrq266OU1SeMY1AyuDQ9DVpuH0U+sEg8pVtHoBUB6fq9lDqXplMGPXuxiZKpYbasxx3KRAlJ4I5DFTz1wmbcoaSKVJxFaG/EHQZNARM9mqquP0QEQFN56d1I0dTgdAZcmp5wLddn2WYKKO76DU/zAC5Np+yJIuArz6zjxOFsn+Ztu+NgGWeutND1OT13xzQTCANEPTfuSGjCeLdNU9VwfJRNDUKIuENJLFkSMHUFLZenekk2GsTCiZtJx0SP8ubCmo0QwPKsGXcoU1HQFByct3DqXC3uUFJDCAGpOMeBLDnARI+mqun0uCx9B8qmhgZ36SVay/Nh8kQv8VR37YaDWADA4DAWypgvf3MdJw9XgRw9UL3rUAVPndniAvVdENYME70cYKJHU9V0fJQ4jv6WShzIknhN24el80Qv6VR345Ynel5vyF1clAnrjQ6aro/Di6W4Q5mq2YoBTZFxbpWrFnZKmDMIWtyll3VM9Giqmg4XTO/EaJceT/SSrOn63KGXdFEIpbN1wx16ACDLEhRZoNdnfw+l37+8sIU7D1YgSfk5zdt256Eynnp+K+4wUkMUZxA2mehlHRM9mqp6u8eb4x0oGSrqNsdFJ1mLiV7iyd0GQtVEJN+8XNwsKFyxQKnX6wc4c6WF4wfLcYcSiyOLJWw0O3xIukOSNYuwzUQv65jo0VSNduixR+9WyqaKepu/rJKs7fZhMdFLNNXdwFCfveXXcSALZcHp83UcmLOga/msmpFlCccPlPGNsxzKshPCmkHkbCEK2diYZUz0aKqaDk9BdqJkamjY7NFLqiiK0Pb6vJYTTvHWEeiVW36doclwuUuPUiyKgK+fqeGOg7e+3rPs2HIJpy82uJ5oB4SiQWgWIq8edyg0QUz0aKqaro8Sl0zfUsnUuDQ9wXr9AAJAQZXjDoVuQnHWbtqft63AyZuUclc2XUQRsFDN9oL0W6kUCzALCi6tO3GHkgqiOIewxcmbWcZEj6ZmMAzQHwQwC/ksK9kNS1fQ8YcYDFlSkUQt10eRDywST/U2MbzJxM1tZkGF3WGPHqXXU89v4s5D5VytVLiRY8slfOMcT6l2QlhVrljIOCZ6NDWjsk0uS98JSRIomSqaXLGQSC23j5LBXtNEiyIo3iYC49Y9eqauwPGY6FE6ud0BLm84OLqcr5UKN3JksYQL6zYn6e6AZM4gbK3GHQZNEBM9mpqm46Ns8RRkp8oWl6YnVcvlmpCkk3wbkGREyq1L2QxNhtMdTiEqovF7+mwdR5ZKUBWWkgOApsk4MGvhuUvNuENJPFGcYelmxjHRo6nZPtGjnSkZKpemJ1Tb7cPS+dAiyVRvZxM3AcDQVfboUSpFIfD02Rpuz+lKhRs5ulxk+eYOCGuOpZsZx0SPpmaU6PEUZKe4ND256nYXls5rOckUbxOBXt3R1+qqDH8QYBhwUh+ly6UNBwVVwkwp30NYXunArIW21+f06lsQZhlRz0E0ZOl6VjHRo6lpOD0UeQqyYyVTQ73NX1JJ1LS5DzLpFHdtx4mekAQMTYbHUz1KmW+cr+HYMk/zXklIAseWizjFU72bEkKCsGYRtjfiDoUmhIkeTU29zdUKu1EyVdT5NDKRuCYk+VR3HcMdrFbYZuoKHE7epBTxBwHOr9o4ssQhLNdzbLmM0xcaiDi8+qYka4blmxnGRI+mpun4KPIUZMfKpsYevYRquVyWnnSj0s1dJHoF9ulRujx/uYWlGRMFjUNYrqdSLEDXZFzZcuMOJdGENYOwtRZ3GDQhTPRoapqujxJvjnesZGloOjxhSJowimB7TPSSTAy6EEMfoVbc8Z/RCzLsDhM9So9vnKvhGFcq3NRti0U8c7ERdxiJJkzu0ssyJno0FcMghNcd8OZ4FwxNxjAI0etz7HuSOF4fuiZDkfn2mVTX9uftYmenocncpUep0XJ8NGwfB2bNuENJtMOLRZxZaSMMOWjpRoQ1wx69DOOdCk2F7fVhGQokicvSd0oIMSrf5OTNRBn157EEOclUb2PHg1i2mQWVJ3qUGqfPN3BkqQSJD5xuqmhoMHUFlzdYvnkjkjmDyN6MOwyaEL5D0FQ0bB9l3hzvWtniLr2kaTocxJJ0iru+60TP0BW4XZ7oUfJFEXDqYp1lmzt0eNHCs1yefmN6EdGgh2jAe40sYqJHU9FwejwF2YOSwRO9pGk6PpelJ5zqrmO4i0EsAGAUZA5joVRY2XShSBJmS4W4Q0mFwwslvLDSQsDyzesSQozKN3mql0lM9Ggqmo4Pi/15u2YZChe+JkzD8dlrmnCKt4FgF6sVAMDQFHi9ISLeC1LCfeN8HceWi7vqQc0zy1BRNlVcXnfiDiWxJCZ6mcVEj6ai3u6hqCtxh5E6XJqePE27x0QvycIhZN9GUKjs6o/JsgRNkdDp8VSPkmsYRDi70saRRZZt7sZtC0U8w/LNGxJGBWGbiV4WMdGjqWjYPZQtlm7uVplL0xOnYbNHL8kUbwtBoQxIu98tZukKHA5koQS7uGajWizAYPn4rhxZLOLsSpvlmzfAFQvZxUSPpqLhcIfeXpRMDQ2HPXpJMpq6yWs5qRRvY1eL0l/KKDDRo2R75mIDhxetuMNIHUNXUbE0XFyz4w4lkYRVRcREL5OY6NFUjCYV8kRvt8qWhqbjI2LjUGK0XS5LTzJ1DxM3txkFBQ4nb1JCDYYhzq/ZuG2xGHcoqXTbYhHPXmT55vUIawahsxV3GDQBE030XNfF937v92JlZeVVn3v22Wfxnve8Bw8++CB+9Vd/FcMhl0JnVRCGcLsDDmPZg4IqQ5IEvB5/PpKg6w8RRhEK6u7LAmk6FHdj1xM3txmaAqfDRI+S6fyqjbmyDl1jv/teHF4o4tyqzfLN6xB6GVHXQTTk+1/WTCzRe/rpp/EjP/IjuHjx4nU//773vQ8f/OAH8fnPfx5RFOHRRx+dVCgUs7bbh6krkLksfU8qpsbJmwnRdHyULQ2C0+4SS93DxM1tRkGG47F0k5LpmYsNHF7gad5eGbqCkqniyiaXp7+SkCQIs4LQqcUdCo3ZxBK9Rx99FB/60IewuLj4qs9dvXoVvV4Pb3zjGwEA73nPe/C5z31uUqFQzBoOl6XvR8lS2aeXEE2XvaaJFoWQOzUM91q6qSuweaJHCeQPAlzacFi2uU8H5y2cudKKO4xEEtYMInsj7jBozCaW6H34wx/G/ffff93PbW5uYmFh4dq/LywsYGODF1dWjfrzeHO8VyVDQ5MneonQ4g69RJO7TUSKDsh7e7BkFlS4XZZJU/KcW2ljsWpAY9n4vhyat3Bmpc19mdchzCp36WVQLIXeYRi+rPQpiqI9lULNzfHJ1n4sLExnD0//mU3MVU1Uq+ZUXi9r5mdMdIfRRP9/TetaSLt+uIa5qpHZaznt/11y5xxQnEexWNjTny8UFLi9PioVI/fluWm/FrLmhasXcOfhmT1f2/sRx2tOStHSoMgb6AxDHGIZ7Mt4cwuI/MYN7wd4n5BOsSR6y8vL2Nr6t+k+tVrtuiWet1KvuwjZVLsnCwslbG05U3mty6ttqBLQanWm8npZo0rA5bX2xP5/TfNaSLvLa22ospTJa7laNVP/31XcvIxQLcNz917qLITAxpYLXcvvyUkWroUs6fUDXFizcc/xWbj7uLb3olgsTP01J215zsDXn9mA9QYOnn+pQFgI1p+57v0A7xOSS5LETQ++YrnKDx06hEKhgKeeegoA8JnPfAbvfOc74wiFpqBu97haYR/KHMaSGE2Wbiaa4ux9tcI2q8DJm5QsZ660cGDGhKrk9+HDOB2aK+KFFfbpvZKwWLqZRVNN9B566CGcOnUKAPCxj30MH/nIR/Dud78bnU4HP/ETPzHNUGiKmk6PPXr7ULY01O1sPVFNK/abJpvqre95Wfo2o6DA5dJ0SpDnLjdxaIFL0sdlvqLD7Q7QdvlA56WEUUHUaSIK2aecJRMv3XziiSeu/fMjjzxy7Z9PnjyJxx57bNIvTwnAZen7UzJV2F4fYRhB4oqKWI2GsfBaTqQoguJtYrjH1QrbRkvTmehRMviDACtbHu67a+HWX0w7IiSBg/MWzq608aaT/HvdJmRltE/PbUCUd99ORcnEAmWaqDCM4HQGLHfbB0WWYBQUtDLWJ5E2wyBExx/C0rmsOImkvgtAIFKMfX0fQ5Nhe3zST8lwYdXGYlWHymmbY3Vo3sLzK824w0gcYVURtjkFP0uY6NFEtT0uSx+HalFDg+WbsWq7fViGwlPVhFK8DQyNWWCf0zINXWXpJiXG85dbODjP6ZDjtjxrYqPZRdcP4g4lUSSzipC79DKFiR5NVMPpcVn6GJRMDTW7G3cYuTZals5rOalUd2Pfg1gAwNRktDmMhRJgGIS4sG6zP28CZFnC0oyJC6vtuENJFrPCE72MYaJHE9W0fRQ5vGLfSqbKE72YcRBLsinu/iduAoChK3CZ6FECXFp3MFPUoWssF5+Eg7Mmnr/CRO+lJGuGiV7GMNGjiWpwHP1YlEwNtRZP9OLUdHxYvJYTSxnDxE0AMAsq3C6nzlH8nr/SwsF5Lq6flANzFi5vONzH/BLCnGHpZsYw0aOJatg9JnpjUDZV1NrcpRenpt1DUee1nFTqGCZuAkBBlTAMQgyH4RiiItqbMIxwdqWN2xbYnzcphq6gaChYrXlxh5IYwqwgcuuIQr7/ZQUTPZqoeruHEhO9fSubGupcmh6rhsMy5KQSwx7EoItQK43hmwmYugKHA1koRitbHixdYRXBhC3PmjjHPr1rhKJBqAaiDieSZgUTPZqoBnfojUXZ0tB02KMXp4bDhxZJpXibCIyZfU/c3GbpKhz26VGMXrjSxCFO25y45TkL567acYeRKMKaQWhvxh0GjQkTPZqoluujzFOQfdM1GWEYodNj71BcWk6fJ3oJpbgbY+nP22YWZLQ9nuhRPKIIeOFKm9M2p2C+rMPpDLhS5SWEWUFkb8UdBo0JEz2amDCMYHt99uiNgRAClaKGBss3YxFFEdpen+sVEkr11hHolbF9P6OgwO7wBJ3isdHsQJIEKhbfbyZNSALLcybOr/FUb5swKgh4opcZTPRoYtpeH0ZBgSzzMhuHssU+vbh4vSFURUBVeC0nkeKsYzjOEz1dRdtj6SbF4+xKezRtc0ylyHRzy7MGzl5ln9620YkeE72s4F0LTUzT8VHmE8mxKRlM9OLSsHvsNU0w1dsY9eiNiaUrsF0mehSPMyttHJxj2ea0bK9ZCLhmAQAgzCp79DKEiR5NTJPDK8aqxBULsWnwoUVyhUPIvRaCwv6XpW8zCyps9uxQDNzOALbXx0LFiDuU3NA1BSVTw9UtrlkAAMmsInRqcYdBY8JEjyamYXPB9DiVTQ11JnqxaNh8aJFUSmcLQaECSPLYvqepy3C7fUR8wE9TdvZqGwfmTAiJZZvTtDxr4hzLN0cKFjD0EfW7cUdCY8BEjyaGN8fjVbY0nujFpNbucahQQinueMs2AUBRZMiShJ7PKbc0XWdWWizbjMHBOe7T2yaEGK1Y4KleJjDRo4mp2T2UOI5+bEa79JjoxaHe7qHMHr1EUt2N0YnemFmGgjZ36dEUDYYhVrY8LM+ZcYeSO7MlHV5vyCFMLxqVb7JPLwuY6NHENOwe+5rGqGiocDoDDIMw7lByp85rObEUdx3DMZ/oAaPJmzZ36dEUXVp3MFfWoanjK0OmnRGSwME5Exe4ZmGEkzczg4keTUzD9nkKMkayJFA0VLQc7veatqbj83Q6oVRvHYExO/bva2gKHD7dpyk6uzLqz6N4LFYNXFhlogcAklFB2GailwVM9GgihkEItztgX9OYcZfe9IVhBNvrs980iaIQSqeGoT6+iZvbTF1mGRdNTRQBZ1fbODjP/ry4LM9auLzhImLRzIsrFjbiDoPGgIkeTUTL9VE0VEicHDZWZVNFw+aJ3jS1XB+mrkCW+XaZNHKnjlC1AHn8lQNWQUXb488aTcdGowNVlrivM0aGrsAoKFhvdOIOJXbCrHAYS0bwzoUmomGz1G0SiqbKE70pa9g+KuzPSyTVW8dwAmWbAGAaCnv0aGrOrrRxYJ5lm3FbmjVwcZ3lm8KsIHIbiEIeb6YdEz2aCA5imYyyqWGrxd0201S3e3zKnlCKu4FgAmWbAGAVFDhdlm7SdJy5yrUKSbA8Y+A8+/QgZBWiYCLqNOMOhfaJiR5NRMPx2Z83AWWTPXrT1nC4JiSpVGcVQ338EzcBQNcU9PoBhgG3ptNkud0BbK+PhYoRdyi5tzBjYKPVgT8I4g4ldqM+va24w6B9YqJHE1FrdXkKMgFlS2OP3pTVWlyWnlSquzGRiZvAaNy6WVDgcpceTdiFVRvLsxYEe9pjp8gy5ssGrmy6cYcSO2FWEDlM9NKOiR5NRM3uocxTkLEbJXo9RBFPGaalbnNZeiJFIeTOFoIJ7NDbZukq7A779Giyzl5tY3mWp3lJsThj4CL36UEYFQRtTt5MOyZ6NBFN2+eJ3gQUVBmSJOD1hnGHkhvsN00mudtApBiIJjBxc5uly7C5YoEmKAwjXN5wsMz+vMRYnjVxYc2JO4zYCbPKpekZwESPJqLh+DzRm5DKi6d6NB0NLktPJNXdwHCCp3kAYBQUJno0Uas1D0VDhVFQ4g6FXjRbKqDTG8LJ+c++MCsImeilHhM9Grtef4jBMOAvrgkpWxrqbSZ60+APAvQHAUxey4mjeOsTLdsEAENXuTSdJurcahvLs1yrkChCYHnOxMX1fJ/qSWaVu/QygIkejV3D9lEpFiAEG8snoWRqqPFEbyoadg8Vi9dyEinOGoIJTdzcZvFEjybs3FWbZZsJtFQ1cCHvfXoFCxj6iPpc6ZRmTPRo7BoOh1dMUtlUUeMuvalo2D7KFss2k0h1J7csfZupK2hz6iZNiNsZwOn0MV/W4w6FXmF5dnSil+e5Z0IICGuGp3opx0SPxq5hs6dpkiqWhq0WT/SmgcvSEyoKoXibEz/RM/XReoU83+zR5FxY41qFpDINFZoqYyvnD1Ulcwahwz69NGOiR2NXb3e5d2yCylYBtXa+f/lMS8PmDr0kknstRIqOSClM9HVURYYsCfR8Trml8eNahWRbmjFwOed9ejDLiLg0PdWY6NHY1dos3ZykSlFDnT16U1Fr93g6nUDKBBelv5JlcJcejV/AtQqJt1g1cGE93316klFByF16qcZEj8auztLNiTI0GUEYodPjzeek1fnQIpFUdx1DvTqV1zI5kIUmYHWLaxWSbnHGxNWahyDMb+22MKtcsZByTPRo7JoOF0xPkhAC1WIBNa5YmLgGr+VEUtzJT9zcZhRU2BzIQmPGtQrJV9BkFA0VG41O3KHERpgVhA5LN9OMiR6NVRRFaDp9nuhNWMXSmOhNGK/l5FLdtYlP3NxmFmTu0qOxO7/GtQppsFg1cCnHfXrCrCDyGoiiMO5QaI+Y6NFYOd0BVEWCpshxh5JpZSZ6E8drOaGiaDRxc8LL0rdZuoq2y0SPxsfrDWF7fcxxrULiLVQNXMhzoierEKqBwGnGHQrtERM9GqvRgmmWuk1a2dSw1cpvOck08FpOJrnXQiSpiJTp3CSbusLSTRqri2s2lmZMSFyrkHiLMwbWGx6GQX5PtIRZxbDNPr20YqJHY8UdetPBXXqTx2s5mRR3HYE5N7XXs3QFDhM9GqNzq20ssT8vFVRFxkyxgNVafh+sCrOCQZOTN9OKiR6N1WjBNG+OJ21UusldepPEZenJpLqrGE5pEAsAGAUFfj/AcJjfJ/o0PlEEXFp3uT8vRUZ9evldsyCMMgbN9bjDoD1iokdjVW/3UDJ4czxp1aKGhu0jivI79nnSRtcyH1okjWqvTm2HHgBACFiGyoEsNBabzS5URaDI35OpsThj4mKe+/TMCgYtJnppxUSPxqrOBdNTUVBlRAA6/jDuUDKL13IyjSZuTq90EwCKuoK250/1NSmbLqy1sTTDaZtpMl/RsdXqwh8EcYcSC2FUMWTpZmox0aOxqtvcOzYNo116Gmrs05sYXssJFAZQOltTW62wzTJUtDh5k8bg/KrNss2UkWUJ8xUDV7fcuEOJhbCqGLa4Sy+tmOjRWDUdH2X2NU1FxSqwT2+CGryWE0fp1BBoJUCe7kmrWVCY6NG+9QcB1psdLM0w0UubxaqBi+s5TfT0IoKug2jI98A0YqJHYzMMQrjdAYrsa5qKsqlyl96EDIYhPF7LiaO4a9Ptz3uRZahoOSzdpP25vOFivmxA4W7O1FmcMXAxpwNZhJCgFKuI3HrcodAeMNGjsWm5PoqGyt1AUzLapccTvUnYLtvktZwsqrOK4ZQWpb9UUVfRYo8e7dOFNRtLLNtMpdlSAS3XR6+fzz49uTiD0Gb5Zhox0aOx4d6x6aoUNWwy0ZuIWquLarEQdxj0CqpzdeqDWACgaKiwOXWT9unCmo1l7s9LJUmWsFAxsLKZz/JNuVhF6DDRSyMmejQ29TaHV0xT2dJQZ+nmRNTaPVR4LSeO6qwjiCHR01QJUYTcPs2n/Wu7ffiDEDN8gJRaC1UDlzfyuWZBtioI7c24w6A9YKJHY1PjsvSpqlgaGnaPu/QmYLPV5bWcMGLoQ+7bCPRKDC8uUDRVtDmQhfZodJpnAILl4Gm1WDVwKa+JXnGWiV5KMdGjsam1upxSOEW6pkAIAa/HXXrjttXs8kQvYRRvY7RWQcTza8vSVbRc9unR3pxfs7E0w7LNNJstFdDMaZ+eXKwiYo9eKjHRo7HZavHmeNqqxQIHskzAVruLCkusEkV11qa+P++lRkvTeaJHuxeFwJUNF0vsz0u1PPfpKcUZhG6NFUQpxESPxqZu++zRm7IK+/QmomH7fGiRMKqzikCf/sTNbaauounwZ412b63hwdAVGAUl7lBon/Lapyc0A0AE+F7codAuMdGjsQijaLQsnTfHU1W2NO7SG7Nefwh/EMDSeVOWJKq7Gssglm2WzqXptDcX1x0sc0l6JuS1T08IAWHOIHRqcYdCu8REj8bC8frQVAkaF8FOVdlUsdnqxB1GpmxP3BQcmpAoirOGoRlfolc0OIyF9ub8ahtLTPQyIc99epJZRehwIEvaMNGjsai1e9w7FoOyVcBWiyd64zS6lnkynSRS34UIA4SqFVsMlqHA6fTBFhXaDX8QYLPZxQITvUzIc5+eMMoIbZ7opQ0TPRqLut3jxM0YsEdv/GqtLkosQU6U0WnefKyj6RVZhqrK8LqD2GKg9Lmy6WK+YkCRWe2SFbnt0zMrCO2NuMOgXWKiR2NRa/dQtrh3bNoqRQ117tIbqy2uCUkc1V1DYMQ3iGVbyVDQ9rhigXbu4pqNRZ7mZUpu+/SMCiLu0ksdJno0FlutLkq8OZ66gipDkSU4HZ4yjMtms4sqT/QSRXWuxrpaYZuls0+PdufCmoNl7s/LlLz26QmrymEsKcREj8aCO/TiM7AWONMAACAASURBVFPUuEtvjGp2D2WL/aZJotpXMYxx4uY2S1fQ5NJ02iHH66PTG2KmxPeTLMlrn54wKoi8JqIwjDsU2gUmejQWDdtnuVtMqsUCNpnojU2dw1iSJQygeJuxTtzcZuoqWg5P9GhnLq47WJ41ISRO8M2aPPbpCVmBKJiIOs24Q6FdYKJH+xZFEep2jyd6MSlbGjabXLEwDl5vgCgCdI2DE5JC8TYRaiVAjv/9pWioaPFEj3boAvvzMmuhauDSRr5O9ABAmFWE7NNLFSZ6tG9udwBZEijw5jgW1WIBGw2e6I1DrTU6zeMOveRQnaujiZsJYBkK7A5P9OjWogi4tOFieZaJXhbNlgpoOj34eevTMyqI2KeXKkz0aN/qNnfoxala1Fi6OSa1dhcVXsuJotoriSjbBACzoMLrDRGEnHJLN7fV6kJRBCwj/pNoGj9ZljBfMXC1lq9TPWGUEfBEL1WY6NG+1Vo9lFm2GZtqscBhLGOy1eqhbHJNSJJo7SuJOdGTJAGzoMDxeKpHN3dxzea0zYybr+i4nLPyTWFVuWIhZZjo0b6NlqXz5jguJVNF1x/mroRkEjZbHT60SJIoguquJibRA17s02OiR7dwYc3BEhO9TBsNZMlZomdU2KOXMkz0aN+4Qy9eQgie6o1JrcWhQkki95qIJAWRmpwbZktXOJCFbmoYhFituxzEknHzFR21dheDQX7WDUhmlT16KcNEj/at1ubNcdxmSlyxMA6ja5k9ekmh2lcxNBfiDuNlTF1Fm4ke3cTqlodKsQBN5YCyLJNlCbMlHVfrXtyhTI9eRDToIhryPTAtmOjRvtXaXZa7xWy0YoGJ3n5cWxPCHXqJodoriViU/lJFQ0HTZukm3diFdRtLVZ7m5cFCVceVHO3TE0JAmDMIbZ7qpQUTPdq3hu3zRC9mVe7S2zenM4AiSyjwKXxiaHZyBrFsKxkqmm4v7jAowS6sOViaTU65MU1OLvv0zAoih316acFEj/al0xsgjCIumI5ZtVjABk/09mWr3cUMT/MSJUk79LYVTQ0t10fEDQt0HV0/QNPxMVfW4w6FpmCuomOj2cEwyE+f3mggy1bcYdAOMdGjfam1e6haBS6Yjlm1xGEs+1Vr9bhDL0Gkvgsx9BEWynGH8jKaKkOSJHR6g7hDoQS6vOFgsapDlnl7lQeqIqNSLGCtlp+KGmGWOXkzRfhORPtSb7OnKQkq1uiUIQjz81Rx3GrtLkpcE5IYo9O8BSCBD5HKpoqmw2EE9GoX1mwscq1CrixUdVzZzE/5pmRWmeilCBM92pea3ePNcQIosgRLV9GwefO5V5vNLntNE2Q0cTNZg1i2lQwmenR9l9YdLLM/L1cWKgYu5Wkgi1ll6WaKMNGjfalxh15icMXC/owSPZZuJoXaTt4glm2WoaHhcCALvVzb7aM/DPnAKGcWqgbWGx6CMB+Nu8KsIHLriNionApM9GhftrhgOjEqRQ1bHMiyZ5utLmZKTPSSQnNWEpvolQwV9TZP9OjlLq7bWJ41ElluTJOjqTJKpob1Rj769IRSABQVUdeOOxTaASZ6tC91m4leUlRMDRtcsbAng2EA2+vzWk4IMfQh91r4/9m7ryBJzute8P/MrMzKzMryts30uMZYeEuQAkUPipcQKXel2N2QVorQg/ZhIxixitjQw+5exl0Tqw1xVyFpr6SlPCVRFI1AgQRBA5AwMwBmgPEzbaZ9V5f3vtLsQ88MAY7r7qnqLzPr/N6GGHb/genpzpPf+c4x5DDrKLfkV0WU6USP/IzFdB0J2p83kmJBZaT26fFqGFad2jedgAo9ck+KtQ4C1LppCyG/F9kSnejtRK7SQUjzgufpTbwdiI2NzUXpvD3XtmiqiGqzRysWyA2WBSzn6H7eqIqHFKyM0EAWTg3SQBaHoEKP7Finp6Ovm1BlD+soBJu79GjFws7kyi2EA9S2aRdidWVz4qZNiR4BokdAo9VjHYXYRK7chiwKUGQaTjaK4iEZ6UIT1ogMvuaUAA1kcQgq9MiO5a+dgtAOPXsIaRIK1TZdkN6BbIkmbtqJVFmC7rNvoQdca99sUKFHNi1uVJGk07yRJUseKJJnZAai0Ymec1ChR3YsX2kjRDv0bEOWPOB5HvUWLXLermyphTAtS7cNqbqMvpZkHeOO/IpIkzfJDUsbdSTDdD9vlMVDCtZGpH2To116jkGFHtmxfKWNAJ2C2EqEVizsSKbUoombNsH1muB7DdsOYrnOp4go095KAkDXTaRLTSSo0BtpsZCM5dxoDGTh1BANY3EIKvTIjmXLLYSo0LMVWrGwM7lKGyE60bMFqboC3ZcEOHv/ePKrEoo1OtEjwFq+iYgmQ/TYc3gQ2R2J4OaJ3ijcnuBkP6xOHZahs45C7sLeP0mJreXKbQTp4dhWgj5asbBdfd1AvUWrFexCqi7behDLdX5FRLlOJ3oEWNqo0WkegaqI8Ag8SiPwAojjeXBKEFajwDoKuQsq9MiOFSoduqNnMyHNixyd6G1LvtJB0EerFexCqtj/fh6wOYyl1uyNzJQ9cnuLmRoNYiEAgERIxuqo3NPzhWjypgNQoUd2xDQtlOqbD8jEPkKaF1kq9LYlS6sV7MOyINZWoPsSrJPclSDw8EoCarRiYaS1OzoqzR6iAZl1FGIDsaCClRG5p8crQZh0T8/2qNAjO1JpdKF4PRA99CVkJ7RLb/tyZVqtYBdCuwRwPExJYx1lSwKqRO2bI245W0cypFBHAAEAxMMKVrOjcU8PSgBmlSZv2h09pZMdydPwClvSFA96uoFWh1YsbFWmSKsV7EKqLqPvS7GOsWWaIqJMKxZG2uJGHYkQ3c8jm/yKCNMCak33n/Rv7tLLso5B7oIKPbIjOdqhZ0scxyEakJEp0aneVmVKLXppYRNSdRm6L8Y6xpZpigclWrEwsiwLWMrUkIr4WEchdsFxSISUkbinx6shWHRHz/ao0CM7ki/TDj27ivi9yJSarGM4Rq7Sph16NiFWljdXKziEX5VoafoIq9S7MEwLAZ/IOgqxkVhQHol7epwahtkowBqJPlXnokKP7Ei23EaIBrHYUkjzYqNIKxa2glYr2IhpQKynHTGI5brN1k33t2iRW1vO1JAKqwBH9/PITyWu3dNzO06SAY6D1XX/v6uTUaFHdiRXaSNIrZu2FPZTobdVtFrBPsTGBkxvEJbgnO8rmiKi3urBNOmN9iha2KjT/jxyk6BPQqdnoNFy/1153heBVaOBLHZGhR7ZkWK1Q/eabCoSkKl1c4uy5RbCfucUFm4mVpfR15xzmgdsrlhQvAKqIzB4gbyfaVpYzdVpfx65GcchHlawlnf/SRenhmBSoWdrVOiRbWt3dXT7Bnyyh3UUcgthvxf5Sgcm9c3fVa7cRpBeWNiCVFmGrsZZx9g2P61YGEkbpRZUWYTipZ+D5GaxgBcr2RG4p0crFmyPCj2ybYVrp3kc3UuwJa8oQJYElGka4F1lijRx0y6kypKjBrFcpykSSlUayDJqFtM1JCPUtklubVQmb3JqEGY1wzoGuQMq9Mi25Wm1gu1trlige3p3ky3TDj074Lt1CN0adDXKOsq2BX0iClTojZzFjWuDWAi5hbBfRq3dR7trsI4yVJwaptZNm6NCj2xbvtKmKYU2F/Z7qdDbgmyZVivYgVReQN8/DnDO+5EUUCXkq7S3cpR0ewby1TbitCid3AbHc4gHZKy7/FSP84Vg1WmXnp0576cqYS5balGhZ3ObKxZoIMud9HWTVivYhLc0j76WYh1jRwKahFKtC7oSOzpWsw3EgwoEgR6hyO3FQgpW3F7oyX5YvRYsna6K2BV9lyLbtrlagU5B7CwSoBULd7N5Mk2rFezAW766eaLnQLIoAABaHfePUiebFjJVJGmtArmLeEhx/UAWjuOutW/SqZ5dUaFHti1f6dAdPZuL+OmO3t3kym1arWADXL8FoV105MRNAADHIahJKNI9vZGxtEFrFcjdRfxelOoddPtuv6dHKxbsjAo9si2maaFc31wyTewr6JNQb/XQc/kPmHuRKdHETTvwlhehaymAF1hH2bGgT0KhRoXeKKg2euj0DRriRO5KEHhEAwrSeXdfo+DUIC1NtzEq9Mi2VBpdKF4PRA996dgZz3MI+73IlWlIxO2s5xuIBGTWMUaeVHbu/bzr/IqIfIX+ro2Cpcy1aZu0XohsQSwkYyXn7vZNXgnCrGZZxyC3QU/rZFs2VyvQm0wnCFP75h2tF5qIUqHH3OYgFmfez7suoEkoVOhEbxQspOt0P49sWSKkYCXr8oEsvhAVejZGhR7Zlhzt0HOMsCZhgwq9W7IsC5lSC9EgFXoscXoXnkYWfc15i9LfK6h6UarT1Dm3s0xgJVtHiu7nkS2KBr3IV9rQdZN1lKHh1BBMWrFgW1TokW3JldsI0Dh6Rwj7ZWwU3H03YKeqzR54noPq9bCOMtKkyhJ0XwLgnf3noHgFGKaJVkdnHYUMUbbcguL1QJFF1lGIQ3gEAWG/F2kXT8Hm1BCsZhmW6d5i1smo0CPbkiu3EaJBLI4QCdDS9NtJF5qIB6n9ijWpNI++NsY6xr3jOAR9NHnT7RY2akhG6PsG2Z5YUMGqi+/pcYIHnNcHq1lkHYXcAhV6ZFty5TaC1LrpCBG/F9lyCxZtcr7JRrGFSIBeWLDmLc2j73dBoQcgoEoo0uRNV1tYr2KM2jbJNsWDMpZdf0+PdunZFRV6ZMssy0Ku0kLYTw/ITqBca0ust2mR889azzcQoa9jtow+xPr65moFFwj4JJq86WLdnoFctY14iE70yPbEQwqypSYM070vXTk1SLv0bIoKPbJljXYflgW61+QQHMchGpCRcfHdgJ2iiZvsSdVlGGoUluCODoGAKqFArZuutZytIx5UIAj02ES2RxQFBFQJmaJ778xztGLBtug7FtmybLmNaEAGR/uDHCPsp3t6t5Ip0sRN1ryFGcevVXivoI9aN91sYb1G0zbJjsVdvmaBVizYFxV6ZMuypRZC1O7mKCHNiw0Xv0XciUa7j55uQlNoch5Lcv4SeqG9rGMMjCp70NMNdHoG6yhkwCwLWMxQoUd2zu2FHq+GYFHrpi1RoUe2bKPUoh16DhMNyFinFQvvs1FsIhakk2mW+G4NnnYJfZ+z9+e9D8ch5PPS5E0XKtc7MEwLQVotRHYoHlSQLrr3nt71XXo0/M1+qNAjW5YpthDW6ETPSWJBGWkq9N5ng9o2mZMLV9ALTAK8wDrKQNHkTXdaTNc3p23SyyGyQ5IkQFNEZF16lYKTFIDjYHXcu0bCqajQI1uWLbUQoQEWjhLSvKi3+uj0aJHzdev5Br2wYMybv4h+cA/rGAPnV0UUaPKm61xNV6ltk9yzeMjd+/R4LULtmzZEhR7Zks3VCm1areAwPL85eXODJm/esF5o0okeS6YBuTiHXnCKdZKB21yxQCd6bqIbJtYLTSSp0CP3KB5SXL1Pj1PDtGLBhqjQI1tSafTgFQV4RXe1Wo2CWFDGWt69P1y2a6PYotUKDEnVFRiSD6aksY4ycEFNQrFGJ3puspZvIqRJkOhnH7lHidDmVQrLZJ1kODg1CKOaYR2D/AxaiEa2JFNqIRKg0zy7E9pleIuz4Ht1cJYJWCaeRhuNpQlYR8KbffQjrNPT0Wj3aagCQ97CJfRdeJoHAJosotMz0O0b9FLMJRbSNTrNIwPhlTxQvSKylZYrW4E5XxhWhQo9u6FCj2xJtkSDWOxKaBXgW3kNcuEyhG4dveAemKIGi+MAjkfCaiGZu4zGP3wdnBaD58DjkI59HLwaZB19111/YcHzNFSBFTl/Ca3xJ1nHGA6OQ0jzolDpYCLuY52GDMBiuopHD8VZxyAuEQ/JWM02XFvoGavnWccgP4MKPbIlmVILISr0bIXTO/BffQm+tZPoxI6iMfUMdF8C4N7fkd1s9/DyO+v47z5/DFY1C33tAppf/R/h2fcopIc+DcSPMfo32H3pQpPaNhniu3V4WkX0tRTrKEMT0rzIlltU6LlAo9VHo60j4qfvGWQw4kEFy9k6njiaYB1l4Hjf5h09y7JofZGNUKFHtmSj2MTB8dE7AbIrJX0KwZnn0Q9Monz812FKt3+o1GQR3b6Bng54w+OQwuOwDj8DfeUs2v/+fyK/8EFYD35+JNo604UmIjRQiBmvS9cqvFdIk5At0T09N1jYqGEsqoKjDgAyIPGwgtOzeViWC7d1iJvPEFanDk4JMA5DrqNhLGRLsiWauGkLlonAzPMIzH0HtYPPor7/Y3cs8gD8tJ2s+tOHT05SIE5/AN4P/w6MegHNf/kD6MvvDjk8e+t5OtFjSXbpWoX3Cmle1+7KGjXza7RWgQyW4vXAKwnIu3ANC8dx4LUorGqWdRTyHlTokbsyTBOleodaN1kzdYTP/QPkwhVUjvwSdC255f9rwCchX7157DsnyQg+9YsQH3wWndf/Hu2X/wKW3htkaltJ07J0dkwdcnEWveBe1kmGKqRJKNU7ME2LdRRyDwzTwkqujrEoFXpksBIhBasuXbPA+cIwa1To2QkVeuSuitUONEWE6KEvF1Y4vYPYqf8CvltD5fAvwhK312YZUEXky7d/gyjE9sL7c78Fq1lB6/n/DWazfK+RbUc3TJTrHRoqxIi3OAtDDrlyrcJ7eTwCVFlEqUb79JxsPddAwCfBK9ENFzJY8ZCMpUyNdYyh4JQgDJq8aSv05E7uKlNq02V0lkwDkTN/DdPjRf3gpwB++w8eIc17yxO99+I8EsRHPgs+thetb/4vMLLzO01sS5ni5kAhQaBveyyoG++gGz7IOsauCGte5O7wYoXY33y6hjFq2yRDkAirWMs3Ybnw0J/zhWFVN1jHIO9BTzzkrrLlFkJ+2jvGSnDmefD9Dhp7f/6miZpb/hg+CcW7FHrAZo+9OP0BiMc/idaLX4K+5J57eyu5OhJh9w+csSWjDzl/Ed3INOskuyKoSchQoedoV9erGIvQ5FQyeIrXA9ml9/R4Xxgm3dGzFSr0yF1tFJt0P48Rde0k5Nx51A5+csdFHrD5g8UwTbQ6+pZ+v5A8CO8Tv4LOT76M/sJbO/68drKSbSBO9/OYkAtXoKuxuw8OcomwJtFAFgerNnpo93REAvRzjwxHIqxgJVNnHWPgOF8EZi0Py43HlQ411ELv29/+Nj7zmc/gU5/6FL7yla/c9M//5E/+BB/96Efxuc99Dp/73Odu+XsIe5lii0bSMyCVFxCY/TZq078Ay3OPBQrHIeTzorCFU73r+NAYpCd/Dd3X/h692dfv7fPbwHKmjniITvRYUDZOj0zbJgCE/DJylbYrW7NGwUK6ivGIz4Xz74ldxIMKlrIuLPQkGRA8sNpV1lHINUO7ZZzNZvGlL30J3/jGNyBJEn7jN34DTz31FKanf9q6c+HCBfzRH/0RHnnkkWHFIAOQK7cRfpBOQnYT32sicuZvUN//MRhKeCAfM+CTUKi2MZXc+jAMPpCA9NR/RO/NrwKmAenIhweSZbdZloXVfAMff2ySdZSRwxk9yIUrKD3wX7GOsmsUrwc8x6HR6sHvo7Z3p5lfr2IsOhqnz4SNRFjB6dkcLPOemnVsifdFYFaz4NUQ6ygEQzzRe+ONN/CBD3wAoVAIqqri2WefxYsvvvi+33PhwgX8+Z//OZ577jl88YtfRLfbHVYcskN93UCt1UOQHlZ2VfDKN9ENH0B/gKPoAz7pjpM3b4f3xyA99evovfU19JdODyzPbqo0euAA+GSaoLfbvPlL0LUkLHG0BluENQlZuqfnOLpuYi3fpP15ZKhkrweKV0S24r4Wb84Xol16NjK0Qi+XyyEej9/4dSKRQDb70z/4ZrOJo0eP4vd///fxzW9+E7VaDX/2Z382rDhkh3LlNkKaFzxPLSy7xZu/DG9pHs2JJwf6cYM+aceXv3ktAunxX0bnx38FfWNmoLl2w0q2jmREBUetWLtOHbG2zeuCmkSTNx1oNddA2O+FJAqsoxCXi4dkrLhwnx6nhmDQ5E3bGNrrbdM03/dQZVnW+37t8/nwl3/5lzd+/Tu/8zv4gz/4A3zhC1/Y8ueIRt29j2nY4nH/XX/PfKaBRERFKERvN3dFvwPlJ/+C/pFPQAve/c9nOwSPgBMXMwgGlZsKni39+YYOoOv9FVR+8CeI/jf/CVLCOYuvi2fTmEz66ev4Lgb+36ffgVyaQ+fQM9DE0brnm4ppmys9HPo159Tc92r1XAZ7xwLQaADZDfTfYjimUgGsZOuO+bu21ZzteAq97NKWnjHJ8A2t0EulUjh16tSNX+fzeSQSiRu/TqfTeOONN/Crv/qrADYLQY9ne3GKxQZMk26770Q87kc+f/eLwHNLRfi8HlRc2F5gR8FL/4qubxwNKQU0htPKvL5Rg6aKN34dCqlb//NVxiAc+QjWv/KfoH7+fwKvRYeScdAuXS1iLLqNf88RtK2vgy1S0qfg0cbR6PLAiLXmK6KA9XzDkV9zw/hacALLAi4tFvDB42NoDOn7r9Nompf+WwxJQBGxnKmhVGravmtqO98TTE5FL7++pWdMcu94nrvjwdfQWjc/+MEP4sSJEyiVSmi323jppZfw4Q//dJCDLMv4wz/8Q6yursKyLHzlK1/BJz/5yWHFITu0lm/SxM1dIpUXoWTPornn6aF9js3F6ffWTuaZOAZh76Nov/h/w+o74wFgNddAknbo7Trf6hvoRA+xjsGEXxHR6vTR7Rmso5AtKlbbME0LIY3upJPh80oCfLLouhZvzheGVS/AskzWUQiGWOglk0l84QtfwG/+5m/i85//PD772c/iwQcfxO/+7u/i/PnziEQi+OIXv4jf+73fw6c//WlYloXf/u3fHlYcskPpQhNR2j02fJaF4JVvoDn5gXtfpXAH93JP7708B54Ap4bQeeUvbL8vp9szUGl0EfHT1/Fu8tQ34Gnl0QvtZx2FCY7nEPZ7XfcQ52Zza1WMxzRaq0B2TTykYDlbYx1joDiPF5wowWpWWEchGGLrJgA899xzeO655973v733Xt6zzz6LZ599dpgRyD2wLAuZUhMxKvSGTs6dB6f30I0M9/Qj5JewUbj3liyO4yA+8Cn0Tn4VvdP/Bu/jnx9AuuFYy28uSrd7a4zb+FZeRSd2FOBHd6hFSPMiV2ljzzZWmhB25taqOLKXRsKT3ZMIKVjKNPDUMdZJBmtzcXoWvBZhHWXkuWx7BxmkUq0LSRQgSzSSfqhMA8HZb6M5+dTQ3yRH/PLARr5zggfSY59D//LL6C+8PZCPOQwruQYtSt9lnN6BmnkXnbjLnl62Kah5kSmN3l03J2q2+yjVO0gE6XsF2T2JkIKNYhOGy+ZNcL4wTFqxYAtU6JHbShebiNEPvaFT19+C6VHQD+wZ+ucKqBIa7R66/cHcG+JkDdLjn0fn1b+BUU4P5GMO2nKmhhgVertKXT+Fnn8SpjTaJ1kRTUKWCj1HuJquYSziAy/QYxHZPZIkQFNE170Q4tQgzEqGdQwCKvTIHaznm4gEaBDLMHFGD4H5F9GcGP5pHrB5byjk9+5ocfrt8MEUxMMfRvulP4bV7wzs4w7KSraBBBV6u8ey4Ft9FZ3EcdZJmAtqXlQaXfT7NJTA7mZXKxiLOWPMPXGXREjBcsZdEyp5XwQm7dKzBSr0yG2t5xuIBuh+3jD5ln+Cvi8BXUvu2ucMa15kSoMdEOGZehB8MInOj//KVsNZTNNCutCkQm8XSeWr4Ewdff8E6yjMCQKPELVv2p6um1jLNzAe8bGOQkZQMqxgacNlA1l8YZhVOtGzAyr0yG2tF5pU6A0Rp3egLb2M1sSTu/p5w5oXmWJz4B9XPP5xGMUV9C7+YOAfe6dylTZ8igivNLoDQXabb+VVtOPHaXLhNdGAjHSxwToGuYOlbB0RvwyJvk8QBuIhBZlyC33dPSf/nC8Mq1GEZdJ6Gdao0CO3tDlxs0UTN4fIt3oC/cAkDCW8q5837PciUx78CQMniJAe/UX0Tn8LRu7qwD/+Tqxk63Sat4uEVhFycQ7d6GHWUWwjGpCxnqcTPTubX61iLEptm4QNj0dAxC9jLe+eF0KcIIKT/bBqedZRRh4VeuSWyvUuPAIPxUsTN4fC1KEtvYJ28uFd/9RBn4RaszeUt4e8Lwzp/k+h/YM/hdUd/Knhdq3kGoiF6GXFbvFf/R7aiftheehu73XRoBfpYhM26mgm72FZwHy6iokYtW0SdhIhBcsb7rqnx2kxmBW6p8caFXrkljYnbtID8rCoG+/AUELQffFd/9y8wA9scfqtCGOHwMcPoP3K/8f8vt5yhk70dounmYOSu4B28iHWUWzFJ4uwLKDe6rGOQm4hU2xB8vDQVIl1FDLCkhEVS64byBKGQYUec1TokVtK55uI0P284bBMaIs/RIvBad51Yb881LHv4pGfh1nNML2vZ1kWtW7uIv/8i2gnH6LTvJ/FcYgGZaQL7E+4yc1m1ysYj9JpHmEr6vei3Oii3XXPnTbOF4ZZXmMdY+RRoUduaa3QRJRWKwyFt3AZANAPTDLLENIkbAyx0OMED6RHntu8r1dYGtrnuZNSrQvLAgI+elM/bJ76BrzFGbSTD7COYktRvxfrVOjZ0uxKBZNxKvQIW7zAIxGSsZJ1z6ke54/BtOl+3VFChR65pfV8E1Fq3RwK/8IPN+/mMZxKGPZ7h77ImfeFIR7/ONrf/1NYveG0id7JwkYN4zEfOJr+OHSB+e+inXwYlkBF9a1EA3SiZ0eFagfdvkHdK8QWEmEFSxn3rFngtQjMaob5FY5RR4UeuYllWdgoNhGjH34DJ1aW4GmX0I0cZJojpHlRrnehG8P9BuwZPwo+MoHOq3+969/s59eqSEWobXPYxNoapPIi2on7WUexrUjAi3ylPfS/b2R7ZlYqmIxrtAqE2EIirLpqcTonqQAvwGpXWUcZaVTokZtUQiobVQAAIABJREFUGj0IPAdVFllHcR1t6eXN9jaO7V89QeDhVyUUq52hfy7x2Mdg5JfQn31t6J/rvebXqxijBcjDZZkIXv4GWuOPAgJ9v7gdj0dAwCchX979k21yezMrZeyJa6xjEAJgc8dtu2eg3nTP4CZei9LkTcao0CM32Zy4SSchg8Z3a5CLs+hEj7COAuDa4vTS8NvJOEGE9Mhn0T35zzB2qV9fN0ys5xtI0W6soVJXT4DX2+jEj7OOYnubi9OpfdMuKvUumh2dpksT++A4pMIqljIu2qenRajQY4wKPXKTdL6JSJAGsQyab+0EuuGDtplKGNIkZIq7s8iZ98chHnoGnR/8KSx9+G8rV3MNhP1eeEVh6J9rVPGdCoLz30F930eYn1A7QcTvxbqLFiI73cxqGZNxHzie2jaJfcTDCpYy7ml13Jy8uc46xkijn87kJmv5BqJ+ess5UJYJ3+oJtBP2OfkIB2RkSrvXSiZMPQhODaLzxleG/rkW0jWM0cj04bEshC5+De34cRhKlHUaR4gGFazTiZ5tXFmmJenEflIRBcvZBtwyv4T3RWnyJmNU6JGbpAs0cXPQ5PwlmKIKQ939Bem3E9IkFGttGLs0IILjOIj3fwrG2nn0508O9XPNr1eQilDb5rDI2bMQmxm0xh5jHcUxAqqIXs9As6OzjjLy6s0eKo0uEmH6HkHsRVMkCDyHQsUd93k5P93RY40KPfI+lmUhXWzRxM0B8y2/irbN7jGJHgGaIu3KPb3rONEL6ZFfROf1v4dZzQzt81xdr2GM7ucNBd+tI3T566jv+yjAU2vsll1bnL6Rp1M91mZXN0/zeGrbJDaUiqhY3HDHmgVOCcDqNmH1hz/4jdwaFXrkfWrXpj2psodxEvcQWgVI9TXmKxVuJRaUsbyxu+Oc+WAS4n0fQvulPxnKfb1Gu496u48ovawYPFNH9N0voxM7Cl1LsU7jOJGAjLUC3dNj7fJKCRO0JJ3YVCqiYiHtkkKP48FpUZiV4b3YJXdGhR55n/VCE/GQTEumB8i3+jo60cMAb7/iORrwYjG9+xe/hb0Pg1MCQ7mvt5CuYjyq0tv6Qbt2L8/kPWiNP8E6jSPFgwpWslTosdTs6ChUO9TaTWwrEVaQLjXR103WUQaC1yIwK3RPjxUq9Mj7rGQbtFphkIw+1PW3bNe2eV08pGAlu/sLWjmOg/jAp2CsXUBv5tWBfuyr6zV6iBsC38qr8FYWUN//MVowvUOxoIxCrY1u32AdZWTNrJQxEfNBEOjxh9iT6BEQ8ctYy7njpRDni+zaaiVyM/pOR95nOVtDIkSF3qAouQswlBhMOcg6yi1pigjDMG+07O4mTvRCeuzzm/v1CksD+7jz61WauDlg3uIs/FdfQnX6FwBBYh3HsQSBRzyouOYBzokuLpZoSTqxvVTYRe2bWoQmbzJEhR55n+VMA4kwFXqDoq6dRCd2iHWM2+M4pCIq1hjt9+L9MUjHP4H29/4YZufeTxZNy8JShgaxDJK3OIvI2b9F/cAnYXoDrOM4XjyoYNlFC5GdpNrooVTvIkUvgojNpSIKFlwykIXXaPImS1TokRt6fQOFahsxWq0wEHynAqm2gm7oAOsodxQPq0wXOQvjRyCkDqHzgz+DZd5bS1u21IIseeCTxQGlG23e/EWEz/4tagefRT8wwTqOKyTCCpay7niAc5rLyyVMJTS6v0tsL+yX0eroqDPothk0zheG1cjf8893sjNU6JEb1gtNRAMyPHR3YSDU9bfRDR8EBHsXHcmIilXGI989h5+B1e+ge/Kr9/RxaK3C4MiZMwif/0fUpn8Bff846ziuEQnIqDZ6aHfpoWe3XVwqYyrhZx2DkLvieA6pqIrFzO7foR80ThDByX5Y9TzrKCOJnujJDas5atscGMuCb/0kOrEjrJPcVTSooFLvMh0QwfE8pEeeg770DnoXf7jjjzO3RovS75llQlv4AUKX/hXVQ5+lNQoDxvMcEiGZyRCkUVaotNHu6oiHqGOFOEMqrODqujtO/zktBrNM7ZssUKFHbljaqNHEzQGRKosAAN2XZJzk7gSBQyQgY6PQYpqDkxRIT/wyeqe/BX3l3I4+xpWVMqYSNGhhp/heA9HTfw5l4x1Ujv4KDDXOOpIrxUMKljPueIBzikvLZUwlNZoYSxwjFVGxkq3DcsGWBd4XhlFeZx1jJFGhR25YzjaQpBO9gVDXTm7uznPIQ0U04GU2kOW9eF8Y4qO/iPbLfwGjuLKt/2+p1kGzrSNOU2N3hM9eQeL1P4Qpaage/hxML7W4DUsirGKJ9untGssCLi2VsZfaNomDKLIIRfZgo8T2asUgcIE4zG3+TCeDQYUeAbA5rTBdoNbNQeD0LpTceXSjh1lH2bKYjUa+C5FJiMc/hvZ3vwRzGz39l5fLmEpp4BxSXNuF0C4hfOZv4D35ZdT3fRjNyacBXmAdy9XCmhftTh+NVp91lJGwUWyCAxD2e1lHIWRbUhEFiy6Yvsn74zBKq6xjjCQq9AgAIF9uQ/F6IEse1lEcT86eQ18bgyk5Z4R3PKggU27ZpkXEM34Unv2Po/Xt/wNmo7il/8+lJdqPtR1cvw3/3AtIvPF/wRK86D75W+gH97KONRI4nkMirGIlR/f0dsOlpRK1bRJHSkVUV9zT47QorHoelqGzjjJyqNAjAIDlbB2JMA2xGATf+pvoRG28O+8WJEmAInmQr7ZZR7nBs/9RCFMPbRZ7rcodf69lWbi8XMbeJLVm3Y3QKiB4+RtI/eSLkKprKB/7NbQmnrD9dFi3SYQULLlgop7dmaaFKysVTNH3BuJA8aCCUr2DVsfZBRIneMCpYdqnxwAVegQAsJKt0zSyAeA7FYj1dfRC+1hH2bZYULbFPb33Eg88AWHiGFrf/t9htqq3/X25chumZVFr1m1wehfKxruInP5LJE78Efh+E+Vj/xH1Ax+nu3iMJMIKlmny5tAtbtTgkz3wqxLrKIRsmyDwSIZVd7RvBuIwqX1z11GfHgEALGXqOLwnxDqG46np05u783jn/dWKBWWs5Rp49JC9Ji2K0x8ATAOtf/vPUD/zP4AP3jzJ9PppHt3P+ym+W4W3OA85dw5yYQZ9LYVe+AAaUx8CBHroZS3ok6DrFqqNHoIa/XkMy7mrRexLBVjHIGTHxiIq5larOL4/wjrKPeG0KIzSKqh3ZHc572mUDMVaroFnHqSlyPdKTZ9Cc/IDrGPsSDyk4MJiCZZlv6ss4qEPgfP60Pq3/xXKs/89hOT0+/75xcUSJkf5fp5lwtPMQ6ytQKosw1uchdCtox+YQC8widID/zUskQYt2QrH3Ri08PB9MdZpXKnV0bGcrePBg1HWUQjZsbGYD2evFmGYFgTeZj+ct4H3x2FkZlnHGDlU6BFUmz30DRMBld6z3AtPPQ2+30Lf78yCWVNE8DyHQrVtyxUFnr0Pg5P9aL34JcjP/LcQDzwBYHNi7MxqBY8fSTBOuAtMA0KnDE+rAE8jC7GxAbGxAU8jA1NUoasJ6GoMjX0fga7GAI668+1sLOLD7GqFCr0hubhYwmTMB9FDU2SJcyleDzTFg3S+iT1J577Q5PxxmJd+xDrGyKFCj2A1W0cyrFLb2z1S06fQjU7b7zhsqzgOqcjmXQA7FnoAICQPwvvkr6H7+j/AyMzC++SvIV3qQRJ5BH0uaH+zLPD9JoRWEZ5OCUKrBE+rAKFdhKddhNCpwpR8MLwhGHIIuhJGK/UodDUKy0N3bJ1mLKrirZksen0DkkjFyCBZFnDuagEPTVMRTZwvFfVhfr3q7EJPDcLqtWB1m+C8zplK7nRU6BGs5Bq2fbB3DMuEunEa1enPsE5yT1IRFQvrNTx59OZ7cHbBB5PwPvNb6F/4Plrf+J+xlPwlTDlpEfL1U7lmHp5WfrOQaxU2C7l2GeA9MLwBGF4/TEmDIQWgRw+h5Q3C8Podef+T3JooCogHFSxu1HF4iu5ID1K21EJPN5Ggn23EBSaiKt6eyeOjj06wjrJjHMeBDyRglNbgGXPOnmGnoycGgqWNGi1Kv0dSeQGm4IWhOvsuSCKs4MSlDPq6CdFj37Y/TlIgPvIcjPWLOHz2y/BHngKnx+11qmXq8DRzEOsb8DQzEOsbEJtZCO3y5qmcHL5R0PVC+9BOPgBTCsDy0OTQUTIe9WFutUKF3oCdnS9gfyrg3A4LQt4jEpDR7umoNHoIOXh4E+ePwSytAVTo7Roq9AhWcg08cMDZBQpr6vrb6EbuYx3jnokeARH/5vTN/eP2nlTHcRz48eP4+skGPm+mIf/kP6N+4BNo7vnQru+E4/otiLV1SPV1iNVViPV1eNpFGN4AdCUKQw6hH5hAO/EADDlIp3LkhomYD987tQrTtMA7eNCCnei6iSsrFXz6yT2soxAyGByH8YgPV9ereOywvSZjbwevxWAUV1jHGCn0tDHi2l0d5XoXkYCNTkKcxuhDyZ1D+fivs04yEKmwioV0zfaFHgBkS22Y3gBa0/ej2yrCl34L/sWX0Zx8Cu3xx6H7Bj+ghe/WINbWIdbWINVWINbWwfeb0NU4DCUKXY2iGzkIXYlQQUfuSlVEaLIH6w4ftGAns2sVRINeKDINGCPuMRZVMb/m7EKP88dhLLzJOsZIoaeQEbe0UUMqojp6ZC9rcv4SdDUOU3LHQ1oqouLtmRw+zjrIFlxdryJ1re3YUKOoTf8CPM08vMVZxN78YxhyEO3kw+iHptAL7IElqlv7wJa1eUrXzMHTzMLTyECspyHW0+AsA7oah65G0dfG0E48uHlKRxMuyQ6NxXyYW3P2oAU7OTNXoN15xHVSURVvXnH28CY+EIdZTsOyLBoAuEuo0BtxV9NVpCJbfPglt6SmT6Ebmb77b3SISMCLVldHrdlDwOaTLGdWy3j4vve/3dR9cei+OJp7noZYX4dUWYGSPQdPKwdT9MGQQzBFFabkgyV4AVMHZ/TBmX0IvQaEThV8rwYAMJQIdDkEwxtCN3IfmpNPbxb09AOKDNBE1IeTl7L46KMT9KV1j/KVNkr1Lj54PMU6CiEDJXo2hzetZBqY3hNkHWdHOEkB55FgNYrg/DQRdzdQoTfi5taq2D9Gbz53iuu34S3NoTn5FOsog8NxGLu2ZsHOo8kr9S6aXR2x27Udczz6gT3oB67d07HMzSKu3wSnd8DrXXBmDxYvwvLIsHgBveAUTEmDKWmwBHsXucQ9wn4vdNNEsdZBLEht9PfinZk8DowHwAt0wk7cJxVRMbNWcWyhB1zbp1daA0+F3q6gQm+EWZaFxY06PnT/GOsojqXkzqMfmLDXtMcBSIYVLKTtXejNrlUwGdPAbbXtmONhKGEYSni4wQjZLo7DeFTD/FoFsSCdRO1Ut2fgykoZn35qL+sohAzFZFzDSw4f3sT7YzBKq/DsfZh1lJFAr7xGWL7aAccBfpUurO+Ukn7bVW2b16WiPqxk6zBNi3WU25pdqWA8Rm3HxB0mYipmV6usYzjauatFjEV9ULz0Dpu4k+/a8Ka1fJN1lB3j/DGYBZq8uVuo0BthC+kqJmI+uhC7Q3y3Dqm2hm5wH+soA6d4PVBlERvFFusot9Ts6CjUOkiGqdAj7pAIKSjXO6g3e6yjOJJlAe/M5jE96dyWNkK2YiKmYWalzDrGjvH+OIzSGusYI4MKvRF2db2KJA1i2TEl8y56wX27vrNtt6QiChbS9jxhmF+rYCzig0D3cIhL8AKPPQk/Li6VWEdxpMWNKjwCd/s7u4S4xETch7m1Ciz7NtzcEadFYTUKsHR6qbUb6ClphM2v1TAe9bGO4Vhum7b5sybiGq6s2POHycxqBRMx+tol7rI35cf5xZIt/87Z3dtX8pieCNJEXOJ6QZ8EnueRLdmz4+ZuOMFzbSDLKusoI4EKvRHV6xtIF5u0WmGHhFYBnlYBvcAk6yhDEwvI6Osm8pU26yjv0+0bWM83MUb384jLxIMydN107AMcK+VaF9lSC1NJP+sohAwfx2Ey5sPMqoPbN4MJGPlF1jFGAhV6I2phvYpYUIbooS+BnVA23t08zeOdubR0SzgOUwkNl2zWSraYriEekiF6XPzfnowmjsO+lB/nFwqskzjK2zNZHBwPUCs3GRmTcZ+jhzfxgSSM3ALrGCOBviuOqCvLZaSidCKyI5YFdcPdbZvXTSU1XF4u26qVbHa1gvGYxjoGIUOxN+XH5eUKDBtPvLWTZruPy0tl3DcZYh2FkF0TCcjo9g2Uah3WUXaED6XoRG+XUKE3oi4tFjFGbZs74mlsgNc76Gvu3z8Y0rwQeA7poj1GOeuGhcWNGibpfh5xKU2REPR5cXW9xjqKI7x9OYe9qQBkWqlARgnHXZu+WWGdZEc4fxxWvQCr32UdxfWo0BtRsytlGsSyQ+rG6c3TvFG49M9x2JPQcGnRHu2bc6sVRAIyPdQRV9ub1HD+KrVv3k2nZ+Ds1SIO76HTPDJ6JuM+zKw6tNDjBfCBBIwi7dMbNir0RlC53kWnqyPs97KO4jyWCSX9DjqR+1gn2TVTCT9mViuwTNZJgHfnC9g/FmAdg5Ch2pPUsJJvoNXRWUextdMzeUzEfPAp7lxxQ8idxEMKqo0eag7dvckFkzCpfXPoqNAbQQvpGqZSAVqUvgNSZQngBRhKlHWUXeP3SVAkASu5OtMc5VoXhUqb1ioQ1xM9AiaiPlxedu5UvWHr9Q28M5vHkb1h1lEIYYLnNzturjj0+8TmQJarrGO4HhV6I+hquorxOD0s74SSHqG2zffYk9RwaYntD5Oz8wXsG/PTZD0yEval/Dg7X7DVICQ7OTNfQCKkIOCTWEchhJk9CfY/m3dqcyDLEusYrkdPTCNobrVC+4Z2wjSgZM+MVNvmdVNxP2ZX2U0CNEwLF5ZKODgWZPL5CdltqYiKvm5iNcv2JN2OdMPC25dzODJFd/PIaEuEFNTbfZRqzhtqwmkxWK0yrJ69dvW6DRV6I6bbM7Caa2Bviu45bZe3OAvTG4Qpj16xoSoigpqEq+ts9vbMr1XhV0T46e09GRUch0N7QnjzcpZ1Ets5M5dHSPMiHJBZRyGEKe5a++blZXsMTNsOjuc32zcLy6yjuBoVeiNmbr2CVFSFJNKy6e1SN06hMwK7825neiKIty6xeeg8M1fAgXF6OUFGy76UH5lSG4UKvfG+rtszcOJiFg8eHJ170oTcydS1qxVObPPmgkmYBRrIMkxU6I2YS0tlTMZp2fR2cUYPcv7SSCxJv509cQ21Vg/pwu7u1Ks2esiWW/R1S0aOIPCbL1iu5FhHsY2TFzMYi6oIajQ1mhAAiAVk9HUDeQe+EOIDCRjZBdYxXI0KvRFzaamEqSQ9MG+XnLsI3ZeAJY7uknmO53BoMoQ3d/lU7+zVAvamaAgLGU3TE0HMrVbQbPdZR2Gu3uzhzHwRD+yPsI5CiH1wHKYSflxecl77Jh9KwSgssY7havTkNEJaHR2ZUosWpe+Amn4Lncgh1jGY2z8ewEq2gUpjd/b2dPsGzs0XaQgLGVleScBU0o/Ts3nWUZj7ybkNTE8EoMi0N4+Q95pKari04rz2TU6LwmpXYXV3t1NolFChN0JmVyuYiGnw0MnItvC9BqTyIrrh/ayjMCd6BBwcD+DtXRoQcepKHomwgoBGQ1jI6Dq0J4QzcwX0+ybrKMzky21cTddwZIr25hHys0KaFwLHYaPorIKJ43jwwRQNZBkieuIfIZeWStiToNO87VI23kUvtA8QqNgAgPsmQ7i0VEK7awz187S7Bk7N5HA/tWmREedXJcRCCs5dLbKOwszLZ9ZxbCoEkQaJEXIzjru2U8957ZtcIAkjR/f0hoUKvRFyabmMqQTtz9suNf02OtHR2513O4rswURMw5m54baSnbyYwVRCg6ZSgU3I8b1hnLiUQbc/3BcsdjS7WkWp1sXBCWrhJuR2ppJ+XFlht+92p4TwOIzMDOsYrkWF3oiot3oo1jpIRUZ3mMhOeJo5CO0S+oE9rKPYyuGpEE7P5ofWStZo9XHuagHH9lKbFiEAEA7ISIUVnLyYYR1lV3V7Br7/9gqeOJyggUyE3IFflaDJIhbTNdZRtoWPTMLIzsOyRrc1fZjou+aImFmpYCqhgec51lEcRUmfQjdyH8DRX5X3CmpeJMIKXj2fHsrHf+38BvaP09AFQt7rgQNRnJkrotbcnWFIdvDjM+sYi6qIhxXWUQixvX0pP85eLbCOsS2crIGTVJilddZRXImeXkfExaUS7SHbLsuCmj6FLrVt3tLD0zFcWCwjWxrs7p5yvYuZ1QqOTdHdPELeS5FFTE8G8eMzw3nBYjfr+SZm16p48GCMdRRCHGFPUsNqroFmR2cdZVv4yCS1bw4JFXoj4vJymfbnbZNUWQI4HroaZx3FlmTJg4cORPCdk8swB3QnwLKAl95exeHJECSJhi4Q8rOOTIWwnK0jU2yxjjJUhmnhxTeX8fB0DBINYCFkS0SPgMmYhouLzhrcxIcnYKSvsI7hSlTojYByvYt6q49EiFpftkNJv41uZBrgqN31dvaPBeAROJyayQ3k4524mEG7o+MI3c0j5JZEj4Dj+yL44ek1x+3M2o6TFzOQJQ+mEvSCkpDt2D8WwLmrRUd9f9g80ZuD5aTQDkGF3gi4slLG3qQGjgqWrTP6UDNn0InSkvQ74jg8diiOExezqN7jEvWVTB2nZ/J4+niS7pIScgcHxgJodnVcXnbeKPWtWMs18M5sAY8ditOLNkK2KR6S0TdMR+3U49QQAAtWfbjTvEcRFXoj4MxcAVNJWquwHUruAnRfHKY3wDqK7WmqhCNTIfz7G0vo6zubmtVo9fH8G0v4wLEkDWAh5C44nsOThxP44el11F02mKXd0fH860t44nAcqkLfCwjZNo7D/lQA5+ad077Jcdzmqd4G3dMbNCr0XE43TFxYLOHgOO0f2g517QQ60cOsYzjGkT1hSCKPr708j94293yZpoXnX1vEwfEgkrT+g5AtiQRl3DcRxL+fWHZUi9adWBbw7RNL2JPQME7DwwjZsf0pP2ZWKzt++coCH56AToXewFGh53KzqxVE/F74VXozulVCuwyptoZu+ADrKI7B8RyePJKEVxTw1R/No9vbWrHXaPfxzz+aBzjg+D66l0fIdhzdG0a7p+P0lcHckWXtzUsZNNt9PHAgyjoKIY6myCJiQS/mViuso2wZnegNBxV6LvfObB4Hxqn9cDvU9FubQ1h4D+sojsLxHJ44koCmiPinH86h3b1zsbe4UcPffPcKwpoXH7p/jO7iELJNHM/hA0eTeP1iBvnKYNec7LbVbANvX8nj6WMpuqNLyADsGwvi9Kxz7rxx/jisTh1myznFqRNQoedilmXh3bkCpieobXPLLBPq2pvUtrlT14azxAIy/vz5C3jhxDLWco0brWW9voF0oYkfvbOOF04s46mjSRzfHwFHD3aE7IimSnjoQBTffn3nd2RZy1fa+NZri3jqaILu5REyIJMxH6rN3sB33Q4Lx3Hgo1MwMrOso7gKHVm42GquAQCIBWXGSZxDKi/A4gXovgTrKM7FcXhwOoZDe0JYytTwwonlG/+o2ekj6JMQCcj41BN7IEv0LYiQe3VgPIBCtY1/e20Rv/zhA446Eas2e/jay/N4ZDqKVNTHOg4hrsHxHKYngjg1k8N/eHov6zhbwofGYaRnIB54knUU16CnLBc7M1fAwYkgrVXYBt/aSXSjh6mNcABkrwdH9kZwZCqMUr0Lj8DDr4h0ekfIoHEcHj+cwKvnN/DSW6t49qkpR3wLa3d0/MuP5nHfZAhTKbpiQMigHRwP4oWTS2h1JqDK9n/k56OT0C+9wjqGq1Drpoudns1jmu7nbRmndyDnLlDb5qBxHCIBGQGfREUeIUPCCzw+eH8Ka4UG3riQYR3nrrp9A1975SrGIioOT9EgJkKGwSsJmIxrODtfYB1lS/hgCmY9B6vTYB3FNajQc6lSrYNCtYNJGlG9ZcrGO+gFJmGJCusohBCybaJHwDMPjuPc1YKtH+warT7+8fuz8KsiHjxIEzYJGab7JoJ4dy4P07T/HhaOF8BH90Jfu8A6imtQoedSZ+cLODgecNRdDaYsC9rKa+jEj7FOQgghO6Z4PXjmwXG8dm4DJy5kbLdjr1jt4O9fmsFY1IfHDsWpTZ6QIQsHZKheEXNrVdZRtkSI74e+cpZ1DNegQs+lTs/QWoXtkKrL4PQ2+oE9rKMQQsg9CfgkfOKxPbi4VMKLby7DsMmb/PV8A//4gzkc2xfGsX0RKvII2SXTk0Gccsi+TT5xAPrqeViWM6cI2w0Vei7U7uqYT1exf4wKva3yrby6eZpHDx6EEBdQZA8+9sgESrUuvvajeXR7d95rOUyWCZy4mMHXf7yAp44msH+MVv4Qspsm4xrKjS5yZfuvWuDVIDivCjO/xDqKK1Ch50LvzOaxN+mHVxRYR3EEvteAnLuITuwo6yiEEDIwHo+An3tgDLLXg7/6zmVcXd/91q1KvYt/+P4M5lar+OTje2iFAiEM8DyH+yaDOHnR/oOaAECI7aP2zQGhQs+FXj23gaN7aYrZVqlrb6IXPgDLQ/sGCSHuwvEcHj0Ux+OHE3jp7VU8/9oiWh196J9XNyycvpLH331vBqmIio88PA4fLUMnhJnpiSCWMnWUa13WUe6KTxyAvnKGdQxXoELPZYrVDlZzDUxPUGvMllgmfKuvox0/zjoJIYQMTTKi4tNPbu7X+/ILl3HiQgbt7uDbOQ3TwpnZPP7i+Yu4slLGRx+Z2FyfQG3xhDAlegRMTwZx4pL9T/X4yCTMagZmu8Y6iuPZf3si2ZbXL2zgyFQIHoFq+K3wFq7AEiToWpJ1FEIIGSpB4PHQdBz7UgHMrFTw1uUcju0L4/EjCYT93nv62NVGD5eXS3h3rgC/IuLp40lEg7SqhhA7OTQRwgsnl1F9oIegT2LdIu8DAAAaU0lEQVQd57Y4XoAQ2w9j9Tz4Qx9iHcfRqNBzEcuy8Pr5DTz7xBTrKI6hXR/CQgghIyKoefHksSTaHR3z6xX83fdmoMke7B8P4sB4AMe1u7exd/sGCpU21vNNXF6uoNLsYiqh4aljScSowCPEliRJwMHxAN68mMGnnrT3syIf3wd9+QxEKvTuCRV6LnJ1vQbLAsaiKusojuBp5iBVV1Cf+jDrKIQQsusU2YMHDsZw//4oSvUONkotvPzOGr7+ylV4JQEBVULAJ0H08DAME7phoW+YKNU7aHd0hDQvwn4vju4NIRlWwdHeVkJs79BUCN89uYwP3j8GTbXvvVkhfgCdKz+BZRrgeBouuFNU6LnIq+fSOLYvDI7uQmyJtvijzbt5gn2/0RFCyLBxPIdoUEE0qOD+/VH4VAn5YhPNro5mpw/TsMALHASOg8BzuH9fBJoiUmFHiAPJkgf7UgG8dTmLjz02yTrObXGKH5wSgJFbgCd1H+s4jkUXuVyi1zdwaiaP4/sirKM4At+tQcmeRTvxAOsohBBiKxzPQVVExEMK9qUCODARxL5UAHuSfozHNfh9EhV5hDjYkakQzi8U0dyFCbz3Qojvh778LusYjkaFnku8O1fAWESFX7Xv5Vo70ZZ/gm7kPlgi3SUhhBBCyOhQZBH7xwJ47WyadZQ7EsYOQb/6JizLYh3FsajQc4lXz6VxdB/tztsKTu9CXTuBVvIh1lEIIYQQQnbdsb0RzKxWUKx2WEe5LS6QBDgOZnaedRTHokLPBbLlFpYzdRyaDLGO4gjq2gn0/RMwZdo1SAghhJDRI0kCju4N4+V31llHuS2O4yCMH0Nv7nXWURyLCj0XePHkCh46GIPooT/OuzIN+JdeQZtO8wghhBAywqYngshX21jJNlhHuS1h4ij0hbdhGfa+T2hXVBk4XK3Zw1uXs3jkvhjrKI6gZN6F4fXTgnRCCCGEjDRB4PHA/ih+dHoNdr0Gx6sh8FoUxtp51lEciQo9h/vh6TUcngrBp9CKgLsyDQTmv4vW2KOskxBCCCGEMDeV1GBaFi4vlVhHuS1h/Cj6s9S+uRNU6DlYt2/g5XfX8djhBOsojqCuvwVT9KEf2MM6CiGEEEIIexyHh6ZjeOXMOrp9g3WaWxLGDkNfuwCr12YdxXGo0HOw186lMR7zIRqQWUexP6OPwNUX0Zx4knUSQgghhBDbiIcUJMMqXnnXnoNZOEmBENsLffEU6yiOQ4WeQ5mmhRffXMUTh+OsoziCb/V16GoMupZiHYUQQgghxFYemo5hbq2KtZw9B7MIY0fQn32NdQzHoULPoU7P5qHKHkzENdZRbI/TO/Av/ADNcTrNI4QQQgj5WZIo4JH74vjOmyvQDZN1nJvwyWkYxRWYjSLrKI5ChZ4DmaaFf3ttAY/T3bwt0ZZ+jH5gEoYaZR2FEEIIIcSW9iQ0+BURJy5kWEe5CSd44Jl8AL1z32MdxVGo0HOgH59dhyjwmJ4IsI5ie3y3Dm35x2iOP846CiGEEEKIrT16XxzvzhWQL9tv8Imw/zH0Z1+D1W2yjuIYVOg5TKuj41uvLuIjD0+A4zjWcWwvOPMtdGJHYMoh1lEIIYQQQmxNkT146GAUz7+xiL5urxZOXglASBxA79LLrKM4BhV6DvP864s4MBZAMqKyjmJ7Umke3uIcneYRQgghhGzR/rEA/IqEH5xaZR3lJp4DT6B/4fuwjD7rKI5AhZ6DZMstvHZ+Ax96YIx1FPszdYQu/gsaUz8HCBLrNIQQQgghzsBxePxwHMvZBi4s2GuROh9IgPNHoc+fZB3FEajQc5B//uEcnjgch6aIrKPYnrb0MkxJQy+0n3UUQgghhBBH8XgEfOj+FH70zhqypRbrOO/j2f8EemdegGXZq7XUjqjQc4iLSyWsZBt4jCZt3pXQLsG/+PLmaR7dYySEEEII2bag5sVDB6P4youX0esbrOPcwMf2wgJgrJ5jHcX2qNBzgEa7jy//+yV8/NEJeAT6I7sjy0T4wj+hlXwIppemkhJCCCGE7NT+8SBiQQXPv7YI07RYxwEAcBwHz4En0D31LTrVuwuqGmzOsiz81XcuY3oyhAPjQdZxbE9b/CG4fhvtsUdYRyGEEEIIcbwPPjCOds/Ai2+twLJHrQdh/Chg9NGfeZV1FFujQs/mXjmzjkyxhQ8/SANY7kaqLEFbegX1/R8HOPrSJoQQQgi5V7zA4UP3p7BRaOK1c2nWcQBsnuqJxz+B3lv/Snv17oCehm1svdDEN368gP/w9F5q2bwLrt9C+OzforH352F6/azjEEIIIYS4hscj4JmHxnFhqYR3Z/Os4wAA+FAKfHIa3be/zjqKbVH1YFO9voH/8q0LeObBMUQDMus49mZZCF/4Z/SCU+iFD7BOQwghhBDiOrLkwc8/NI7XL2RwZq7AOg4AQDz8DPpX34JRWGYdxZao0LMh3TDxp988j7DfiwcORFnHsT3//IvwNHNoTj7NOgohhBBCiGtpioSPPTKBExcyOHEhw/zOHicpEA/9HDqv/R0s1mFsiAo9mzEtC19+4TLaXQPPPjkFjtYD3JG68jrU9TdRve8zAO9hHYcQQgghxNU0VcLHHp3E+YUSXn53jXmxJ0w9APQ76J17kW0QG6JCz0Ysy8I//WAW6UITz31wHwSeirw7kbPnEJh/EdVDn4UlqqzjEEIIIYSMBEX24GOPTGApU8cLbyxB19mtOeA4HuIjz6F/9gXo65eY5bAjKvRswrIsfOvVRVxYKOGXntkP0UN/NHcila4idPGrqN33GZhyiHUcQgghhJCRIkkCPvLwOJpdHX/3vRmUa11mWXg1CPHhz6Lzw/8XZt0ew2LsgKoJG9ANE3/93St481IWv/LzByFL1IJ4J3LuAqJn/gr1A5+A7ouzjkMIIYQQMpI8goCnjyWxb8yPv//+DGZWKsyyCLG98Bx4Eu3v/T+wdHZFp51QocdYvdXDH/7ju8j+/+3de1BU993H8feywAISuSiIF+I11QYxWJ2JJcQ8RCtqBRFji2SIYxONkzBM69Q2KK3GG5ZkmgxN06m9PLGN0ZhGvFAfNYmPSSNREzHqo3hJTVCUyxqUy3LfPc8fqWSoMUuNuwvr5/Xf4Xc8vy/Lx93z3d+eszWNzJ18D8GBfp4uqVvrVfY+of/3OrUjptPWO9rT5YiIiIjc2UwmRgwMZWLsAPaVlLP74AWaW+0eKcU8dBymwFCa/3c9hqPdIzV0J2r0PKi8uoGVGz6iT0gAqQlDsfiZPV1S92U46H1mB8Gf7ePaqFTag/t5uiIRERER+ZfwkACmjI+mqdXOH4tOcerTGrffqMVkMuE3ZgqOxlqt7AEmowffi/TzzxtwOHpe+W3tDoqKP+OdknIeHjuQe4eEu72G0NAgrl1rdPu8t8LcVEPY8Y2Y7C3UjZiK4avvFbydgoMtNDTc2U+EohzIl5QFuU5ZELi1HNTUNvHRWStBAX48/J2BRIW796Z5hsNO2/E9GC02gqYtxhQQ7Nb53cXHx0SfPjf/3dToudmZC1f57/85TWiwhUnfGchdQf4eqaNHNHqGQVD5QULOFdHYL46mqPvApEXo200v5ALKgXxJWZDrlAWBW8+B4TD45+VaTpVdJSoskAdiB9C/r/saPsMwaD/9Lo4rZQROW4xP70i3ze0uzho93fXDTT6tqKOo+DP+ebmOh8cO5FvRulPk1/GrvUjvszsxt9Ry7Vsp2IP0xfEiIiIiPYXJx8SIQaEM7d+bTyvqePO9fxIREsi4kREMG9AbHxd/jZjJZMLv2/9F+2cl2LauwH9sCv6xUzD53DmLBlrRcyHDMCgtu0pR8WdUfN7I+JERxA7vg7+v56/F664rer71l+l9bhf+tWU0Ro2lOSIGfDz/eHkzvWMroBzIl5QFuU5ZELh9ObDbHVyoqufTinoamtuIHRZO7LC+hPe23IYqv57DdpW2E3sBg4CHHsfc526Xz+kO+uimmxmGQbnVxsGTlRwqrcLs48P4kRHcOzgMs7n7vIPQrRo9exuB1ScIKj+EX/0lmqLiaIocDT5acHYHvZALKAfyJWVBrlMWBFyTg9qGFj6tqKOsqoEAfzP3DArhnkEhDOgT7LKrdAzDwH7xBG1n3sPcbwT+Y6Zh7j8Sk8m1K4uu5NFGb+fOnfzud7+jvb2defPm8eijj3YaLy0tZdmyZdhsNsaPH8+zzz6Lr2/XT+67Y6P3cuEJzl2qZdTdYYy6O5TI0MBuGSBPN3qmtiYsNecIsJ4isOo47b0iaO4zkpawYWrw3Ewv5ALKgXxJWZDrlAUBF+fAMKipa+bSFRuXP2/E1txG//BeRPcLZlBEMP3CArH4395Pdhn2NuzlJ2n/7Agmv0D8Rj2E7+A4fO7qe1vncQePXaNXVVXFCy+8wNatW/H39yc9PZ3777+fESNGdOyzZMkSVq9eTVxcHEuXLmXLli1kZGS4qiS3KLfamD1xGH1DAj1dSvfhsOPbaMWv7hJ+deVYrp7H11ZJW3B/2u4ayNV75+Cw3OXpKkVERETEnUwmwkMCCQ8JJHY4tLS2c6W2GWttE2cuXOVqfQuBFl8iQgKICAsk/K4AQoMthAb7Exzkz62spZjMfvgOjsN89304qs/TfvEYLUcK8QkMwTz4PsyRIzD3HYwpuE+3XKz5T7is0SsuLmbChAmEhn5x05GkpCR2795NVlYWAJcuXaK5uZm4uDgA0tLSKCgo+I8aPVdfxHkr+oQEEBTge9vffbjd/Hx9vlmNhoHJ0YapvQUfewum9mZ82hrxabPh09qIT/M1fFuuYm6qxdxah92vF/bAcOwBIbQPe4Cm4EgwfRE/rd95lp/FH//27vd/SdxLOZDrlAW5TlkQcG8O/APgrt7BDI3+1w8MA1tLO3W2VuobW7l8rZVPKhtpaG6ntc1OoMVMkMWPIIsvQRZfAixmLBZfAvx88ffzwd/XB39fM70CfekV8BVnnIO+DYO+jYGBo7YKR005jvOHsB8rwnDY8ekdiSk4HFNwH8y9IzAP/k63av6c9UIuO8eurq4mIiKiYzsyMpLjx4/fdDwiIoKqqqr/aI6wsF7fvNDbbF3Wg54uQURERERE7nAuuzuIw+Ho1PEahtFp29m4iIiIiIiI3BqXNXpRUVFYrdaObavVSmRk5E3Hr1y50mlcREREREREbo3LGr34+Hg++OADampqaGpqYu/evUycOLFjfODAgVgsFo4cOQLA9u3bO42LiIiIiIjIrXH51yv8/ve/p62tjUceeYQFCxawYMECsrOziY2N5fTp0+Tm5tLQ0EBMTAx5eXn4+/u7qhwREREREZE7Qo/+wnQRERERERG5kcs+uikiIiIiIiKeoUZPRERERETEy6jRExERERER8TJq9ERERERERLyMGj0vt3PnTqZPn86UKVPYuHHjDeOlpaWkpaWRlJTEsmXLaG9v90CV4g7OsvD2228zc+ZMUlJSeOqpp6itrfVAleJqznJw3f79+3n44YfdWJm4m7MsnD9/nszMTFJSUnj88cf1nODFnGXh5MmTzJ49m5SUFJ588knq6uo8UKW4Q0NDAzNmzKC8vPyGMZ0z9kCGeK3KykojMTHRuHr1qmGz2Yzk5GTj3Llznfb5/ve/bxw9etQwDMPIyckxNm7c6IlSxcWcZaG+vt544IEHjMrKSsMwDOPFF180Vq1a5alyxUW68pxgGIZhtVqNqVOnGomJiR6oUtzBWRYcDocxZcoU49133zUMwzCee+45Iz8/31Pligt15Xlh7ty5xv79+w3DMIy8vDzj17/+tSdKFRf7+OOPjRkzZhgxMTHGxYsXbxjXOWPPoxU9L1ZcXMyECRMIDQ0lKCiIpKQkdu/e3TF+6dIlmpubiYuLAyAtLa3TuHgPZ1loa2tj+fLl9OvXD4CRI0dSUVHhqXLFRZzl4Lrc3FyysrI8UKG4i7MsnDx5kqCgICZOnAjAokWLePTRRz1VrrhQV54XHA4HNpsNgKamJgICAjxRqrjYli1bWL58OZGRkTeM6ZyxZ1Kj58Wqq6uJiIjo2I6MjKSqquqm4xEREZ3GxXs4y0JYWBjf+973AGhubmb9+vVMnjzZ7XWKaznLAcBf/vIX7r33Xu677z53lydu5CwLFy5coG/fvixdupRZs2axfPlygoKCPFGquFhXnheeeeYZcnNzSUhIoLi4mPT0dHeXKW6wZs0axo8f/5VjOmfsmdToeTGHw4HJZOrYNgyj07azcfEeXf1b19fXs3DhQkaNGsWsWbPcWaK4gbMcnD17lr179/LUU095ojxxI2dZaG9v5/Dhw8ydO5fCwkKio6NZt26dJ0oVF3OWhebmZpYtW8Yrr7zC+++/T0ZGBj//+c89Uap4kM4ZeyY1el4sKioKq9XasW21Wjstx//7+JUrV75yuV56PmdZgC/ercvIyGDkyJGsWbPG3SWKGzjLwe7du7FarcyePZuFCxd2ZEK8j7MsREREMHjwYGJjYwGYMWMGx48fd3ud4nrOsnD27FksFgtjxowB4Ic//CGHDx92e53iWTpn7JnU6Hmx+Ph4PvjgA2pqamhqamLv3r0d11sADBw4EIvFwpEjRwDYvn17p3HxHs6yYLfbWbRoEdOmTWPZsmV6l85LOctBdnY2e/bsYfv27axfv57IyEhee+01D1YsruIsC2PHjqWmpobTp08DsG/fPmJiYjxVrriQsywMHjyYyspKzp8/D8A777zT8QaA3Dl0ztgz+Xq6AHGdfv368ZOf/ITHHnuMtrY2HnnkEcaMGcOCBQvIzs4mNjaW559/ntzcXBoaGoiJieGxxx7zdNniAs6yUFlZyalTp7Db7ezZsweA0aNHa2XPy3TlOUHuDF3Jwm9/+1tyc3NpamoiKiqK/Px8T5ctLtCVLOTl5fHjH/8YwzDo06cPa9eu9XTZ4iY6Z+zZTIZhGJ4uQkRERERERG4ffXRTRERERETEy6jRExERERER8TJq9ERERERERLyMGj0REREREREvo0ZPRERERETEy6jRExERERER8TJq9EREpMc5fvw4v/zlLwE4ceIE2dnZX7t/RUUFM2bMYObMmRw9etTl892qzMxMdu/efduP+8wzz/CnP/3pth9XRES6L31huoiI9DiffPIJVVVVAMTGxlJQUPC1+x86dIi+ffvyyiuvuGU+ERERT1OjJyIiHmez2cjJyaGsrAwfHx9iYmJYuXIla9eu5dixY9hsNgzDYPXq1QwYMICCggLq6+vJyckhNTWVVatWUVRUxEcffcS6detwOBwAPPnkk4SEhPDiiy9SX19PZmYmGzZs+Mrjjhs3DpvNxurVqykpKcFsNjN58mTmzp170/nq6+t59tlnOX36NCaTiQcffJDFixfj6+tLbGwsCxcu5MCBA1RXV/PEE0+QkZHR5cekpKSE559/nqamJnx8fMjKyiIxMZH09HTmz59PUlISAM899xwAS5Ys4Y033mDTpk04HA5CQ0P5xS9+wfDhw2//H0xERLo9fXRTREQ87q233sJms7F9+3b+9re/AV80OtXV1bz++uvs2rWLWbNm8Yc//IH+/fuTnZ3N+PHjycvL63Sc3/zmN8yfP5+tW7eydu1aDh48yIQJEzr2/+tf/8qxY8e+8rgABQUFtLS0sGvXLrZt20ZJSQkXLly46XyrV68mNDSUnTt38uabb3LmzBn+/Oc/A9Da2kpYWBibN2+moKCAvLw8WlpauvR41NbWkpOTQ35+PoWFhbz88susWLGCy5cvM2fOHLZu3QqA3W5nx44dzJkzh8OHD7Nt2zY2btzItm3beOKJJ8jKyvpGfxcREem5tKInIiIeN27cOF544QUyMzOJj49n3rx53HPPPYSHh7N582YuXrzIoUOH6NWr19ceZ9q0aaxcuZJ9+/YRHx/P4sWLb9hn7NixhISEfOVxi4uLycnJwWw2YzabefXVVwE6Gqt/995777Fp0yZMJhP+/v6kp6ezYcMGFi5cCMCkSZMAiImJobW1lcbGRiwWi9PH4+OPP8ZqtfL00093/MxkMnHmzBmmT59Ofn4+VquVU6dOMWTIEIYMGcKWLVsoKysjPT2949/U1dVx7do1p/OJiIj3UaMnIiIeFx0dzVtvvcWhQ4c4ePAg8+fPZ8WKFfzqV79i/vz5TJo0iWHDhrFjx46vPU56ejqJiYkcOHCAf/zjH7z00ks33Nxk//79rFmz5iuP6+vri8lk6ti3oqKCgICAm87ncDg67e9wOGhvb+/Yvt7UXd/HMIwuPR52u53hw4fzxhtvdPysqqqK8PBw/Pz8SEpKoqioiKNHjzJnzpyOuWfOnMmSJUs6tqurqwkJCenSnCIi4l300U0REfG41157jZycHBISEliyZAkJCQk8/fTTJCYmkpGRwejRo3n77bex2+0AmM3mTg3Vdenp6ZSWlpKWlsaqVauoq6vDarV22ufAgQM3Pe53v/tdCgsLcTgctLa2kp2dzYcffnjT+RISEnj11VcxDIPW1la2bNlCfHz8N3484uLiKCsr48MPPwSgtLSUpKSkjhvC/OAHP6CwsJCSkpKOa/USEhL4+9//TnV1NQCbNm1i3rx537gWERHpmdToiYiIx6WmpmK325k+fTppaWnU19ezefNmDh8+THJyMrNmzSI6Opry8nIcDgdxcXFcvHjxhmvQfvrTn1JQUEBqaiqZmZlkZWUxaNCgTvukp6ff9LhZWVn4+fkxc+ZMUlNTeeihh5gyZcpN58vNzaWmpobk5GSSk5MZOnQoixYt+saPR3h4OAUFBeTn55OSksLPfvYz8vPzO36X0aNHYzabmTp1aseqYUJCAgsWLOBHP/oRycnJFBUV8dJLL3VacRQRkTuHyejq50hERERERESkR9A1eiIiIm5y8ODBG+7ced3999/P0qVL3VyRiIh4K63oiYiIiIiIeBldoyciIiIiIuJl1OiJiIiIiIh4GTV6IiIiIiIiXkaNnoiIiIiIiJdRoyciIiIiIuJl/h/5E4XYDWVFpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "left_sats = df[df[\"left\"] == 1][\"satisfaction_level\"]\n",
    "not_left_sats = df[df[\"left\"] == 0][\"satisfaction_level\"]\n",
    "\n",
    "sns.kdeplot(left_sats, shade=True, label=\"Left\")\n",
    "sns.kdeplot(not_left_sats, shade=True, label=\"Not Left\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question: What can you conclude from the plot above?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bin of 0.0 to 0.2 of satisfaction level clearly depicts the low satisfaction of population of 2/3 dense population nnd data points are present in that range.\n",
    "The bin of 0.2 to 0.4 of satisfaction level clearly depicts the below average satisfaction of population of 2.8/3 dense population and data points are present in that range.\n",
    "There are very few people who have left job and were satisfied with rating 0.6 to 1 with, especially those who are truely satisfied and left the job are clearly very less as seen from the declining peak from 1.2 dense population to 0.\n",
    "\n",
    "on the other hand the low satisfaction level 0.2 to .4 have less dense people means those who are not saisfied but still working in company whereas .5 to 1 show satisfaction of dense population \n",
    "\n",
    "Looking at the the satisfcation we can conclude that people who are leaving have a low satisfaction level, most likely below 0.5 are leaving and people having a high satisfaction_level, most likely above 0.5 are likely to stay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For feature engineering we will two new features. Looking at the the satisfcation we can conclude that people who are leaving have a low satisfaction level, most likely below 0.5 are leaving and people having a high satisfaction_level, most likely above 0.5 are likely to stay. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 12: Make a new feature 'satisfaction_level_type' through following conditions:**<br>\n",
    "- **satisfaction_level >= 0.5 then satisfaction_level_type = 'High'**\n",
    "- **satisfaction_level < 0.5 then satisfaction_level_type = 'Low'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>dept</th>\n",
       "      <th>salary</th>\n",
       "      <th>satisfaction_level_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years   dept  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary satisfaction_level_type  \n",
       "0     low                     Low  \n",
       "1  medium                    High  \n",
       "2  medium                     Low  \n",
       "3     low                    High  \n",
       "4     low                     Low  "
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write the code here to make bins as mentioned above\n",
    "df[\"satisfaction_level_type\"] = df['satisfaction_level'].apply(lambda x: 'High' if x >= 0.5 else 'Low')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 13: Make a count plot for satisfaction_level_type and and see which type has more turn over using hue='left'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac13395288>"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write Code Here\n",
    "sns.set(rc = {'figure.figsize':(5,5)})\n",
    "sns.countplot(x=\"satisfaction_level_type\", hue= \"left\", data= df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously we saw that employees having high number of projects are leaving. We also saw that some employees with extremely less number of projects are also leaving the company. Let us see how number of projects and satisfaction level are related.<br>\n",
    "We can see this by checking the satisfaction level type and number of projects in according to that specific type. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "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>number_project</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>3807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_project  count\n",
       "0               4   3807\n",
       "1               3   3504\n",
       "2               5   2281\n",
       "3               2    591\n",
       "4               6    228\n",
       "5               7      5"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sats_wrt_projects = df.loc[df['satisfaction_level_type'] == \"High\", 'number_project']\n",
    "sats_wrt_projects = sats_wrt_projects.value_counts().reset_index().rename(columns={'index': 'number_project', 'number_project': 'count'})\n",
    "sats_wrt_projects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make a Plot of your findings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac12e90588>"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(5,5)})\n",
    "sns.barplot(x=\"number_project\",y=\"count\",  data=sats_wrt_projects )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen clearly that highly satisfied employees are on maximum assigned 4 projects then as per graph it also shows a peak less than 4 project is 3 projects per person who have shown high satisfacion from company there are a very low number of individuals who have shown high satisfaction on 2 projects and even less to those are which are assigned 6 projects. These 6 projects assigned individuals who have shown high satisfaction could be program managers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "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>number_project</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_project  count\n",
       "0               2   1797\n",
       "1               6    946\n",
       "2               4    558\n",
       "3               3    551\n",
       "4               5    480\n",
       "5               7    251"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sats_wrt_projects = df.loc[df['satisfaction_level_type'] == \"Low\", 'number_project']\n",
    "sats_wrt_projects = sats_wrt_projects.value_counts().reset_index().rename(columns={'index': 'number_project', 'number_project': 'count'})\n",
    "sats_wrt_projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac1405e8c8>"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(5,5)})\n",
    "sns.barplot(x=\"number_project\",y=\"count\",  data=sats_wrt_projects )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What did you infer drom the above plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from graph above individuals who are least satisfied with company are those who are given 2 projects similarly the managers who have been given 6 and 7 projects, dissatisfaction level is also high. It can be seen that there wasnt seen individuals from graph above this graph that shows a very less range of people assigned 7 projects and shown good satisfaction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that there is are people associated with low satisfaction level, have either too less projects, i.e. number of projects = 2 or high number of projects i.e. 6 or even 7. The over all count for 7 is low but there is a significant raise if we compare its count with higher satisfaction level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 14: Make a new column 'employee_type' and assign categories as following:**<br>\n",
    "- **If number of projects is equal to 2 then employee_type='unburdened'**\n",
    "- **If number of projects is between 3 and 5 then employee_type = 'Satisfactory'**\n",
    "- **If number of projects is 6 and above then employee_type='Burdened'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code here\n",
    "\n",
    "df[\"employee_type\"] = df['number_project'].apply(lambda x: 'unburdened' if x == 2 else ('Satisfactory' if x>=3 and x<= 5 else 'Burdened'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>dept</th>\n",
       "      <th>salary</th>\n",
       "      <th>satisfaction_level_type</th>\n",
       "      <th>employee_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>Low</td>\n",
       "      <td>unburdened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Burdened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>High</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "      <td>Low</td>\n",
       "      <td>unburdened</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years   dept  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary satisfaction_level_type employee_type  \n",
       "0     low                     Low    unburdened  \n",
       "1  medium                    High  Satisfactory  \n",
       "2  medium                     Low      Burdened  \n",
       "3     low                    High  Satisfactory  \n",
       "4     low                     Low    unburdened  "
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 15: Make a countplot to see which type of employee is leaving**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_type</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burdened</td>\n",
       "      <td>911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>1093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unburdened</td>\n",
       "      <td>1567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee_type  left\n",
       "0      Burdened   911\n",
       "1  Satisfactory  1093\n",
       "2    unburdened  1567"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write code here\n",
    "emp_type_leav =df.groupby('employee_type').agg({'left': 'sum'})\n",
    "emp_type_leav.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ac13356f88>"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='employee_type', hue='left', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Redundant Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint: Look for the columns used in Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code here\n",
    "# Write code here\n",
    "df=df.drop(['number_project','satisfaction_level'],axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving further, we need to apply one-hot encoding on categorical variables i.e. **dept, salary, satisfaction_level_type,** and **employee_type**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 16: Do ONE HOT ENCODING of the above mentioned variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>dept_IT</th>\n",
       "      <th>dept_RandD</th>\n",
       "      <th>dept_accounting</th>\n",
       "      <th>dept_hr</th>\n",
       "      <th>...</th>\n",
       "      <th>dept_support</th>\n",
       "      <th>dept_technical</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "      <th>satisfaction_level_type_High</th>\n",
       "      <th>satisfaction_level_type_Low</th>\n",
       "      <th>employee_type_Burdened</th>\n",
       "      <th>employee_type_Satisfactory</th>\n",
       "      <th>employee_type_unburdened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.53</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.86</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.88</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.87</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.52</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   last_evaluation  average_montly_hours  time_spend_company  Work_accident  \\\n",
       "0             0.53                   157                   3              0   \n",
       "1             0.86                   262                   6              0   \n",
       "2             0.88                   272                   4              0   \n",
       "3             0.87                   223                   5              0   \n",
       "4             0.52                   159                   3              0   \n",
       "\n",
       "   left  promotion_last_5years  dept_IT  dept_RandD  dept_accounting  dept_hr  \\\n",
       "0     1                      0        0           0                0        0   \n",
       "1     1                      0        0           0                0        0   \n",
       "2     1                      0        0           0                0        0   \n",
       "3     1                      0        0           0                0        0   \n",
       "4     1                      0        0           0                0        0   \n",
       "\n",
       "   ...  dept_support  dept_technical  salary_high  salary_low  salary_medium  \\\n",
       "0  ...             0               0            0           1              0   \n",
       "1  ...             0               0            0           0              1   \n",
       "2  ...             0               0            0           0              1   \n",
       "3  ...             0               0            0           1              0   \n",
       "4  ...             0               0            0           1              0   \n",
       "\n",
       "   satisfaction_level_type_High  satisfaction_level_type_Low  \\\n",
       "0                             0                            1   \n",
       "1                             1                            0   \n",
       "2                             0                            1   \n",
       "3                             1                            0   \n",
       "4                             0                            1   \n",
       "\n",
       "   employee_type_Burdened  employee_type_Satisfactory  \\\n",
       "0                       0                           0   \n",
       "1                       0                           1   \n",
       "2                       1                           0   \n",
       "3                       0                           1   \n",
       "4                       0                           0   \n",
       "\n",
       "   employee_type_unburdened  \n",
       "0                         1  \n",
       "1                         0  \n",
       "2                         0  \n",
       "3                         0  \n",
       "4                         1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_OneHot=pd.get_dummies(df,columns=[\"dept\", \"salary\", \"satisfaction_level_type\", \"employee_type\"])\n",
    "df_OneHot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_OneHot.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 17: Creating Independant and Dependant Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df['left']\n",
    "X=df.drop(['left'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 18: Perform Train Test Split with test size 30 percent and random state = 100**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write code here\n",
    "xtrain,xtest, ytrain, ytest = train_test_split(X, Y, test_size=0.3, random_state=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10499, 23) (10499,)\n",
      "(4500, 23) (4500,)\n"
     ]
    }
   ],
   "source": [
    "print(xtrain.shape, ytrain.shape)\n",
    "print(xtest.shape,ytest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 19: Get the predictions using the following models.**<br>\n",
    "- Random Forest\n",
    "- Logistic Regression\n",
    "- Ada Boost\n",
    "- XG Boost\n",
    "\n",
    "<br> \n",
    "\n",
    "**Also get the following scores for each of the above models**\n",
    "- Accuracy\n",
    "- Precision\n",
    "- Recall\n",
    "- F1-Score\n",
    "- Classification Report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the model from sklearn\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making instance and training the model\n",
    "rf=RandomForestClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "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",
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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>last_evaluation</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>dept_IT</th>\n",
       "      <th>dept_RandD</th>\n",
       "      <th>dept_accounting</th>\n",
       "      <th>dept_hr</th>\n",
       "      <th>dept_management</th>\n",
       "      <th>...</th>\n",
       "      <th>dept_support</th>\n",
       "      <th>dept_technical</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "      <th>satisfaction_level_type_High</th>\n",
       "      <th>satisfaction_level_type_Low</th>\n",
       "      <th>employee_type_Burdened</th>\n",
       "      <th>employee_type_Satisfactory</th>\n",
       "      <th>employee_type_unburdened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5220</th>\n",
       "      <td>0.50</td>\n",
       "      <td>185</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4819</th>\n",
       "      <td>0.91</td>\n",
       "      <td>202</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9065</th>\n",
       "      <td>0.66</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8696</th>\n",
       "      <td>0.98</td>\n",
       "      <td>241</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>0.86</td>\n",
       "      <td>254</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      last_evaluation  average_montly_hours  time_spend_company  \\\n",
       "5220             0.50                   185                   3   \n",
       "4819             0.91                   202                   2   \n",
       "9065             0.66                   180                   4   \n",
       "8696             0.98                   241                   2   \n",
       "5571             0.86                   254                   2   \n",
       "\n",
       "      Work_accident  promotion_last_5years  dept_IT  dept_RandD  \\\n",
       "5220              0                      0        0           0   \n",
       "4819              0                      0        0           0   \n",
       "9065              0                      0        0           0   \n",
       "8696              0                      0        1           0   \n",
       "5571              1                      0        0           1   \n",
       "\n",
       "      dept_accounting  dept_hr  dept_management  ...  dept_support  \\\n",
       "5220                0        1                0  ...             0   \n",
       "4819                0        0                0  ...             0   \n",
       "9065                0        0                0  ...             0   \n",
       "8696                0        0                0  ...             0   \n",
       "5571                0        0                0  ...             0   \n",
       "\n",
       "      dept_technical  salary_high  salary_low  salary_medium  \\\n",
       "5220               0            0           1              0   \n",
       "4819               0            0           0              1   \n",
       "9065               1            0           1              0   \n",
       "8696               0            0           0              1   \n",
       "5571               0            0           1              0   \n",
       "\n",
       "      satisfaction_level_type_High  satisfaction_level_type_Low  \\\n",
       "5220                             1                            0   \n",
       "4819                             1                            0   \n",
       "9065                             0                            1   \n",
       "8696                             1                            0   \n",
       "5571                             1                            0   \n",
       "\n",
       "      employee_type_Burdened  employee_type_Satisfactory  \\\n",
       "5220                       0                           1   \n",
       "4819                       0                           1   \n",
       "9065                       0                           1   \n",
       "8696                       0                           1   \n",
       "5571                       0                           1   \n",
       "\n",
       "      employee_type_unburdened  \n",
       "5220                         0  \n",
       "4819                         0  \n",
       "9065                         0  \n",
       "8696                         0  \n",
       "5571                         0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get predictions\n",
    "rf_pred=rf.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Precision**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the code to import the function for calculation of the specific score\n",
    "from sklearn.metrics import precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write the code to calculate\n",
    "rf_precision= precision_score(ytest,rf_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the code to import the function for calculation of the specific score\n",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9853333333333333\n"
     ]
    }
   ],
   "source": [
    "# Write the code to calculate\n",
    "rf_acc= accuracy_score(ytest, rf_pred)\n",
    "print(\"Accuracy:\", rf_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recall**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the code to import the function for calculation of the specific score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9654231119199272"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write the code to calculate\n",
    "rf_recall=recall_score(ytest,rf_pred)\n",
    "rf_recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**F1-Score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the code to import the function for calculation of the specific score\n",
    "from sklearn.metrics import f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9698354661791591"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write the code to calculate\n",
    "rf_f1_score=f1_score(ytest,rf_pred)\n",
    "rf_f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the code to import the function for calculation of the specific score\n",
    "from sklearn.metrics import classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not left       0.99      0.99      0.99      3401\n",
      "        left       0.97      0.97      0.97      1099\n",
      "\n",
      "    accuracy                           0.99      4500\n",
      "   macro avg       0.98      0.98      0.98      4500\n",
      "weighted avg       0.99      0.99      0.99      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Write the code to calculate\n",
    "target_names = ['not left', 'left']\n",
    "print(classification_report(ytest, rf_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the model\n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=100)"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create instance and train, random _state=100\n",
    "lr = LogisticRegression(random_state=100)\n",
    "lr.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the predictions\n",
    "lr_pred=lr.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8608888888888889"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write the code here\n",
    "lr_accuracy=accuracy_score(ytest,lr_pred)\n",
    "lr_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Precision**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7307317073170732"
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write the code here\n",
    "lr_precision=precision_score(ytest,lr_pred)\n",
    "lr_precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recall**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6815286624203821"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write the code here\n",
    "lr_recall=recall_score(ytest,lr_pred)\n",
    "lr_recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**F1 Score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7052730696798494"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write the code here\n",
    "lr_f1_score=f1_score(ytest,lr_pred)\n",
    "lr_f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not left       0.90      0.92      0.91      3401\n",
      "        left       0.73      0.68      0.71      1099\n",
      "\n",
      "    accuracy                           0.86      4500\n",
      "   macro avg       0.82      0.80      0.81      4500\n",
      "weighted avg       0.86      0.86      0.86      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Write the code here\n",
    "target_names = ['not left', 'left']\n",
    "print(classification_report(ytest, lr_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ada Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write the code here to import the model\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(random_state=100)"
      ]
     },
     "execution_count": 574,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write the code here to make an instance and train the model with random state =100\n",
    "clf_adb=AdaBoostClassifier(random_state=100)\n",
    "clf_adb.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predictions\n",
    "pred_clf_adb=clf_adb.predict(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.928"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "ab_accuracy= accuracy_score(ytest,pred_clf_adb)\n",
    "ab_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Precision**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8611369990680335"
      ]
     },
     "execution_count": 579,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "ab_precision= precision_score(ytest,pred_clf_adb)\n",
    "ab_precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recall**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8407643312101911"
      ]
     },
     "execution_count": 581,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "ab_recall= recall_score(ytest,pred_clf_adb)\n",
    "ab_recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**F1-Score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.850828729281768"
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "ab_f1_score=f1_score(ytest,pred_clf_adb)\n",
    "ab_f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not left       0.95      0.96      0.95      3401\n",
      "        left       0.86      0.84      0.85      1099\n",
      "\n",
      "    accuracy                           0.93      4500\n",
      "   macro avg       0.91      0.90      0.90      4500\n",
      "weighted avg       0.93      0.93      0.93      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Write code here\n",
    "target_names = ['not left', 'left']\n",
    "print(classification_report(ytest, pred_clf_adb, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XG Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\anaconda\\lib\\site-packages (1.6.2)\n",
      "Requirement already satisfied: numpy in c:\\anaconda\\lib\\site-packages (from xgboost) (1.18.1)\n",
      "Requirement already satisfied: scipy in c:\\anaconda\\lib\\site-packages (from xgboost) (1.4.1)\n"
     ]
    }
   ],
   "source": [
    "#Write the code here to import the model\n",
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_xgb = xgb.XGBClassifier(seed=25,nthread=1,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=1, nthread=1, num_parallel_tree=1, predictor='auto',\n",
       "              random_state=100, reg_alpha=0, ...)"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write the code here to make an instance and train the model with random state =100\n",
    "clf_xgb.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predictions\n",
    "xgb_pred = clf_xgb.predict(xtest)\n",
    "xgb_pred_prb=clf_xgb.predict_proba(xtest)[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9795555555555555"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "xgb_accuracy= accuracy_score(ytest,xgb_pred)\n",
    "xgb_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Precision**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9615032080659945"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "xgb_precision= precision_score(ytest,xgb_pred)\n",
    "xgb_precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recall**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9545040946314831"
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "xgb_recall= recall_score(ytest,xgb_pred)\n",
    "xgb_recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**F1-Score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9579908675799086"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write code here\n",
    "xgb_f1_score=f1_score(ytest,xgb_pred)\n",
    "xgb_f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification Report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not left       0.99      0.99      0.99      3401\n",
      "        left       0.96      0.95      0.96      1099\n",
      "\n",
      "    accuracy                           0.98      4500\n",
      "   macro avg       0.97      0.97      0.97      4500\n",
      "weighted avg       0.98      0.98      0.98      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Write code here\n",
    "target_names = ['not left', 'left']\n",
    "print(classification_report(ytest, xgb_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result Comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 20: Do the comparison of the above used models as per the scores found.Make a datafram that shows the models and scores for each models.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the code here\n",
    "comparison_dict = {\"Algorithm\": [\"Random Forest\", \"Logistic Regression\", \"AdaBoost\", \"XG Boost\"],\n",
    "                   \"Accuracy\": [rf_acc, lr_accuracy, ab_accuracy,  xgb_accuracy],\n",
    "                   \"Precision\": [rf_precision, lr_precision, ab_precision,  xgb_precision],\n",
    "                   \"Recall\": [rf_recall, lr_recall, ab_recall, xgb_recall],\n",
    "                   \"F1 Score\": [rf_f1_score, lr_f1_score, ab_recall,  xgb_recall]\n",
    "                   }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.985333</td>\n",
       "      <td>0.974288</td>\n",
       "      <td>0.965423</td>\n",
       "      <td>0.969835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XG Boost</td>\n",
       "      <td>0.979556</td>\n",
       "      <td>0.961503</td>\n",
       "      <td>0.954504</td>\n",
       "      <td>0.954504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.928000</td>\n",
       "      <td>0.861137</td>\n",
       "      <td>0.840764</td>\n",
       "      <td>0.840764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.860889</td>\n",
       "      <td>0.730732</td>\n",
       "      <td>0.681529</td>\n",
       "      <td>0.705273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Algorithm  Accuracy  Precision    Recall  F1 Score\n",
       "0        Random Forest  0.985333   0.974288  0.965423  0.969835\n",
       "3             XG Boost  0.979556   0.961503  0.954504  0.954504\n",
       "2             AdaBoost  0.928000   0.861137  0.840764  0.840764\n",
       "1  Logistic Regression  0.860889   0.730732  0.681529  0.705273"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pd.DataFrame(comparison_dict)\n",
    "comparison.sort_values(['Recall', 'Accuracy'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task 21: Which model has the best score? Do you think that you need to apply any sort of tunning on the model selected. If Yes, then apply it conclude with the final scores of the best model.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest have accuracy of 0.985333 precision of 0.974288 most importantly recall of 0.965423 and F1score of 0.969835,thus Random forest without any  tunning on the model is best selected."
   ]
  }
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