Mentormind_Internship_2_barplots_Akshay_Gulhane.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd99fee3",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Employee retention strategies are integral to the success and well-being of a company. There are often many reasons why employees leave an organization, and in this project, I will explore some of the key drivers of employee attrition. Employee attrition measures how many workers have left an organization and is a common metric companies use to assess their performance. While turnover rates vary from industry to industry, the Bureau of Labor Statistics reported that among voluntary separations the overall turnover rate was 25% in 2022.\n",
    "\n",
    "In this notebook, I will explore dataset on HR Analytics. The data consists of nearly 1,500 current and former employees with information related to their job satisfaction, work life balance, tenure, experience, salary, and demographic data. Below is a brief overview and summary statistics of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ee15210",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0c6818c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import seaborn as sns\n",
    "import plotly\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3650f398",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fadc0da",
   "metadata": {},
   "outputs": [],
   "source": [
    "hr_data = pd.read_csv(r'C:\\Users\\ASUS\\Downloads\\WA_Fn-UseC_-HR-Employee-Attrition.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "407ad3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                  4                        0                     5  \n",
       "1                  7                        1                     7  \n",
       "2                  0                        0                     0  \n",
       "3                  7                        3                     0  \n",
       "4                  2                        2                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775f5f1f",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a72fae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Cleaning \n",
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e89f6e5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   object\n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 402.1+ KB\n"
     ]
    }
   ],
   "source": [
    "hr_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "30a9ccac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         0\n",
       "Attrition                   0\n",
       "BusinessTravel              0\n",
       "DailyRate                   0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EmployeeCount               0\n",
       "EmployeeNumber              0\n",
       "EnvironmentSatisfaction     0\n",
       "Gender                      0\n",
       "HourlyRate                  0\n",
       "JobInvolvement              0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "JobSatisfaction             0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "MonthlyRate                 0\n",
       "NumCompaniesWorked          0\n",
       "Over18                      0\n",
       "OverTime                    0\n",
       "PercentSalaryHike           0\n",
       "PerformanceRating           0\n",
       "RelationshipSatisfaction    0\n",
       "StandardHours               0\n",
       "StockOptionLevel            0\n",
       "TotalWorkingYears           0\n",
       "TrainingTimesLastYear       0\n",
       "WorkLifeBalance             0\n",
       "YearsAtCompany              0\n",
       "YearsInCurrentRole          0\n",
       "YearsSinceLastPromotion     0\n",
       "YearsWithCurrManager        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "6476445d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 0 missing values in the data.\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {} missing values in the data.\".format(hr_data.isnull().sum().sum()))      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82dc23c",
   "metadata": {},
   "source": [
    "There are no missing values in dataset so we can proceed with visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09af7293",
   "metadata": {},
   "source": [
    "# Visualizing metrics using barplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "b8a45449",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be8854d730>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2183.88x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 1 (Attrition of employees for EducationField v/s MonthlyIncome gender wise.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale=1.3)\n",
    "sns.catplot(x=\"EducationField\", y=\"MonthlyIncome\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=8, aspect=1.3, palette='bwr',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fda4c36",
   "metadata": {},
   "source": [
    "CONCLUSION  \n",
    "In barplot no 1 mostly attirition of male employees are from marketing field with monthly income less than 6000 and for female employees it is also from marketing field with monthly income around 7000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "eff9a18f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bea23504c0>"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pw7xx48apVatWDh+33HKLfbnVatWcOXPUtWtXtW/fXgMGDFB6errDGLt27VJCQoI6dOigbt26KSkpyWF5dcYAAAAAAE/jMYVuyZIlmjNnToX5u3fv1tChQ/Xtt9/aP9asWWNfPm/ePC1fvlyvvvqqVqxYIZPJpEGDBqm4uFiSlJOTo/79++vKK6/UqlWrNHz4cM2ePVurVq2q9hgAAAAA4IncXuiOHDmiJ554QrNnz1bz5s0dllksFqWlpalt27YKDw+3f4SGhkqSiouLtWjRIg0fPlyxsbFq3bq1Zs2apSNHjmj9+vWSpI8++kg+Pj6aNGmSWrRoofj4ePXr108LFiyo9hgAAAAA4IncXuh++eUXNWjQQJ9++qnat2/vsGz//v0qKipSixYtKt02NTVVp06dUkxMjH1eUFCQoqKitGXLFknS1q1bFR0dLW9vb/s6MTEx2rdvn44dO1atMQAAAADAE3lXvUrt6t69u7p3717psj179shkMik5OVlff/21zGazYmNjNXLkSAUGBiorK0uS1LhxY4ftGjZsqMOHD0uSsrKyFBkZWWG5JGVmZlZrDAAAAADwRG4vdOfy66+/ymw2q0mTJpo/f77S09P12muvac+ePUpOTlZBQYEkycfHx2G7+vXrKy8vT5JUWFhY6XJJKioqqtYYzvL2dvsBUAAAANQhk8nkMO2Jvw/WVUYvL8957efKYpScZ+PRhW748OHq16+fgoKCJEmRkZEKDw/XQw89pB07dsjX11dS2XVw5dNSWVHz8/OTJPn6+la4uUlRUZEkyd/fv1pjOMNsNikk5BKntwcAAIDxmM0mh2lP/H3QCBldLSjI+d/r65IzOT260JlMJnuZK1d++mRWVpb9NMns7GxdccUV9nWys7PVunVrSVJERISys7Mdxij/vFGjRiotLa1yDGdYrTbl5592ensAAAC4h8lkcig9NWG12hym8/MLnB7HZrNVvaKTY585nZNzqlb+HS8vs8cUqfz8Alks1kqXeWrO6hZtjy50o0ePVm5ursNz43bs2CFJatmypS6//HIFBARo8+bN9jKWn5+vnTt3KiEhQZIUHR2t5cuXy2KxyMvLS5K0adMmNW/eXGFhYQoMDKxyDGeVllb+TQMAAADPZDabFBLsK/P/fm90Zvszp50tClaLRTm5BQ7ly1XOLIo2m+2i+J3VYrEa4nU6k9OjC13v3r315JNP6p133lGvXr20b98+vfzyy+rdu7f9zpcJCQmaOXOmQkND1aRJE82YMUMRERHq2bOnJCk+Pl4LFy7U+PHj9cQTTyglJUXJycmaPHmypLJr56oaAwAAABcHs9kks5eXfly5RCezs2q8feGJPIfpb+ZOr/EYAQ0j1PGBfjKbTbVS6HBh8ehCd+utt2r27NmaP3++5s+fr8DAQN19990aOXKkfZ0RI0aotLRUEyZMUGFhoaKjo5WUlGS/yUlYWJgWLlyoKVOmKC4uTuHh4RozZozi4uKqPQYAAAAuLiezs5R/+GCNt7NZSh2mnRkDqAmPKnTTp1f8C8Ydd9yhO+6446zbeHl5KTExUYmJiWddp127dlqxYsV5jQEAAAAAnsZz7tEJAAAAAKgRCh0AAAAAGBSFDgAAAAAMikIHAAAAAAZFoQMAAAAAg6LQAQAAAIBBUegAAAAAwKAodAAAAABgUBQ6AAAAADAoCh0AAAAAGBSFDgAAAIDHaRjawD7dKDTYfUE8HIUOAAAAgMe5u1uMrm7WRFc3a6Le3W5wdxyP5e3uAAAAAADwR+GhDTQw/g53x/B4HKEDAAAAAIPiCB0AAEAtyszM0JIlCyRJ/foN0mWXNXFzIgAXEo7QAQAA1KLk5CSlpGxXSsp2LV2a5O44AC4wFDoAAIBalJFx0D596NDBc6wJADVHoQMAAAAAg6LQAQAAAIBBUegAAAAAwKAodAAAAABgUDy2AAAAADAgs9kks9lU4+1MJpPDtLe388d4rFabrFab09vj/FHoAAAAAIMxm00KCfGX2VzzMnZmCSwb5xKnc1itVuXknKbUuRGFDgAAADCYsqNzZu3cuFqncn6r0bZFp084TG/5+D2nMlwScqmibrtPZrOJQudGFDoAAADAoE7l/KaTv2XVaBubxeIwXdPt4Vm4KQoAAAAAGBSFDgAAAAAMikIHAAAAAAZFoQMAAAAAg+KmKAAAAPB4mZkZWrJkgSSpX79BuuyyJm5OBHiG8z5Cd+LECe3du1fFxcWynHHHHAAAAMBVkpOTlJKyXSkp27V0aZK74wAew+lCt3nzZj3wwAPq3Lmz7r77bv36668aPXq0pk+f7sp8AAAAgDIyDtqnDx06eI41gYuLU4Vu06ZNGjhwoHx9ffXcc8/JZit7kGBUVJSWLl2qxYsXuzQkAAAAAKAipwrdm2++qdtuu03vv/+++vbtay90gwcP1hNPPKGVK1e6NCQAAAAAoCKnCt2uXbsUHx8vSTKZTA7LunTpooyMjPNPBgAAAAA4J6cKXWBgoI4ePVrpssOHDyswMPC8QgEAAAAAquZUobvttts0a9Ys7dixwz7PZDIpKytL8+fPV7du3VyVDwAAAABwFk49h2706NH66aef9OCDD+rSSy+VJD377LPKyspS48aN9eyzz7o0JAAAAACgIqeO0DVo0EArV67U5MmTFR0drZtuukmtWrVSYmKiVq9erdDQUKfCzJs3T3369HGY989//lPx8fHq2LGjunfvrtdee02FhYX25RkZGWrVqlWFjzNvzLJr1y4lJCSoQ4cO6tatm5KSHJ9dYrVaNWfOHHXt2lXt27fXgAEDlJ6e7tRrAAAAAIC64tQROkny8fHRgw8+qAcffNAlQZYsWaI5c+YoOjraPm/r1q16+umnNXLkSN1xxx1KT0/XSy+9pNzcXE2bNk2StHv3btWvX18bNmxwuEFL+XV8OTk56t+/v3r06KHJkydr+/btmjx5soKDg+03dpk3b56WL1+uadOmqVGjRpoxY4YGDRqktWvXysfHxyWvDwAAAABczelCt2PHDv3444/Kz8+vsMxkMumpp56q1jhHjhzR+PHjtW3bNjVv3txh2fLlyxUTE6PBgwdLkpo1a6ZRo0bphRde0OTJk+Xj46M9e/aoefPmatiwYaXjf/TRR/Lx8dGkSZPk7e2tFi1aKD09XQsWLFB8fLyKi4u1aNEiJSYmKjY2VpI0a9Ysde3aVevXr1evXr1qslsAAAAAoM44VeiSk5M1ffp0+/Pn/qgmhe6XX35RgwYN9Omnn2ru3LkOjzwYMGCAzOaKZ4WWlpbq5MmTCg0N1e7du9WyZcuzjr9161ZFR0fL2/v3lxoTE6N3331Xx44dU0ZGhk6dOqWYmBj78qCgIEVFRWnLli0UOgAAAAAey6lCt3jxYt1222169dVXFRwcfF4Bunfvru7du1e6LCoqyuHz4uJiLV68WNdee639Or09e/YoPDxcjz76qPbv369mzZpp2LBh6tq1qyQpKytLkZGRDuOUH83LzMxUVlaWJKlx48YV1jl8+PB5vTZvb6cuUQQAABeQMy8JMZlM/H7gpLraj15envP1OVcWI+Q0QsaqltU1Z7I4Vejy8vL0yCOPnHeZq4nS0lKNGTNGaWlp+vDDDyWVFbz9+/fLz89PY8aMkb+/vz799FMNGjRIixcv1o033qjCwsIK18HVr19fklRUVKSCggJJqnSdvLw8p/OazSaFhFzi9PYAAODCYDabHKb5/cA5F+N+DAryc3eEajFCTiNklJzL6VShu/nmm/Xjjz+qS5cuzmxeYydPntTIkSO1efNmzZkzR+3bt5dUVsK2bNkib29veyFr06aN9u7dq6SkJN14443y9fVVcXGxw3hFRUWSJH9/f/n6+koqK4fl0+Xr+Pk5/4W3Wm3Kzz/t9PYAAODCYLXaHKZzck65MY1x1dV+9PIye8wv//n5BbJYrJUuM0JOI2SUPDdndf9o4VShe+mll/T4448rIyND7dq1q7T4/PnPf3Zm6Aqys7M1aNAgHTp0SAsWLHC41k0qK2V/FBkZqW+//VaSFBERoezs7ApjSlKjRo1UWlpqn3fFFVc4rNO6devzyl5aWvk3DQAAni4zM0NLliyQJPXrN0iXXdbEzYmM68x7DthsNn4/cNLFuB8tFqshXqcRchoho+RcTqcK3ZdffqkDBw5o3759+utf/1phuclkckmhy8vLU9++fXXy5EktW7ZMrVq1cliempqqRx55RAsWLFCnTp3s83/++Wf7jVKio6O1fPlyWSwWeXl5SZI2bdqk5s2bKywsTIGBgQoICNDmzZvthS4/P187d+5UQkLCeb8GAACMKDk5SSkp2yVJS5cmaezYl9wbCABQKacK3bx583TDDTfomWee0aWXXurqTHbTpk3TwYMHtXDhQoWGhuro0aP2ZaGhoYqMjNTVV1+tyZMna+LEiQoJCdFHH32k7du36+OPP5YkxcfHa+HChRo/fryeeOIJpaSkKDk5WZMnT5ZUdtpmQkKCZs6cqdDQUDVp0kQzZsxQRESEevbsWWuvDQDKcSQEnigj46B9+tChg+dYEwDgTk4VuuPHj2vq1Kn2a9lqg9Vq1bp161RSUqK+fftWWL5x40Y1bdpU8+fP18yZMzVy5Ejl5+crKipKixcvth/NCwsL08KFCzVlyhTFxcUpPDxcY8aMUVxcnH2sESNGqLS0VBMmTFBhYaGio6OVlJTEQ8UB1AmOhAAAAGc5Vejat2+vPXv26MYbb3RpmOnTp9unzWazUlJSqtwmNDRUU6dOPec67dq104oVK8663MvLS4mJiUpMTKx+WABwEY6EAAAAZzlV6IYNG6bRo0fr+PHj6tChgwICAiqsEx0dfd7hAAAAAABn51Sh69evnyTp3XffleT4oEebzSaTyaRdu3adfzoAAAAAwFk5VeiWLl3q6hwAAAAAgBpyqtB17tzZ1TkAAAAAADXkVKGTpH379umtt97S5s2blZ+fr5CQEHXq1ElPPfWUWrRo4cqMAAAAAIBKOFXo0tLS9PDDD8vb21u33nqrLr30Uh09elT/+te/9OWXX2rlypWUOgAAAACoZU4VupkzZ6pp06Z6//33FRgYaJ9/4sQJ9e3bV7NmzdLbb7/tspAAAAAAgIrMzmy0ZcsWDR061KHMSVJgYKAGDx6sLVu2uCQcAAAAAODsnCp03t7e8vHxqXSZj4+PiouLzysUAAAAAKBqThW6tm3b6sMPP5TNZnOYb7PZ9MEHH6hNmzYuCQcAAAAAODunrqF75pln9Mgjj6h379668847FR4erqNHj+qzzz5Tenq6Fi9e7OqcAAAAAIA/cKrQtW3bVgsXLtTrr7+uuXPnymazyWQyqU2bNlqwYIGio6NdnRMAAAAA8AdOP4cuJiZGy5YtU2lpqfLz83XJJZeotLRUwcHBLowHAAAAADgbp66hKy4u1oQJE/Tggw/Kz89PjRo1UkpKim6++WZNmTJFFovF1TkBAAAAAH/gVKGbM2eO1q1bpz//+c/2eddee62ef/55/fWvf9WCBQtclQ8AAAAAcBZOFbq///3vev7559W3b1/7vAYNGqhPnz4aNWqUVq1a5bKAAAAAgFGE+tWvdBqoLU5dQ5eTk6OmTZtWuqx58+Y6cuTIeYUCAABA3cnMzNCSJWVnWPXrN0iXXdbEzYmMq9uVDfWvfdn2aaC2OXWErkWLFvriiy8qXbZ+/Xo1a9bsvEIBAACg7iQnJyklZbtSUrZr6dIkd8cxtFC/+oqPulzxUZdzhA51wqkjdAMGDNDo0aOVm5urHj16KCwsTMePH9eGDRv0j3/8Q9OmTXN1TgAAANSSjIyD9ulDhw6eY00AnsapQterVy+dOHFCb7/9tv7xj3/Y54eEhOjFF190uFkKAAAAAKB2OP0cuocfflgPPfSQ9u3bp9zcXAUFBemqq66S2ezUWZwAAAAAgBpyutBJkslk0lVXXeWqLAAAAACAGnCq0B0/flxTpkzRl19+qYKCAtlsNoflJpNJO3fudElAAAAAAEDlnCp0kyZN0ldffaVevXopIiKC0ywBAAAAwA2cKnTffPONXnjhBT300EOuzgMAAAAAqCanDq35+Pjo8ssvd3UWAAAAAEANOFXoevbsqbVr17o6CwAAAACgBpw65TIqKkpvvvmmDh48qPbt28vX19dhuclk0lNPPeWSgAAAAACAyjlV6F5++WVJ0pYtW7Rly5YKyyl0AAAAAFD7nCp0qamprs4BAAAAAKih83qwOACgjNlsktlscmpbk8nkMO3t7dyjYKxWm6xWW9UrAgCAC0a1C92aNWtqNPCf//znGkYBAGMym00KDvaXl5dzRezMImg2mxQScolT41gsVuXmnqbUAQBwEal2oRs7dmy1BzWZTBQ6ABcNs9kkLy+z3p3+hQ4fPF7j7XOPnXKYnvTU/9V4jMaXh2rI2DtkNpsodB4uMzNDS5YskCT16zdIl13WxM2JAABGVu1Ct3HjxtrMAQCGd/jgcaWnHa3xdqWlFodpZ8aAcSQnJyklZbskaenSJI0d+5J7AwEADK3aha5Jk9//glhYWFjhUQUAAKBqGRkH7dOHDh08x5oAAFTNqZui3HTTTbrrrrt033336brrrnN1JgAA4ALcrMd12JcAPJVThW7o0KH65JNP9PHHH6tZs2a67777dO+99yoiIsLV+QAAgBPMZpOCG/jJy9vL6e3PnHb6Zj2lFuXmFRi6iJTd+MhPXl5u3pcWi3Jzjb0vAbieU4Vu8ODBGjx4sFJSUrR69WotWrRIc+bMUUxMjOLj49WjRw/5+Pi4OisAAKgms9kkL28vvfLUZKX/ur/G2x+z/Pb79JHf9MTt/Ws8RrOrr9SLcyca/mY9ZTc+8tKK8W8qe9+hGm+ffzTHYfqtR5+r8RgNmzfVQ1NGGn5fAnC983oOXbt27dSuXTuNHz9e3333nZKSkjR69GgFBgbq3nvv1eOPP67LL7+82uPNmzdPmzZt0vvvv2+ft2vXLk2ZMkU///yzgoOD1adPHw0cONC+3Gq16u2339bKlSuVn5+v66+/XhMnTlSzZs1cOgYAAEaU/ut+7dmxp8bbebesL5NP2ZGlkuJS7Umr+RgXmux9h5SZ+t8ab2cJKpH+d3DPUlLi1BgAcDbOncR9hszMTCUlJWnWrFnasmWLmjdvrvj4eP3nP/9Rr169tHbt2mqNs2TJEs2ZM8dhXk5Ojvr3768rr7xSq1at0vDhwzV79mytWrXKvs68efO0fPlyvfrqq1qxYoVMJpMGDRqk4uJil40BAAAAAJ7IqSN0J0+e1BdffKE1a9Zo27Zt8vX11Z/+9CdNnDjRfpOU559/XkOGDNH06dPVu3fvs4515MgRjR8/Xtu2bVPz5s0dln300Ufy8fHRpEmT5O3trRYtWig9PV0LFixQfHy8iouLtWjRIiUmJio2NlaSNGvWLHXt2lXr169Xr169XDIGAAAAAHgip47QdenSRRMmTFBJSYlefvllffvtt5o6dWqFO162bdvW4c5Olfnll1/UoEEDffrpp2rfvr3Dsq1btyo6Olre3r/3zpiYGO3bt0/Hjh1TamqqTp06pZiYGPvyoKAgRUVFacuWLS4bAwAAAK5hNpfd6bOmH5XdLdTZD2fvWAp4IqeO0D322GO6//77ddVVV51zvf79++vJJ5885zrdu3dX9+7dK12WlZWlyMhIh3kNGzaUVHaqZ1ZWliSpcePGFdY5fPiwy8ZwlrO3JQZgLF5envN/3ZOyoHKuuoV9VTzpe8GTsjjDk/LXVpa6+r40mUwKCqwvsxN3DHXV3UIlyWqxKP9EkWy2ijeYMcrX2wg5jZCxqmV1zZksThW6MWPGVGu9Sy5x/j+aVPYA8z/eLbN+/fqSpKKiIhUUFEhSpevk5eW5bAxnnO8PGgBwRlCQn7sjoAqu/KXUKPi+dJ3a2pd1/X357/fmKz8zs0bbFOTmOkx/Puklp/7toMsu002Dhyo42N+p7euSUf7vGCGnETJKzuWsdqHr3r17ladPljOZTNqwYUONw/yRr69vhRuTFBUVSZL8/f3l6+srSSouLrZPl6/j5+fnsjGcYbXalJ9/2untARiHl5fZY94o8vMLZLFY3R0D53DmLeetVptyck7Vyr/D96XrXAz7sq6/L/MzM5VzIL1G21pLSx2ma7r9H51tXxrl622EnEbIKHluzur+YaXaha5z587VLnSuEhERoezsbId55Z83atRIpf/7j52dna0rrrjCYZ3WrVu7bAxnlZYa980LgDFZLFZ+9ni4M0/xstlsF8XXi+9L16mtfcn3pWcyQkbJGDmNkFFyLme1C9306dNrHOh8RUdHa/ny5bJYLPL637nWmzZtUvPmzRUWFqbAwEAFBARo8+bN9jKWn5+vnTt3KiEhwWVjAAAAAIAnOq8rAE+dOqWsrCxlZmZW+HCF+Ph4nTx5UuPHj1daWppWr16t5ORkDRkyRFLZdW8JCQmaOXOmNm7cqNTUVI0aNUoRERHq2bOny8YAAAAALhSXBvmfMX3hX8d7oXPqpigHDhzQs88+q19++eWs6+zatcvpUOXCwsK0cOFCTZkyRXFxcQoPD9eYMWMUFxdnX2fEiBEqLS3VhAkTVFhYqOjoaCUlJdlvcuKKMQAAAIALxe0dWuqLH9P+N93CzWlwvpwqdJMnT9aBAwc0ZMgQXX755TKbXXOrz8pO62zXrp1WrFhx1m28vLyUmJioxMTEs67jijEAAJ4vMzNDS5YskCT16zdIl13WxM2JAMDzhAX669Fb2rk7BlzEqUL3ww8/aOLEifrzn//s4jgAADgvOTlJKSnbJUlLlyZp7FjnbmsOAIBROFXoLrnkEoWHh7s6CwAA5yUj46B9+tChg+dYExcCjsgCgJM3Rbn33nu1dOlSWSwWV+cBAAColvIjsikp27V0aZK74wCAW1T7CN24cePs06Wlpfrmm2/Us2dPtWvXrsIDuE0mk6ZOneq6lAAAAH/AEVkAqEGh27x5s8PnERERkqSUlBTXJgIAAAAAVEu1C90///nP2swBAAAAAKghp66hGzdunA4erPzUhv/+978aOnToeYUCAAAAAFSt2kfoMjMzJUk2m01//etf1aNHD3l5eVVY7+uvv9a///1v1yUEAAAAAFSq2oXu5Zdf1ldffSWp7KYnTz/9dKXr2Ww2denSxTXpAADwUGazSWazqcbbmUwmh2lvb6dOlpEkWa02Wa02p7cHABhftQvd5MmT9e9//1s2m00vvPCCnnzySV1xxRUO65jNZgUFBemGG25weVAAADyF2WxScAM/eXlXPFOlOtueOR0SconTOSylFuXmFVDqAOAiVu1C16hRI8XFxUkq+4tibGysQkNDay0YAACeymw2ycvbS2OfmqB9aftqtO3x0qP26aNHjuqhOx5zKkPzls01fe6rMptNFDoAuIhVu9CdKS4uTjabTTt37tTp06dls1V8I4mOjj7vcAAAeLJ9afu0a0dqjbYJbREkL5+y0yxLiku1a1fNtgcA4ExOFbqUlBQ988wzysrKkiR7oTOZTLLZbDKZTNq1a5frUgIAAAAAKnCq0E2dOlXe3t6aNm2aIiIiZDY7f0E3AAAAAMA5ThW6nTt36o033lCPHj1cnQcAAAAAUE1OHVoLCwvjqBwAuEg9U2Cl0wAAAFVxqpU9+uijeu+993T69GlX5wGAi06IuY18TeHyNYUrxNzG3XEAAICBOHXKZXp6uvbu3asuXbro6quvlq+vr8Nyk8mk5ORklwQEgAtdPVOgGnrd5O4YAADAgJwudK1bt7Z//sfHFlT2GAMAAAAAgGs5Vejef/99V+cAAAAAANSQU4Wu3N69e/X999/rxIkTCgkJ0fXXX6+rrrrKVdkAABchs9kks9nk1LYmk8lh2tvbuRt4Wa02Wa2cbQIA8HxOFTqbzaaJEydq5cqVDqdXmkwmxcXFacqUKQ5vqgAAVIfZbFJwAz95eXs5vf2Z0yEhlzg1jqXUoty8AkodAMDjOVXoFi5cqFWrVmnEiBG65557FB4eruzsbH3yySd65513dPXVV6t///6uzgoAuMCZzSZ5eXvp5adnKP3XgzXe/rfS479PHzmugXeMqPEYza6+XC+9nSiz2UShAwB4PKcK3ccff6wnnnhCTz75pH1e06ZN9dRTT6mkpEQrV66k0AEAnJb+60Ht+Xlvjber16JEpnpl06UlJdqTWvMxAAAwEqcuLjh8+LBiYmIqXXbDDTfo0KFD5xUKAAC4l63Y+vt0kfUcawIA3MmpQtekSROlpqZWumznzp0KDQ09r1AAAMC9LFmlsp60yHrSIsuRUnfHAQCchVOnXPbu3VtvvfWWGjZsqDvvvFNms1lWq1Xr1q3T3Llz9fDDD7s6JwAAqEvFNlkOlLg7BWrAE+4QK3GXWKCuOVXoBg0apK1bt2r06NF6/vnnFRwcrNzcXJWWluqGG27QM8884+qcAAAAOAuz2aSQYD+Zvdx7h1hJslosysnlLrFAXXGq0Pn4+Gjx4sX6+uuv9f333ysvL08NGjRQdHS0YmNjXZ0RAAAA52A2m2T28tIXf3lLOQcyarz9qeM5DtPLnx7rVI6QK5rojjHDuUssUIeqXejGjRt3zuXHjh3T559/rs8//1wmk0lTp04973AAAACovpwDGTq6d3+Nt7OUlDpMOzMGAPeodqH761//KpPJpEaNGslsPvd51TxUHAAAAABqX7UL3Z133qkvv/xSRUVFuvPOO9WrVy9df/31tZkNAAAAAHAO1S50s2bNUmFhof75z39q3bp16t+/v8LCwtSrVy/16tVL11xzTW3mBAAAAAD8QY1uiuLr66u77rpLd911l06ePKn169dr3bp1WrJkiZo2barevXvrrrvu0lVXXVVbeQEAAAAA/+PUXS4lKSAgQHFxcYqLi1Nubq7Wr1+vzz77TPPnz1dkZKRWr17typwAADfKzMzQkiULJEn9+g3SZZc1cXMi47IUW+TlU3Ytemmxxc1p3I9npwHA+XG60J3p9OnTOnnypE6fPi2LxaKMjJrfLhcA4LmSk5OUkrJdkrR0aZLGjn3JvYEM7OSRAgX8b/rUkQK3ZnE3s9mk4AZ+8vJ2/7PTLKUW5ebx7DQAxuN0ocvKytLnn3+uzz77TCkpKQoICNBtt92mJ598Ul26dHFlRgCAm2VkHLRPHzp08BxroiqWYqvyDp5ydwyPYDab5OXtpXmj/qLMvQdqvH3u6eO/T2cf14R7nnYqx2UtrtCwWWN4dhoAQ6pRoTty5Ig+++wzff7559q+fbv8/f116623avDgweratat8fHxcHnDz5s16/PHHK13WtGlTbdy4UePGjatwimejRo309ddfS5KsVqvefvttrVy5Uvn5+br++us1ceJENWvWzL7+rl27NGXKFP38888KDg5Wnz59NHDgQJe/HgAA4Chz7wHt/2VvjbcrbVpi/02mtKTEqTEAwOiqXegeeeQR/fTTT6pfv75iY2M1Z84cxcbGqn79+rWZTx07dtS3337rMG/Pnj0aPHiwhg4dKknavXu3hg4dqoSEBPs6Xl6/n74xb948LV++XNOmTVOjRo00Y8YMDRo0SGvXrpWPj49ycnLUv39/9ejRQ5MnT9b27ds1efJkBQcHKz4+vlZfHwAAAAA4q9qF7scff5TZbFbLli11/PhxffDBB/rggw8qXddkMik5OdklAX18fBQeHm7/vKSkRNOmTdPtt9+uBx54QBaLRWlpaRo2bJjDeuWKi4u1aNEiJSYmKjY2VlLZIxi6du2q9evXq1evXvroo4/k4+OjSZMmydvbWy1atFB6eroWLFhAoQMAAADgsap9O6jo6Ghdf/318vX1lc1mO+eH1WqttcAffvihDh8+rHHjxkmS9u/fr6KiIrVo0aLS9VNTU3Xq1CnFxMTY5wUFBSkqKkpbtmyRJG3dulXR0dHy9v6938bExGjfvn06duxYrb0WAAAAADgf1T5C9/7779dmjmopKirS/Pnz1bdvXzVs2FBS2emX5UcEv/76a5nNZsXGxmrkyJEKDAxUVlaWJKlx48YOYzVs2FCHDx+WVHaDl8jIyArLJSkzM1NhYWG1/dIAAC5gKzLLVM9qnwYA4ELnkscW1JVPPvlERUVF6tOnj33er7/+KrPZrCZNmmj+/PlKT0/Xa6+9pj179ig5OVkFBWW3hP7jDVvq16+vvLw8SVJhYWGly6WyEums83keDgDj8PLynP/rtZXFlc/7OpfzzV+a7S1vldqnayuLEb7mZKw5I+Q0QkapdnIG1/PWSYvlf9P1nB6nqiyetC/5OeQ6Rt+X52KoQrdmzRrdfvvtCgkJsc8bPny4+vXrp6CgIElSZGSkwsPD9dBDD2nHjh3y9fWVVHYtXfm0VFbU/Pz8JEm+vr4qLi52+LfKi5y/v79TWc/3eTgA4IygIL9aGdeVz/uqVcVmlR5yzR2Xa2tfupoRchoho2SMnEbIKNVOzpjQYG06nvu/6QbnPZ4R9qURMkrGyGmEjJJzOQ1T6I4fP64ff/xRQ4YMcZhvMpnsZa5c+emTWVlZ9lMts7OzdcUVV9jXyc7OVuvWrSVJERERys7Odhij/PNGjRo5lddqtSk//7RT2wIwFi8vs8e8UeTnF8hicf11zGc+m8tqtSknp3aeo2aUfWmEnGSsOSPkNEJGqXZyBterpzsbVbwBnrOMsC/5OeQ6RtyX1f3jqWEK3Q8//CCTyaTOnTs7zB89erRyc3OVlJRkn7djxw5JUsuWLXX55ZcrICBAmzdvthe6/Px87dy50/6Yg+joaC1fvlwWi8X+uINNmzapefPm53X9XGlp7d0cBgAqY7FYa+Vnj81mc5i+GH6+1da+dDUj5DRCRskYOY2QUTJGTjK6jhFyGiGj5FxOzzlhtAqpqam6/PLL7adJluvdu7e+++47vfPOOzpw4IC++uorvfDCC+rdu7datGghHx8fJSQkaObMmdq4caNSU1M1atQoRUREqGfPnpKk+Ph4nTx5UuPHj1daWppWr16t5OTkCkcDAQAAAMCTGOYI3W+//abg4OAK82+99VbNnj1b8+fP1/z58xUYGKi7775bI0eOtK8zYsQIlZaWasKECSosLFR0dLSSkpLsN0IJCwvTwoULNWXKFMXFxSk8PFxjxoxRXFxcHb06AKgbZrPJ4Xq46nLlTVGsVpvDKZwAAMB5hil0kyZNOuuyO+64Q3fcccdZl3t5eSkxMVGJiYlnXaddu3ZasWLF+UQEAI9mNpsUHOzv1B20XHlTFIvFqtzc05Q6AABcwDCFDgBwfsxmk7y8zHpzzPs6tDe76g3OkJOX//t0dr6ei3/dqQxNWzTUyL/0kdlsotABAOACFDoAuMgc2putfbsO1WibkvBS+ztGSUlpjbcHAAC1wzA3RQEAAAAAOOIIHQCnZGZmaMmSBZKkfv0G6bLLmrg5EQAAwMWHI3QAnJKcnKSUlO1KSdmupUuTqt4AAAAALkehA+CUjIyD9ulDhw6eY00AAADUFgodAABALfKzelU6DQCuwDV0cBmuqYKn4XsSgCe44rSv0v0L7dMA4EocoYPLcE0VPA3fkwA8gZ/VS61PXqLWJy/hCB0Al6PQwWW4pgqehu9JAABwoaPQAQCq5FXqW+k0AABwL66hAwBUyS+/sQpMNvs0AADwDBQ6AECVvCy+Cjh+lbtjAACAP+CUSwAAAAAwKAodAAAAABgUhQ4AAAAADIpCBwAAAAAGxU1RAA+TmZmhJUsWSJL69Rukyy5r4uZEAAAA8FQcoQM8THJyklJStislZbuWLk1ydxwAAAB4MI7QAR4mI+OgffrQoYPnWPP8mc0mmc0mp7Y1mUwO097ezv19yGq1yWq1ObUtgIubd7FJFm+bfRoALkYUOuAiZTabFBzsLy8v54rYmUXQbDYpJOQSp8axWKzKzT1NqQNQY0HHvZVnKrVPA8DFiJ9+wEXKbDbJy8uspSu3KuvoiRpvn3ei0GH6L/P+VeMxIsID9fgDnWQ2myh0AGrMu9SksCP13B0DANyKQgdc5LKOntChw3k13s5isTpMOzMGAAAAzg83RQEAAAAAg6LQAQAAAIBBUegAAAAAwKAodAAAAABgUBQ6AACAi1ygycs+HWTinnmAkfA/1gAyMzO0ZMkCSVK/foN02WVN3JwIAABcSNp6B2hH6UlJUhtv554rCsA9OEJnAMnJSUpJ2a6UlO1aujTJ3XEAAMAFJtDkrZvqBeumesEK5AgdYCj8jzWAjIyD9ulDhw6eY01UhaOdAAAAuJBwhA4XFY52AgAA4ELCETpcVOryaKfZbJLZbKrxdiaTyWHa29v5v7tYrTZZrTant/cU7EsAAIDKUeiAWmA2mxQc4i8vc80LxJnFxWw2KSTE+YvTLVarcnNOG7qImM0mBQf7y8vLzfvSYlVurrH3JQAAuPBQ6ODA2SMhkuuOhlwIR0LMZpO8zGYt/2K7snNO1mjb/FNFDtNzln/rVIaGIQF6+I4OMptNht6fZrNJXl5mLVrwb2UdzqvRtnm5BQ7TU1/+zKkMEY0baMCgmwy/LwEAwIWHQge7siMY/jI7cVSpfHvHsZw7GmK1WpVj8KNK5bJzTirzaH6NtrFYrA7TNd2+rtT3C1FJ0Yn/TYfW+r+XdThPBw/k1Gib0jP2ZanFWuPtAQAAPB2FDnZlR+fM2vzLr8o/VVD1Bn9QUFTsML3++5QajxF0iZ9uuPZqjoQYQETzW3R439f/m+7q5jQAAAAXJwodKsg/VaDck6dqvJ3VZnOYdmYMGEd9vxBdGXWvu2MAAABc1Azx2IKMjAy1atWqwsfKlSslSbt27VJCQoI6dOigbt26KSnJ8Xb0VqtVc+bMUdeuXdW+fXsNGDBA6enpDutUNQYAAAAAeBpDHKHbvXu36tevrw0bNjjceCMwMFA5OTnq37+/evToocmTJ2v79u2aPHmygoODFR8fL0maN2+eli9frmnTpqlRo0aaMWOGBg0apLVr18rHx6daY8BzeMKNW6QL4+YtAAAAMDZDFLo9e/aoefPmatiwYYVlycnJ8vHx0aRJk+Tt7a0WLVooPT1dCxYsUHx8vIqLi7Vo0SIlJiYqNjZWkjRr1ix17dpV69evV69evfTRRx+dcwx4Dk+5cYt0Yd28BQAAAMZkiEK3e/dutWzZstJlW7duVXR0tLy9f38pMTExevfdd3Xs2DFlZGTo1KlTiomJsS8PCgpSVFSUtmzZol69elU5RlhY2Hm/Bo4quUb5jVvW/ednHc8/XePtTxUUOUx/8I/vncoRGuSvu2LacPMWAAAAuJUhCt2ePXsUHh6uRx99VPv371ezZs00bNgwde3aVVlZWYqMjHRYv/xIXmZmprKysiRJjRs3rrDO4cOHJanKMZwtdOXFy2QyKSjIz+lC59qjSjbl5xfIZqtYQpx5cHNtOVuW8vnH808rO+dEjce1nFG+LFabU2NUlqe6893ByBmrWlbX2Jeuw750HSPvS0/KKBkjpxEySsbIaeSMVS2ra+xL13Emi8cXuuLiYu3fv19+fn4aM2aM/P399emnn2rQoEFavHixCgsL5ePj47BN/fr1JUlFRUUqKCi7/X5l6+TllT2kuKoxnFFZ8dp3MEOFhcVn2eLsSkpKHaZ3/brPqUy+vj5qfnkTBQf7O7V9XQoK8nN3hGoxQk4yuo4Rchoho2SMnEbIKBkjpxEySsbIaYSMkjFyktF1jJDTCBkl53J6fKHz8fHRli1b5O3tbS9dbdq00d69e5WUlCRfX18VFzuWpPIS5u/vL19fX0llxbB8unwdP7+yHVbVGM4oOxJWdkqgl5dZQUF+KiwsVkFhYY3HOvNoms1mc2qMM+XnFzg8vLpceU5PYISMkjFyGjmjZIycRsgoGSOnETJKxshJxpozQk4jZJSMkdPIGSVj5DRCRslzc1b3rDyPL3RS5aUqMjJS3377rSIiIpSdne2wrPzzRo0aqbS01D7viiuucFindevWklTlGM4qLa38m8bdLBarx2YrZ4SMUu3k9AsMVXFBftl00Plfv2mEfWmEjJIxchoho2SMnEbIKBkjpxEySsbIaYSMkjFyktF1jJDTCBkl53J6zgmjZ5GamqqOHTtq69atDvN//vlntWzZUtHR0dq2bZssFot92aZNm9S8eXOFhYWpdevWCggI0ObNm+3L8/PztXPnTnXq1EmSqhwDqEuXt71VQQ2bKahhM13eppu74wAAAMCDeXyhi4yM1NVXX63Jkydr69at2rt3r6ZNm6bt27dr6NChio+P18mTJzV+/HilpaVp9erVSk5O1pAhQySVnbKZkJCgmTNnauPGjUpNTdWoUaMUERGhnj17SlKVY+DCERASdsb0pW5McnZ+gaFqddP9anXT/fILDHV3HAAAAHgwjz/l0mw2a/78+Zo5c6ZGjhyp/Px8RUVFafHixWrVqpUkaeHChZoyZYri4uIUHh6uMWPGKC4uzj7GiBEjVFpaqgkTJqiwsFDR0dFKSkqyX5MXFhZW5Ri4METd2FO/2P7xv+kebk4DAAAAnB+PL3SSFBoaqqlTp551ebt27bRixYqzLvfy8lJiYqISExOdHgMXhoDgMN1w1yPujgEAAAC4hMefcgkAAAAAqByFDsAFy6decKXTAAAAFwoKnQE0bBRhn24UEXGONQGcqWHoDfL3ayJ/vyZqGHqDu+MAAAC4nCGuobvY3RMXr09Wlz2P4u4/x7s5DWAcPj7BahrxJ3fHAAAAqDUUOgNo2KiRBj35tLtjAAAAAPAwnHIJAAAAAAZFoQMAAAAAg6LQwWVCwsJ/n760oRuTAAAAABcHCh1cpsvtd6npVS3V9KqW6tLzTnfHAQAAAC543BQFLhMcFq7ej/RzdwwAAADgosEROgAAAAAwKAodAAAAABgUhQ4AAAAADIpCBwAAAAAGRaEDAAAAAIOi0AEAAACAQVHoAAAAAMCgKHQAAAAAYFAUOgAAAAAwKAodAAAAABgUhQ4AAAAADIpCBwAAAAAGRaEDAAAAAIOi0AEAAACAQVHoAAAAAMCgKHQAAAAAYFAUOgAAAAAwKAodAAAAABgUhQ4AAAAADIpCBwAAAAAGRaEDAAAAAIOi0AEAAACAQVHoAAAAAMCgKHQAAAAAYFAUOgAAAAAwKAodAAAAABgUhQ4AAAAADMoQhS43N1cvvfSSbrnlFl133XV65JFHtHXrVvvycePGqVWrVg4ft9xyi3251WrVnDlz1LVrV7Vv314DBgxQenq6w7+xa9cuJSQkqEOHDurWrZuSkpLq7PUBAAAAgDMMUeieffZZ/fTTT3rjjTf08ccf69prr9XAgQO1d+9eSdLu3bs1dOhQffvtt/aPNWvW2LefN2+eli9frldffVUrVqyQyWTSoEGDVFxcLEnKyclR//79deWVV2rVqlUaPny4Zs+erVWrVrnj5QIAAABAtXh8oUtPT9d3332niRMnqlOnTrrqqqs0fvx4NWrUSGvXrpXFYlFaWpratm2r8PBw+0doaKgkqbi4WIsWLdLw4cMVGxur1q1ba9asWTpy5IjWr18vSfroo4/k4+OjSZMmqUWLFoqPj1e/fv20YMECd750AAAAADgnjy90ISEheu+999SmTRv7PJPJJJvNpry8PO3fv19FRUVq0aJFpdunpqbq1KlTiomJsc8LCgpSVFSUtmzZIknaunWroqOj5e3tbV8nJiZG+/bt07Fjx2rplQEAAADA+fH4QhcUFKTY2Fj5+PjY53322Wc6cOCAbr75Zu3Zs0cmk0nJycnq3r27evTooVdeeUUnTpyQJGVlZUmSGjdu7DBuw4YNdfjwYfs6ERERFZZLUmZmZq29NgAAAAA4H95Vr+JZtm3bphdeeEG33Xabunfvrjlz5shsNqtJkyaaP3++0tPT9dprr2nPnj1KTk5WQUGBJDkUQkmqX7++8vLyJEmFhYWVLpekoqIip7N6e5f1ZS8vz+rNZ8vjSTmNkFEyRk4jZ6xqWV1jX7oO+9J1jLwvPSmjZIycRsgoGSOnkTNWtayusS9dx5kship0GzZs0HPPPaf27dvrjTfekCQNHz5c/fr1U1BQkCQpMjJS4eHheuihh7Rjxw75+vpKKruWrnxaKitqfn5+kiRfX1/7DVLOXC5J/v7+TmU1m00KCbnEqW1rW1CQn7sjVMkIGSVj5CSj6xghpxEySsbIaYSMkjFyGiGjZIycRsgoGSMnGV3HCDmNkFFyLqdhCt0HH3ygKVOmqGfPnpo5c6b9iJrJZLKXuXKRkZGSyk6lLD/VMjs7W1dccYV9nezsbLVu3VqSFBERoezsbIcxyj9v1KiRU3mtVpvy809LKmvanvRNlJ9fIIvFWmG+J+U0QkbJGDmNnFEyRk4jZJSMkdMIGSVj5CRjzRkhpxEySsbIaeSMkjFyGiGj5Lk5q3twyBCFbtmyZXrllVfUp08fvfDCCzKbfz8UOXr0aOXm5jo8N27Hjh2SpJYtW+ryyy9XQECANm/ebC90+fn52rlzpxISEiRJ0dHRWr58uSwWi7y8vCRJmzZtUvPmzRUWFuZ07tLSyr9p3M1isXpstnJGyCgZIycZXccIOY2QUTJGTiNklIyR0wgZJWPkNEJGyRg5yeg6RshphIySczk954TRs9i3b5+mTp2qnj17asiQITp27JiOHj2qo0eP6sSJE+rdu7e+++47vfPOOzpw4IC++uorvfDCC+rdu7datGghHx8fJSQkaObMmdq4caNSU1M1atQoRUREqGfPnpKk+Ph4nTx5UuPHj1daWppWr16t5ORkDRkyxM2vHgAAAADOzuOP0H3xxRcqKSnR+vXr7c+NKxcXF6fp06dr9uzZmj9/vubPn6/AwEDdfffdGjlypH29ESNGqLS0VBMmTFBhYaGio6OVlJRkP20zLCxMCxcu1JQpUxQXF6fw8HCNGTNGcXFxdflSAQAAAKBGPL7QDR06VEOHDj3nOnfccYfuuOOOsy738vJSYmKiEhMTz7pOu3bttGLFCqdzAgAAAEBd8/hTLgEAAAAAlaPQAQAAAIBBUegAAAAAwKAodAAAAABgUBQ6AAAAADAoCh0AAAAAGBSFDgAAAAAMikIHAAAAAAZFoQMAAAAAg6LQAQAAAIBBUegAAAAAwKAodAAAAABgUBQ6AAAAADAoCh0AAAAAGBSFDgAAAAAMikIHAAAAAAZFoQMAAAAAg6LQAQAAAIBBUegAAAAAwKAodAAAAABgUBQ6AAAAADAoCh0AAAAAGBSFDgAAAAAMikIHAAAAAAZFoQMAAAAAg6LQAQAAAIBBUegAAAAAwKAodAAAAABgUBQ6AAAAADAoCh0AAAAAGBSFDgAAAAAMikIHAAAAAAZFoQMAAAAAg6LQAQAAAIBBUegAAAAAwKAodAAAAABgUBQ6AAAAADAoCh0AAAAAGBSF7n+sVqvmzJmjrl27qn379howYIDS09PdHQsAAAAAzopC9z/z5s3T8uXL9eqrr2rFihUymUwaNGiQiouL3R0NAAAAACpFoZNUXFysRYsWafjw4YqNjVXr1q01a9YsHTlyROvXr3d3PAAAAACoFIVOUmpqqk6dOqWYmBj7vKCgIEVFRWnLli1uTAYAAAAAZ2ey2Ww2d4dwt3/84x8aPny4fvrpJ/n6+trnP/PMMyosLNS7775b4zFtNpus1rJdazJJZrNZJaWlcufuNplMquftLavVqspilOcsLC6R1Wqt+4Aq+/d9fepVmfF0YbEsbsooSV5ms/x9farMefJ0kSxW93zNvcwmBfjXrzLjiZNFsljcsy+9vMwKDDh7RumMnPmFKnVTTm8vswKDfKvcl/m5p2UpddO+9DYrKNi/Wvsy79gJlZZa6jbg/3h7e6lBWGCV+zLnt1yVlpTWfUBJ3vW8FXJpcLX25bHfjrs1Z9ilodXYlzkqLSmp+4CSvOvVU8ilIVVmzDuWK4ub9qMkedXzVoOws3/N7T/Tj+fJUuqenF7e3goIbVD1+2NunqxuyihJZm9v+QdXnbMwP19Wi3tymr285RsUVGXGopMnZLW452el2ctL9QPO/rNS+j1nccEpt+b08bukyn1ZUlQom5t+bzOZzapX/+zv4dLvOUtLStz2u7rJZJJ3Pcffgb28qnfszbsWcxlGQUGBJMnHx8dhfv369ZWXl+fUmCaTSV5eJod59bw9Y3ebzef+5vD1qVdHSc6uqoz+vj7nXF5XqsoZ4F+/jpKcXVUZAwM8P6MkBQb5VrlObasqZ1Cwfx0lObvq7MsGYYF1kOTcqsoZcmlw3QQ5h+rsy7BLQ+sgyblVvS9D6ijJ2VWVsUFYcN0EqUKVP9NDG9RRkrOr8v0x2P0ZpWr8rhEUVEdJzq6qjPUDPP9npST5+F1SB0nOraqc9ep7/nu4VPZHKHerTs4K29RCDsMpPyr3xxugFBUVyc/Pzx2RAAAAAKBKFDpJjRs3liRlZ2c7zM/OzlZERIQ7IgEAAABAlSh0klq3bq2AgABt3rzZPi8/P187d+5Up06d3JgMAAAAAM7OMy7qcjMfHx8lJCRo5syZCg0NVZMmTTRjxgxFRESoZ8+e7o4HAAAAAJWi0P3PiBEjVFpaqgkTJqiwsFDR0dFKSkqqcKMUAAAAAPAUPLYAAAAAAAyKa+gAAAAAwKAodAAAAABgUBQ6AAAAADAoCh0AAAAAGBSFDgAAAAAMikIHAAAAAAZFofNwVqtVc+bMUdeuXdW+fXsNGDBA6enp7o51TvPmzVOfPn3cHaOC3NxcvfTSS7rlllt03XXX6ZFHHtHWrVvdHauCY8eOKTExUTExMerYsaMGDx6stLQ0d8c6q3379qljx45avXq1u6NUkJGRoVatWlX4WLlypbujOVizZo3uuusutW3bVr169dJnn33m7kgONm/eXOl+bNWqlW677TZ3x7MrKSnRrFmz1K1bN3Xs2FGPPvqofvjhB3fHquDUqVN65ZVXFBsbq+uvv17Dhg3TgQMH3B3LrrKf4bt27VJCQoI6dOigbt26KSkpyU3pfne295p9+/apQ4cOOnTokBtSOaos4z//+U/Fx8erY8eO6t69u1577TUVFha6KWHlGf/+97/r7rvvVrt27dSjRw+99957cvdTrqr63WLChAnq3r17HSaqqLKM48aNq/Bz85ZbbnFTwjKV5czOztazzz6rTp066YYbbtDo0aN1/PhxNyWsmLFPnz5nfR9as2aNR2SUpB07dighIUEdO3ZUbGys/vKXv6i4uLj2w9jg0d566y3bjTfeaPvyyy9tu3btsg0YMMDWs2dPW1FRkbujVWrx4sW2Vq1a2RISEtwdpYL+/fvb7rnnHtuWLVtse/futb3yyiu2du3a2dLS0twdzcEDDzxge+ihh2wpKSm2tLQ02/Dhw21dunSxnT592t3RKiguLrbdd999tsjISNuqVavcHaeCjRs32tq2bWs7cuSILTs72/5RUFDg7mh2a9assV1zzTW2JUuW2Pbv3297++23ba1bt7b98MMP7o5mV1RU5LD/srOzbd9++60tKirK9tFHH7k7nt3s2bNtXbp0sX3zzTe2/fv328aPH2+77rrrbFlZWe6O5uCJJ56wde3a1fbPf/7TlpaWZpswYYLtpptush0/ftzd0Sr9GX78+HHbDTfcYBs/frwtLS3N9vHHH9vatm1r+/jjjz0qp81ms6WmptpiY2NtkZGRtoMHD7opXZnKMm7ZssV2zTXX2N59913b/v37bV999ZUtNjbWNnbsWI/J+OWXX9qioqJsH3zwge3AgQO2L774wtahQwfb4sWL3ZLxbDnPtH79eltkZKTt1ltvreNkvztbxri4ONsbb7zh8PPz2LFjbkpZec6ioiJbr169bPfff78tJSXF9uOPP9r+9Kc/2Z544gmPyZiTk1PhfWjw4MG2P/3pT7YTJ054RMZjx47ZOnfubHvxxRft/79jYmJs06dPr/U8FDoPVlRUZOvYsaNt2bJl9nl5eXm2du3a2dauXevGZBVlZWXZBg4caOvQoYPtT3/6k8cVuv3799siIyNt27Zts8+zWq22nj172t588003JnN0/Phx26hRo2x79uyxz9u1a5ctMjLS9tNPP7kxWeVef/11W58+fTy20L3zzju2e+65x90xzspqtdpuvfXWCj/sBwwYYJs/f76bUlWtuLjY1qtXL9vIkSPdHcXBPffcY5s2bZr98xMnTtgiIyNtn3/+uRtTOSr///zll1/a51ksFtvtt99ue/vtt92W61w/w+fPn2/r2rWrraSkxD7v9ddft91xxx0elXPevHm2du3a2eLi4txa6M6VcfTo0bb+/fs7rL9mzRpbVFRUnf6h9lwZV65caZs1a5bD+sOGDbMNGjSozvKVq87vFkeOHLHFxMTYEhIS3FLozpWxtLTU1rZtW9v69evrPNcfnSvnqlWrbB06dLAdPXrUPu/rr7+23XbbbXValmryu+Tf/vY3W1RUlC01NbXO8tls585Y/oeFM/fZ1KlTbb179671XJxy6cFSU1N16tQpxcTE2OcFBQUpKipKW7ZscWOyin755Rc1aNBAn376qdq3b+/uOBWEhITovffeU5s2bezzTCaTbDab8vLy3JjMUUhIiN544w1dffXVkqTffvtNSUlJioiIUMuWLd2cztGWLVu0YsUKvfbaa+6Ocla7d+/2uP12pv/+97/KyMjQ3Xff7TA/KSlJQ4YMcVOqqn344Yc6fPiwxo0b5+4oDoKDg/Wvf/1Lhw4dksVi0YoVK+Tj46NrrrnG3dHs9u3bJ0nq1KmTfZ7ZbFbr1q3d+nP9XD/Dt27dqujoaHl7e9vnxcTEaN++fTp27JjH5Pzmm280Y8YMPf/883Wa6Y/OlXHAgAEaM2ZMhW1KS0t18uTJuop4zoz333+/Ro4cKUmyWCz6+uuv9f3336tLly51lq86OSXJZrNp7Nixuvfee9W5c+c6zyedO+P+/ftVVFSkFi1auCXbmar6vxMTE6NLL73UPq9r167asGGDAgICPCLjmU6fPq2//OUv6tu3r1q1alVn+aRzZwwODpYk/d///Z8sFosOHTqkr776qk5+L/auehW4S1ZWliSpcePGDvMbNmyow4cPuyPSWXXv3t3t566fS1BQkGJjYx3mffbZZzpw4IBuvvlmN6U6txdffFEfffSRfHx89M4778jf39/dkezy8/M1ZswYTZgwocL3pyfZs2ePwsPD9eijj2r//v1q1qyZhg0bpq5du7o7mqSyN3up7M1p4MCB2rlzp5o2baonn3zSY/8/FRUVaf78+erbt68aNmzo7jgOxo8fr1GjRum2226Tl5eXzGazZs+erSuuuMLd0ezCw8Mllf18P/OXvIyMDBUVFbkr1jl/hmdlZSkyMtJhXvnXPjMzU2FhYbWer9y5ci5btkxS2TWf7nSujFFRUQ6fFxcXa/Hixbr22msVGhpaF/EkVe89OzMzUz169JDFYtHNN9+sRx55pI7S/a6qnEuWLNHRo0c1f/58vfvuu3WY7Hfnyrhnzx6ZTCYlJyfr66+/ltlsVmxsrEaOHKnAwECPybl//3516tRJc+fO1Zo1a1RaWqqbb75ZiYmJCgoK8oiMZ1q+fLlOnTqlJ598sg5SOTpXxk6dOmnw4MGaPXu2Zs2aJYvFos6dO+vFF1+s9VwcofNgBQUFkiQfHx+H+fXr13frG/+FYNu2bXrhhRd02223eewvzn379tWqVat0zz336KmnntIvv/zi7kh2kyZNUocOHSocWfIkxcXF2r9/v06ePKmRI0fqvffeU9u2bTVo0CBt2rTJ3fEkyf4X+eeff169e/fWokWL1KVLFw0bNsxjMv7RJ598oqKiIo+88dHevXsVFBSkuXPnasWKFbrvvvv0/PPPKzU11d3R7Nq3b68WLVpo4sSJOnz4sIqLi7VkyRLt2rWrbi6cd0JhYWGl70OSeC86D6WlpRozZozS0tI0ceJEd8epICgoSB9//LFmz56t3bt3V3pk0Z1SU1P19ttva8aMGRW+Pz3Fr7/+KrPZrCZNmmj+/Pl6/vnn9dVXX2nYsGGyWq3ujmd38uRJrVmzRrt379brr7+ul19+Wdu2bdOwYcPcfjOcP7JYLHr//ff16KOP1nkprkp+fr7279+vxx57TCtXrtTs2bN14MABTZo0qdb/bY7QeTBfX19JZb+Ylk9LZW+gfn5+7opleBs2bNBzzz2n9u3b64033nB3nLMqP1XwlVde0fbt2/XBBx9o2rRpbk5VdkfGrVu36m9/+5u7o5yTj4+PtmzZIm9vb/ubfZs2bbR3714lJSXpxhtvdHNCqV69epKkgQMHKi4uTpJ0zTXXaOfOnVq8eLFHZPyjNWvW6Pbbb1dISIi7ozjIyMhQYmKilixZYj+dsW3btkpLS9Nbb72luXPnujlhmXr16mnu3LkaO3asunXrJm9vb3Xr1k3333+/fv75Z3fHq5Svr2+Fslle5DzpzAEjKf9D0+bNmzVnzhyPvFQhICBAUVFRioqKktVq1ahRo5SYmKgmTZq4O5qKior03HPP6cknn1Tr1q3dHeeshg8frn79+tmPckVGRio8PFwPPfSQduzY4TFf93r16snf31+vv/66/X2pQYMGeuCBB7Rjxw61a9fOzQl/9/333yszM1MPPvigu6NUMHPmTOXn5+utt96SJF177bVq0KCB+vXrp759+9bq9ypH6DxY+als2dnZDvOzs7MVERHhjkiG98EHH2j48OG65ZZbtGDBAoei7AmOHTumtWvXymKx2OeZzWa1aNGiwveBu6xatUrHjh2z3xq+Y8eOkqSJEyeqV69ebk7nyN/fv8JfbiMjI3XkyBE3JXJU/v/4j6eztWzZ0iNuuf5Hx48f148//qi77rrL3VEqSElJUUlJidq2beswv3379vZTWz1F8+bNtWLFCn3//ffatGmT5s6dq9zcXF155ZXujlapiIiISt+HJKlRo0buiGRo2dnZeuyxx/Tjjz9qwYIFHneWyNatW7Vjxw6HeeXXdXvK+9BPP/2kX3/9VW+//bb9fejdd99VZmamOnbsqE8//dTdESWVXav/x1MWy3/el19W4wkiIiLUvHlze5mTfv+ae9p70YYNG9SuXTtdfvnl7o5SwbZt2yp9D5J+v366tlDoPFjr1q0VEBDgcD1Afn6+du7c6XBBPapn2bJleuWVV/TYY4/pzTff9MhTNLKzszV69Gh9//339nklJSXauXOnR1xULZX9BWrdunVas2aN/UOSRowYoffee8+94c6Qmpqqjh07VnjW4M8//+wxN0qJiorSJZdcop9++slh/p49ezzquq9yP/zwg0wmk9tuPnAu5X8A2717t8P8PXv2qFmzZu6IVKmTJ08qISFBP//8sxo0aKCgoCCdOHFC//73vz3m2s4/io6O1rZt2xz+0LRp0yY1b968Tq+fuxDk5eWpb9++On78uJYtW+Zw0zNPsWjRogpng/z000/y9vb2mD86tGvXTv/4xz/0ySef2N+HHn74YTVs2FBr1qzxmJI8evRoDRw40GFeeVn2lPchqezar9TUVIfnIe7Zs0eSPOrnp1RWmjzx/41UVowrew+SVOv/dyh0HszHx0cJCQmaOXOmNm7cqNTUVI0aNUoRERHq2bOnu+MZyr59+zR16lT17NlTQ4YM0bFjx3T06FEdPXpUJ06ccHc8u9atW+vmm2/W5MmTtXXrVu3Zs0fPP/+88vPz1a9fP3fHk1T2F/lmzZo5fEhSWFiYR5yKUy4yMlJXX321fV/u3btX06ZN0/bt2zV06FB3x5NUdirbE088oblz52rt2rU6cOCA3nnnHX333Xfq37+/u+NVkJqaqssvv9wjT/lu166dOnXqpOeff17/+c9/tH//fr355pvatGmTBg8e7O54dgEBATKZTJo6dap2796t1NRUDR06VJdddpl69+7t7niVio+P18mTJzV+/HilpaVp9erVSk5O9ug7sXqqadOm6eDBg5oxY4ZCQ0Pt70NHjx51KMzuNGDAAG3fvl1z5sxRenq61q1bpxkzZujxxx/3mFOtfX19K7wPNWjQQN7e3mrWrFmd3pnxXHr37q3vvvtO77zzjg4cOKCvvvpKL7zwgnr37u0xf6SVpIcfflheXl4aPXq09uzZo23btmnChAm64YYbdO2117o7np3FYlFaWlqFs1o8Rf/+/fXNN9/ozTff1IEDB7Rp0yaNHTtWsbGxtX63Za6h83AjRoxQaWmpJkyYoMLCQkVHRyspKckjjy55si+++EIlJSVav3691q9f77AsLi5O06dPd1MyRyaTSW+++aZef/11jRw5UidOnFCnTp304Ycf6rLLLnN3PEMxm82aP3++Zs6cqZEjRyo/P19RUVFavHhxnd/m+FyGDRsmPz8/zZo1S0eOHFGLFi301ltv6YYbbnB3tAp+++03+22ZPY3ZbNa8efP05ptvaty4ccrLy1NkZKSWLFmiDh06uDueg9dff12vvvqqEhISZDabdeutt2rMmDEOjwXwJGFhYVq4cKGmTJmiuLg4hYeHa8yYMfbrPlE9VqtV69atU0lJifr27Vth+caNG9W0aVM3JHPUqVMnvfvuu3rzzTeVlJSk0NBQDRgwQIMGDXJ3NMO59dZbNXv2bM2fP1/z589XYGCg7r77bvtjITxFaGioPvzwQ02bNk0PPvigfHx81KNHD497NE1ubq5KSko89n3o5ptv1rvvvqu5c+cqOTlZISEh6tmzp5555pla/7dNNk+7fQ0AAAAAoFo45RIAAAAADIpCBwAAAAAGRaEDAAAAAIOi0AEAAACAQVHoAAAAAMCgKHQAAAAAYFAUOgAAAAAwKAodAAAXOR5JCwDGRaEDALjcc889p1atWunbb7+tdPk333yjVq1a6bXXXqvjZI7Gjh2r7t27O7Wt1WrVypUr9dhjj+mGG27Qddddp7i4OC1dulTFxcUuTlp7Vq5c6fB12Lx5s1q1alXho23bturWrZvGjRun3377rUb/xqFDh9SqVSutXr3a1fEB4KLn7e4AAIALz4QJE/Sf//xHEydO1Nq1a+Xn52dfdurUKU2cOFFXX321Ro0a5caUzisoKNDQoUP1008/6ZFHHtETTzyhevXqafPmzZo5c6a++uorvfPOO/Lx8XF31Cq988476ty5c4X5L730kq699lr756dOndLWrVu1YMEC/fe//9WKFSvqMiYA4CwodAAAlwsODtakSZP01FNP6c0339S4cePsy15//XVlZ2dr5cqVhig8lZk2bZp++OEHvf/+++rQoYN9/s0336yoqCiNHDlSH374ofr37+++kOepZcuWDq9Nkrp06aLS0lK99957SktLU8uWLd0TDgBgxymXAIBa0aNHD/Xu3Vvvv/++duzYIUn64YcftGzZMj399NO65pprlJmZqWeffVadO3dW+/bt1bdvX+3cudNhnEOHDmnMmDG6+eabde211+rGG2/UmDFjlJOTY1+ne/fumjp1qvr27avrrrtOL730kiTp/fff15/+9Ce1bdtWXbt21aRJk3Ty5MmzZn7rrbfUs2dPffnll7r77rvVpk0b3XHHHfrrX/9qX+f48eNatWqV4uPjKxQeSbrzzjs1cOBARURE2PNXdrrhH0/37NOnj5577jmNGDFC1113nQYPHmzfdvHixbrzzjvVuXNn+zh79uzRkCFDdN111+m6667TU089pYMHD9rHKz91ctOmTRowYIDat2+vm266Sa+99ppKS0vt+y0jI0N//etf1apVKx06dOjsX9D/CQwMrDBv//79GjFihLp06aIOHTqoT58+2rZt2znHqc7XHgBQNQodAKDWTJgwQSEhIXr55ZdVUlKiSZMmqX379ho0aJCOHz+uhx9+WL/88otefPFFvf7667JarXrssce0d+9eSWWnNj7++OPau3evJk6cqKSkJCUkJGjt2rV64403HP6tDz/8UK1atdJbb72le++9V3//+9/12muv6bHHHlNSUpKeeuopffLJJ3r11VfPmfno0aN6+eWX9fjjj+u9995T06ZNNXbsWHumTZs2qbS0VLfeeutZxxgzZozuvPPOGu+vzz77TPXq1dPcuXP1+OOP2+fPmjVLAwcO1KuvvqqYmBjt27dPDz/8sI4dO6bp06drypQpOnjwoB555BEdO3bMYcznnntO119/vebPn6+7775bixYt0scffyxJevvttxUeHq7Y2FitWLFCDRs2tG9ntVpVWlpq/8jLy9PGjRuVlJSktm3b6qqrrpIkpaWl6b777tPBgwc1YcIEzZw5UyaTSX379tX3339f6eusztceAFA9nHIJAKg1ISEhmjRpkp5++mkNGDBABw4c0Jo1a+Tl5aXk5GTl5ubq//7v/9SkSRNJ0i233KK77rpLs2fP1pw5c7R//35FRERo+vTpuuKKKyRJMTEx2rFjR4Wy0LBhQ40dO1Zmc9nfKl966SU1adJEjz32mMxmszp37ix/f3+HI3uVKSgo0JQpU3TjjTdKkq688krdeuut+uqrr9SiRQtlZWVJkpo2berSfSVJZrNZr7zyivz9/SXJfsTs9ttv1/33329fb/To0fL19dWSJUsUEBAgSbrxxhvVo0cPLVy4UM8//7x93QceeEBPPfWUfZ0NGzboyy+/1MMPP6yoqCj5+PgoNDS0wtHGfv36VcjXoEED3XbbbUpMTLTv57ffflv16tXT0qVL7UfvunXrpt69e2vGjBlauXJlhXGq87UHAFQPhQ4AUKt69uypu+66S+vWrdNLL72kK6+8UlLZka5rrrlGjRo1sp8CaDabdcstt+jTTz+VJF1zzTVatmyZrFarDh48qP379+vXX3/Vf//7X/s25Vq0aGEvGVJZ8VuxYoXuu+8+3X777erWrZvuvvtumUymKjOfWW7KT508ffq0PaNUdgTL1Zo2bWovc2eKjIx0+Pw///mPbrjhBvn6+tr3Q0BAgDp16qR///vfDut27NjR4fOIiAj7azmXyZMn69prr5XFYtGGDRu0aNEiPfbYY3rmmWcc1vv+++916623OpyK6e3trV69emnu3Lk6depUhbGr87UHAFQPhQ4AUOu6du2qdevWKTY21j4vNzdX6enpDndSPFNBQYH8/Py0ePFivfvuu8rJydGll16qa6+9Vn5+fjpx4oTD+pdeeqnD53fddZesVquWLVumt99+W7Nnz1aTJk00evRo9erV65x5z7wrZ3mBK39WW/kRpczMTF199dWVbn/06FGFhITI27tmb7N/fA1nm5+bm6t169Zp3bp1FdYNDQ11+NzX19fhc7PZXK3nzjVv3lxt27aVVFZw/fz8NGfOHPn5+Wnw4MH29fLy8irNfemll8pms1V6zWJ1v/YAgKpR6AAAbhEYGKjOnTtrzJgxlS738fHR3/72N02fPl2jR4/W/fffby8rzzzzjP1GK+fSu3dv9e7dWydOnNC3336rBQsWKDExUZ06dVKjRo2cyh0TE6N69erpq6++ciioZxoyZIgKCgr02Wef2Y8IWiwWh3Wqc5TsbAIDA3XTTTdVehfNmpbI6ho6dKg2bNigOXPmqFu3bvajhg0aNKj0uXRHjx6VVHbabXZ2tsOy6nztAQDVw01RAABu0blzZ+3bt89+JKj849NPP9XKlSvl5eWlbdu2KTAwUIMHD7aXuVOnTmnbtm1VnvI4cuRIPf3005LKCsSdd96pYcOGyWKxVCgYNREUFKT7779fH330kVJSUiosX7t2rX755Rfde++9kmS/xq382jtJKikpqXTb6urcubPS0tJ0zTXX2PdbmzZttGTJEq1fv75GY515muq5eHl5aeLEiSotLdUrr7xinx8dHa1//etfDkdMLRaL/v73v6tt27aVlrPqfO0BANVDoQMAuEW/fv1ktVrVr18/rVu3Tps2bdKLL76opUuX2u+g2K5dO504cULTp0/X5s2b9be//U2PPfaYfvvtNxUUFJxz/JiYGK1fv16vvfaaNm3apC+++EKzZ8/WlVdeqdatW59X9meffVatWrVS3759NXPmTH3zzTf617/+pZdfflljx47VLbfcoieeeEJS2RGsjh076oMPPtCnn36qb775Rk899ZQKCwud/veHDRumAwcOaMiQIdqwYYO++eYbDR8+XH//+99r/NqCgoK0c+dOff/991Vm6tChg+655x59//33+vvf/y5Jevrpp1VcXKzHH39cn332mTZu3KgnnnhCBw8e1LPPPlvpONX52gMAqodTLgEAbtGoUSMtX75cr7/+uiZNmqSioiJdeeWVmjJliv2OjnFxcTp06JBWrVqlZcuWqVGjRoqNjdWjjz6qF1988ZwPt3744YdVUlKi5cuXa9myZfL19dWNN96oxMRE1atX77yyBwUF6f3339cHH3ygdevWafny5bLZbGrWrJnGjRunBx54wOHUx+nTp+uVV17Riy++qICAAN1///3q2LFjpXeArI7WrVvrww8/1KxZszRmzBjZbDZFRkZq7ty5uu2222o01oABAzR16lQNHDhQixcvrnL9xMREbdiwQa+99ppuvfVWXX311Vq2bJneeOMNvfDCCzKZTGrXrp2WLl2qTp06VTpGdb72AIDqMdmqc2U0AAAAAMDjcMolAAAAABgUhQ4AAAAADIpCBwAAAAAGRaEDAAAAAIOi0AEAAACAQVHoAAAAAMCgKHQAAAAAYFAUOgAAAAAwKAodAAAAABgUhQ4AAAAADIpCBwAAAAAGRaEDAAAAAIP6fyQ3PPEmTaEXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 2 (Attrition of employees for YearsInCurrentRole v/s MonthlyIncome.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"YearsInCurrentRole\", y=\"MonthlyIncome\",\n",
    "             data=hr_data, kind=\"bar\",height=5, aspect=1.8, palette='twilight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d1ce0d",
   "metadata": {},
   "source": [
    "CONCLUSION  \n",
    "This barplot shows that the monthly income is positively corelated with the years of role experince as the experince increases the income also increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "dde8df09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be90186a90>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1180x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 3 (Attrition of employees for Department v/s WorkLifeBalance gender wise.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"Department\", y=\"WorkLifeBalance\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=5, aspect=1.1, palette='Paired',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98160468",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "In barplot no 2 attrition of male employees are from Human resources department whoes work life balance are more than 3 and for female employees it is from Research and development department whoes work life balance are between 2.5 and 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "6e0f1072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bef992fbe0>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1160x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 4 (Attrition of employees for HourlyRate v/s JobRole gender wise.)\n",
    "\n",
    "sns.set(style='whitegrid', font_scale= 1)\n",
    "sns.catplot(x=\"HourlyRate\", y=\"JobRole\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=6, aspect=.9, palette='Greens',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73060e6a",
   "metadata": {},
   "source": [
    "CONCLUSION  \n",
    "In barplot no 3 the attrition of male employees are from Manager job role with hour rate 73rs/hr and for female employees it is from Manager and Healthcare representative job role with 75rs/hr hour rate each. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "eb35a1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be9cdcb490>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1280x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 5 (Attrition of employees for Department v/s TotalWorkingYears gender wise.)\n",
    "\n",
    "sns.set(style='ticks', font_scale= 1)\n",
    "sns.catplot(x=\"Department\", y=\"TotalWorkingYears\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=5, aspect=1.2, palette='icefire',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d58de6",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "In this barplot the attrition of male employees are mostly from sales department with total working years of 9 years and other side for female employees it is from Research and development department with total working years 8.5 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "11b60ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be9b199f10>"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1480x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 6 (Attrition of employees for MaritalStatus v/s JobSatisfaction gender wise.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"MaritalStatus\", y=\"JobSatisfaction\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=5, aspect=1.4, palette='Reds',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e8ed61",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "In this barplot we can see the employees which are divorced are taking attrition or thinking of it. male employees mostly with 2.6 jobsatisfaction and female employees with 2.8 jobsatisfaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "b83bc467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bea1f9b7c0>"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 7 (Attrition of employees for Department v/s MonthlyIncome.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"Department\", y=\"MonthlyIncome\",\n",
    "             data=hr_data, kind=\"bar\",height=4, aspect=1.5, palette='twilight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf02d462",
   "metadata": {},
   "source": [
    "CONCLUSION    \n",
    "This barplot shows the highest monthly income is for sales department. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "63bc48e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be9379db80>"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1480x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 8 (Attrition of employees for Department v/s MonthlyIncome gender wise.)\n",
    "\n",
    "sns.set(style='whitegrid', font_scale= 1)\n",
    "sns.catplot(x=\"Department\", y=\"MonthlyIncome\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=5, aspect=1.4, palette='spring',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fb8802",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "In this barplot the attrition of male employees are from sales department with monthlyincome 6000 and for female employees attrition is also from sales department with monthlyincome 5700."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "8654dfca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be9488ab50>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 9 (Attrition of employees for JobLevel v/s JobRole gender wise.)\n",
    "\n",
    "sns.set(style='whitegrid', font_scale= 1)\n",
    "sns.catplot(x=\"JobLevel\", y=\"JobRole\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=6, aspect=1.1, palette='seismic',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d379b2d",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "In this barplot the attrition of male employees are from research director's job role whoes joblevel is 5 and also for female employees the attrition is from research director's role with same joblevel 5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "91d4c9bb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be942c2f70>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1640x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 10 (Attrition of employees for EnvironmentSatisfaction v/s EducationField gender wise.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"EnvironmentSatisfaction\", y=\"EducationField\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=6, aspect=1.3, palette='turbo_r',col_order = ['Male','Female'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96becd0d",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "In this barplot the attrition of male employees are from other educational fields with environment satisfaction 3.2 and for female employees attrition it is from same other educationfield with environmentsatisfaction 3.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "a038c9dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be9b6f40d0>"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 11 (Attrition of employees for BusinessTravel v/s JobLevel gender wise.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"BusinessTravel\", y=\"JobLevel\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=5, aspect=1.2, palette='copper',col_order = ['Male','Female'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca599897",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "In this barplot the attrition of male employees are non travellers whoes job levels are between 1.5 and 2 and female employees attrition are also non travelers whoes job levels are 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "0363f658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be9b2c6a30>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2083.88x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 12 (Attrition of employees for YearsInCurrentRole v/s JobRole gender wise.)\n",
    "\n",
    "sns.set(style='whitegrid', font_scale=1.3)\n",
    "sns.catplot(x=\"YearsInCurrentRole\", y=\"JobRole\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=9, aspect=1.1, palette='binary',col_order = ['Male','Female'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9aaf743",
   "metadata": {},
   "source": [
    "CONCLUSION  \n",
    "In this barplot the attrition of male employees are research directors playing same role from more than 14 years and also same for female employees research directors playing same role from more than 14 years. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "2647a961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2be9bbb93a0>"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1120x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 13 (Attrition of employees for MaritalStatus v/s MonthlyIncome gender wise.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"MaritalStatus\", y=\"MonthlyIncome\",hue=\"Attrition\", col=\"Gender\",\n",
    "             data=hr_data, kind=\"bar\",height=4, aspect=1.3, palette='twilight',col_order = ['Male','Female'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033fa95b",
   "metadata": {},
   "source": [
    "CONCLUSION  \n",
    "This barplot shows the attrition of male employees which are married and have monthlyincome 5500 and also married females with monthlyincome 6000 have done attrition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "b7af0336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bf02b7d340>"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1680x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 14 (Attrition of employees for JobRole v/s MonthlyIncome.)\n",
    "\n",
    "sns.set(style='darkgrid', font_scale= 1)\n",
    "sns.catplot(x=\"JobRole\", y=\"MonthlyIncome\",\n",
    "             data=hr_data, kind=\"bar\",height=8, aspect=2.1, palette='twilight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04e158c",
   "metadata": {},
   "source": [
    "CONCLUSION    \n",
    "This barplot shows the highest monthly income is for Manager job role."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "97ce40ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2bf01f1ed60>"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot no 15 (Attrition of employees for JobSatisfaction v/s MonthlyIncome.)\n",
    "\n",
    "sns.set(style='ticks', font_scale= 1)\n",
    "sns.catplot(x=\"JobSatisfaction\", y=\"MonthlyIncome\",\n",
    "             data=hr_data, kind=\"bar\",height=5, aspect=1.2, palette='Greens')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46f977f",
   "metadata": {},
   "source": [
    "CONCLUSION   \n",
    "it shows that employees with monthly income more than 6500 have job satisfaction 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7d768fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}