diff --git a/Major Project Submissions/Major project.ipynb b/Major Project Submissions/Major project.ipynb
new file mode 100644
index 0000000..36c3cc9
--- /dev/null
+++ b/Major Project Submissions/Major project.ipynb	
@@ -0,0 +1,2122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "ffc94891",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn import datasets\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.svm import LinearSVC, SVC\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import precision_score, recall_score\n",
+    "from sklearn.metrics import f1_score\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "232c9185",
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"EmployeeAttrition.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f419585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Analysing/Exploring dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2180f3ec",
+   "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>1465</th>\n",
+       "      <td>36</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Frequently</td>\n",
+       "      <td>884</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>23</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2061</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1466</th>\n",
+       "      <td>39</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>613</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1467</th>\n",
+       "      <td>27</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>155</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Life Sciences</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2064</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1468</th>\n",
+       "      <td>49</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Frequently</td>\n",
+       "      <td>1023</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1469</th>\n",
+       "      <td>34</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>628</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2068</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</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",
+       "1465   36        No  Travel_Frequently        884  Research & Development   \n",
+       "1466   39        No      Travel_Rarely        613  Research & Development   \n",
+       "1467   27        No      Travel_Rarely        155  Research & Development   \n",
+       "1468   49        No  Travel_Frequently       1023                   Sales   \n",
+       "1469   34        No      Travel_Rarely        628  Research & Development   \n",
+       "\n",
+       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
+       "1465                23          2        Medical              1   \n",
+       "1466                 6          1        Medical              1   \n",
+       "1467                 4          3  Life Sciences              1   \n",
+       "1468                 2          3        Medical              1   \n",
+       "1469                 8          3        Medical              1   \n",
+       "\n",
+       "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
+       "1465            2061  ...                         3            80   \n",
+       "1466            2062  ...                         1            80   \n",
+       "1467            2064  ...                         2            80   \n",
+       "1468            2065  ...                         4            80   \n",
+       "1469            2068  ...                         1            80   \n",
+       "\n",
+       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
+       "1465                 1                 17                      3   \n",
+       "1466                 1                  9                      5   \n",
+       "1467                 1                  6                      0   \n",
+       "1468                 0                 17                      3   \n",
+       "1469                 0                  6                      3   \n",
+       "\n",
+       "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
+       "1465               3               5                  2   \n",
+       "1466               3               7                  7   \n",
+       "1467               3               6                  2   \n",
+       "1468               2               9                  6   \n",
+       "1469               4               4                  3   \n",
+       "\n",
+       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
+       "1465                        0                     3  \n",
+       "1466                        1                     7  \n",
+       "1467                        0                     3  \n",
+       "1468                        0                     8  \n",
+       "1469                        1                     2  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a47aea72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1470, 35)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3e382536",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bd4df800",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
+       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
+       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
+       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
+       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
+       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
+       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
+       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
+       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
+       "       'YearsWithCurrManager'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0ba4404c",
+   "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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c22c7fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Male      882\n",
+       "Female    588\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender=data[\"Gender\"].value_counts()\n",
+    "gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0da23561",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1]), [Text(0, 0, 'Female'), Text(1, 0, 'Male')])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data['Gender'].value_counts().plot(kind='bar',color=['purple','skyblue'],title=\"Graph For Different genders\",figsize=(4,5))\n",
+    "pd.crosstab(data['Gender'],data['Attrition']).plot(kind=\"bar\",color=['purple','skyblue'],figsize=(5,6))\n",
+    "plt.title(\"Staying/left the company vs Gender\")\n",
+    "plt.xlabel(\"Stay/Left\")\n",
+    "plt.ylabel(\"No of people who left the company based on gender\")\n",
+    "plt.legend([\"Left\",\"Stay\"])\n",
+    "plt.xticks(rotation=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "db9b66e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Research & Development    961\n",
+       "Sales                     446\n",
+       "Human Resources            63\n",
+       "Name: Department, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dep=data[\"Department\"].value_counts()\n",
+    "dep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "01f04007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1, 2]),\n",
+       " [Text(0, 0, 'Human Resources'),\n",
+       "  Text(1, 0, 'Research & Development'),\n",
+       "  Text(2, 0, 'Sales')])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data['Department'].value_counts().plot(kind='bar',color=['purple','skyblue'],title=\"Departments in the company\",figsize=(5,6))\n",
+    "pd.crosstab(data['Department'],data['Attrition']).plot(kind=\"bar\",figsize=(5,6),color=['purple','skyblue'])\n",
+    "plt.title(\"Staying/left the company vs Department\")\n",
+    "plt.xlabel(\"Stay/Left\")\n",
+    "plt.ylabel(\"No of people who left the company based on department\")\n",
+    "plt.legend([\"Left\",\"Stay\"])\n",
+    "plt.xticks(rotation=360)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c882c442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Age group of the company'}>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data['Age'].value_counts().plot(kind='bar',color=['purple','skyblue'],title=\"Age group of the company\",figsize=(12,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f0bd561",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "        34, 35, 36, 37, 38, 39, 40, 41, 42]),\n",
+       " [Text(0, 0, '18'),\n",
+       "  Text(1, 0, '19'),\n",
+       "  Text(2, 0, '20'),\n",
+       "  Text(3, 0, '21'),\n",
+       "  Text(4, 0, '22'),\n",
+       "  Text(5, 0, '23'),\n",
+       "  Text(6, 0, '24'),\n",
+       "  Text(7, 0, '25'),\n",
+       "  Text(8, 0, '26'),\n",
+       "  Text(9, 0, '27'),\n",
+       "  Text(10, 0, '28'),\n",
+       "  Text(11, 0, '29'),\n",
+       "  Text(12, 0, '30'),\n",
+       "  Text(13, 0, '31'),\n",
+       "  Text(14, 0, '32'),\n",
+       "  Text(15, 0, '33'),\n",
+       "  Text(16, 0, '34'),\n",
+       "  Text(17, 0, '35'),\n",
+       "  Text(18, 0, '36'),\n",
+       "  Text(19, 0, '37'),\n",
+       "  Text(20, 0, '38'),\n",
+       "  Text(21, 0, '39'),\n",
+       "  Text(22, 0, '40'),\n",
+       "  Text(23, 0, '41'),\n",
+       "  Text(24, 0, '42'),\n",
+       "  Text(25, 0, '43'),\n",
+       "  Text(26, 0, '44'),\n",
+       "  Text(27, 0, '45'),\n",
+       "  Text(28, 0, '46'),\n",
+       "  Text(29, 0, '47'),\n",
+       "  Text(30, 0, '48'),\n",
+       "  Text(31, 0, '49'),\n",
+       "  Text(32, 0, '50'),\n",
+       "  Text(33, 0, '51'),\n",
+       "  Text(34, 0, '52'),\n",
+       "  Text(35, 0, '53'),\n",
+       "  Text(36, 0, '54'),\n",
+       "  Text(37, 0, '55'),\n",
+       "  Text(38, 0, '56'),\n",
+       "  Text(39, 0, '57'),\n",
+       "  Text(40, 0, '58'),\n",
+       "  Text(41, 0, '59'),\n",
+       "  Text(42, 0, '60')])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.crosstab(data['Age'],data['Attrition']).plot(kind=\"bar\",figsize=(15,6))\n",
+    "plt.title(\"Staying/left the company vs Age\")\n",
+    "plt.xlabel(\"Stay/Left\")\n",
+    "plt.ylabel(\"No of people who left the company based on age\")\n",
+    "plt.legend([\"Left\",\"Stay\"])\n",
+    "plt.xticks(rotation=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "64a4c032",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def onehot_encode(df, column):\n",
+    "    df = df.copy()\n",
+    "    dummies = pd.get_dummies(df[column], prefix=column)\n",
+    "    df = pd.concat([df, dummies], axis=1)\n",
+    "    df = df.drop(column, axis=1)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d63fca5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_inputs(df):\n",
+    "    df = df.copy()\n",
+    "    \n",
+    "    # Drop single-value columns and id columns\n",
+    "    df = df.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis=1)\n",
+    "    \n",
+    "    # Binary-encode binary columns\n",
+    "    df['Gender'] = df['Gender'].replace({'Female': 0, 'Male': 1})\n",
+    "    df['OverTime'] = df['OverTime'].replace({'No': 0, 'Yes': 1})\n",
+    "    df['Attrition'] = df['Attrition'].replace({'No': 0, 'Yes': 1})\n",
+    "    \n",
+    "    # Ordinal-encode the BusinessTravel column\n",
+    "    df['BusinessTravel'] = df['BusinessTravel'].replace({'Non-Travel': 0, 'Travel_Rarely': 1, 'Travel_Frequently': 2})\n",
+    "    \n",
+    "    # One-hot encoding\n",
+    "    for column in ['Department', 'EducationField', 'JobRole', 'MaritalStatus']:\n",
+    "        df = onehot_encode(df, column=column)\n",
+    "    \n",
+    "    # Split df into X and y\n",
+    "    y = df['Attrition']\n",
+    "    X = df.drop('Attrition', axis=1)\n",
+    "    \n",
+    "    # Train-test split\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=1)\n",
+    "    \n",
+    "    # Scale X\n",
+    "    scaler = StandardScaler()\n",
+    "    scaler.fit(X_train)\n",
+    "    X_train = pd.DataFrame(scaler.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
+    "    X_test = pd.DataFrame(scaler.transform(X_test), index=X_test.index, columns=X_test.columns)\n",
+    "    \n",
+    "    return X_train, X_test, y_train, y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f0fd71e8",
+   "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>BusinessTravel</th>\n",
+       "      <th>DailyRate</th>\n",
+       "      <th>DistanceFromHome</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>EnvironmentSatisfaction</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>HourlyRate</th>\n",
+       "      <th>JobInvolvement</th>\n",
+       "      <th>JobLevel</th>\n",
+       "      <th>...</th>\n",
+       "      <th>JobRole_Laboratory Technician</th>\n",
+       "      <th>JobRole_Manager</th>\n",
+       "      <th>JobRole_Manufacturing Director</th>\n",
+       "      <th>JobRole_Research Director</th>\n",
+       "      <th>JobRole_Research Scientist</th>\n",
+       "      <th>JobRole_Sales Executive</th>\n",
+       "      <th>JobRole_Sales Representative</th>\n",
+       "      <th>MaritalStatus_Divorced</th>\n",
+       "      <th>MaritalStatus_Married</th>\n",
+       "      <th>MaritalStatus_Single</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0.762337</td>\n",
+       "      <td>-2.005534</td>\n",
+       "      <td>-0.816540</td>\n",
+       "      <td>1.742140</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>-0.669480</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>0.055811</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.128910</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>785</th>\n",
+       "      <td>0.326292</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>1.652087</td>\n",
+       "      <td>1.366162</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>-1.586784</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>-0.239963</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>0.838814</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>918</th>\n",
+       "      <td>1.525416</td>\n",
+       "      <td>1.722911</td>\n",
+       "      <td>-1.436773</td>\n",
+       "      <td>-0.012423</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>0.844541</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>2.642657</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>1.884966</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1335</th>\n",
+       "      <td>0.217281</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>0.035051</td>\n",
+       "      <td>1.240836</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>-1.225876</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>2.017168</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>1.884966</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>-0.109753</td>\n",
+       "      <td>-2.005534</td>\n",
+       "      <td>0.180264</td>\n",
+       "      <td>-1.015031</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>-1.620241</td>\n",
+       "      <td>-0.998124</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>2.982320</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>-0.436787</td>\n",
+       "      <td>1.722911</td>\n",
+       "      <td>0.015361</td>\n",
+       "      <td>-1.015031</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>0.247824</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>0.893837</td>\n",
+       "      <td>1.766532</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>905</th>\n",
+       "      <td>-0.872832</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>-0.311985</td>\n",
+       "      <td>-1.015031</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>1.041724</td>\n",
+       "      <td>-0.998124</td>\n",
+       "      <td>1.740736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>1.884966</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1096</th>\n",
+       "      <td>0.326292</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>0.190109</td>\n",
+       "      <td>-0.388401</td>\n",
+       "      <td>-0.892452</td>\n",
+       "      <td>0.247824</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>-1.373763</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>1.740736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>1.448930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>0.653326</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>0.524838</td>\n",
+       "      <td>0.864859</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>0.696654</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>1.740736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1061</th>\n",
+       "      <td>-1.417888</td>\n",
+       "      <td>-2.005534</td>\n",
+       "      <td>0.022744</td>\n",
+       "      <td>0.488881</td>\n",
+       "      <td>-0.892452</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>0.598063</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>-0.965030</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>4.208217</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1029 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Age  BusinessTravel  DailyRate  DistanceFromHome  Education  \\\n",
+       "99    0.762337       -2.005534  -0.816540          1.742140   0.092884   \n",
+       "785   0.326292       -0.141311   1.652087          1.366162   1.078220   \n",
+       "918   1.525416        1.722911  -1.436773         -0.012423   0.092884   \n",
+       "1335  0.217281       -0.141311   0.035051          1.240836   1.078220   \n",
+       "1182 -0.109753       -2.005534   0.180264         -1.015031   1.078220   \n",
+       "...        ...             ...        ...               ...        ...   \n",
+       "715  -0.436787        1.722911   0.015361         -1.015031   1.078220   \n",
+       "905  -0.872832       -0.141311  -0.311985         -1.015031   0.092884   \n",
+       "1096  0.326292       -0.141311   0.190109         -0.388401  -0.892452   \n",
+       "235   0.653326       -0.141311   0.524838          0.864859   0.092884   \n",
+       "1061 -1.417888       -2.005534   0.022744          0.488881  -0.892452   \n",
+       "\n",
+       "      EnvironmentSatisfaction    Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
+       "99                  -0.669480  0.843888    0.055811        0.384204 -0.063108   \n",
+       "785                 -1.586784  0.843888   -0.239963        0.384204  0.838814   \n",
+       "918                  1.165128  0.843888    0.844541        0.384204  2.642657   \n",
+       "1335                 1.165128  0.843888   -1.225876        0.384204 -0.063108   \n",
+       "1182                 1.165128 -1.184992   -1.620241       -0.998124 -0.063108   \n",
+       "...                       ...       ...         ...             ...       ...   \n",
+       "715                  0.247824 -1.184992    0.893837        1.766532 -0.063108   \n",
+       "905                  1.165128 -1.184992    1.041724       -0.998124  1.740736   \n",
+       "1096                 0.247824  0.843888   -1.373763        0.384204  1.740736   \n",
+       "235                  1.165128 -1.184992    0.696654        0.384204  1.740736   \n",
+       "1061                 1.165128 -1.184992    0.598063        0.384204 -0.965030   \n",
+       "\n",
+       "      ...  JobRole_Laboratory Technician  JobRole_Manager  \\\n",
+       "99    ...                       2.128910        -0.255377   \n",
+       "785   ...                      -0.469724        -0.255377   \n",
+       "918   ...                      -0.469724         3.915780   \n",
+       "1335  ...                      -0.469724        -0.255377   \n",
+       "1182  ...                      -0.469724        -0.255377   \n",
+       "...   ...                            ...              ...   \n",
+       "715   ...                      -0.469724        -0.255377   \n",
+       "905   ...                      -0.469724        -0.255377   \n",
+       "1096  ...                      -0.469724         3.915780   \n",
+       "235   ...                      -0.469724         3.915780   \n",
+       "1061  ...                      -0.469724        -0.255377   \n",
+       "\n",
+       "      JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
+       "99                         -0.335309                  -0.255377   \n",
+       "785                        -0.335309                  -0.255377   \n",
+       "918                        -0.335309                  -0.255377   \n",
+       "1335                       -0.335309                  -0.255377   \n",
+       "1182                        2.982320                  -0.255377   \n",
+       "...                              ...                        ...   \n",
+       "715                        -0.335309                  -0.255377   \n",
+       "905                        -0.335309                   3.915780   \n",
+       "1096                       -0.335309                  -0.255377   \n",
+       "235                        -0.335309                  -0.255377   \n",
+       "1061                       -0.335309                  -0.255377   \n",
+       "\n",
+       "      JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
+       "99                     -0.495745                -0.533521   \n",
+       "785                    -0.495745                -0.533521   \n",
+       "918                    -0.495745                -0.533521   \n",
+       "1335                    2.017168                -0.533521   \n",
+       "1182                   -0.495745                -0.533521   \n",
+       "...                          ...                      ...   \n",
+       "715                    -0.495745                -0.533521   \n",
+       "905                    -0.495745                -0.533521   \n",
+       "1096                   -0.495745                -0.533521   \n",
+       "235                    -0.495745                -0.533521   \n",
+       "1061                   -0.495745                -0.533521   \n",
+       "\n",
+       "      JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
+       "99                       -0.237630               -0.530514   \n",
+       "785                      -0.237630               -0.530514   \n",
+       "918                      -0.237630                1.884966   \n",
+       "1335                     -0.237630                1.884966   \n",
+       "1182                     -0.237630               -0.530514   \n",
+       "...                            ...                     ...   \n",
+       "715                      -0.237630               -0.530514   \n",
+       "905                      -0.237630                1.884966   \n",
+       "1096                     -0.237630               -0.530514   \n",
+       "235                      -0.237630               -0.530514   \n",
+       "1061                      4.208217               -0.530514   \n",
+       "\n",
+       "      MaritalStatus_Married  MaritalStatus_Single  \n",
+       "99                 1.088445             -0.690165  \n",
+       "785                1.088445             -0.690165  \n",
+       "918               -0.918742             -0.690165  \n",
+       "1335              -0.918742             -0.690165  \n",
+       "1182               1.088445             -0.690165  \n",
+       "...                     ...                   ...  \n",
+       "715                1.088445             -0.690165  \n",
+       "905               -0.918742             -0.690165  \n",
+       "1096              -0.918742              1.448930  \n",
+       "235                1.088445             -0.690165  \n",
+       "1061               1.088445             -0.690165  \n",
+       "\n",
+       "[1029 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Training and testing data\n",
+    "X_train, X_test, y_train, y_test = preprocess_inputs(data)\n",
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "dfd1083f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99      0\n",
+       "785     0\n",
+       "918     0\n",
+       "1335    0\n",
+       "1182    0\n",
+       "       ..\n",
+       "715     0\n",
+       "905     0\n",
+       "1096    0\n",
+       "235     0\n",
+       "1061    0\n",
+       "Name: Attrition, Length: 1029, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6ff446f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   Logistic Regression trained.\n",
+      "                   K-Nearest Neighbors trained.\n",
+      "                         Decision Tree trained.\n",
+      "Support Vector Machine (Linear Kernel) trained.\n",
+      "   Support Vector Machine (RBF Kernel) trained.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Inno\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:985: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
+      "C:\\Users\\Inno\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                        Neural Network trained.\n",
+      "                         Random Forest trained.\n",
+      "                     Gradient Boosting trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "models = {\n",
+    "    \"                   Logistic Regression\": LogisticRegression(),\n",
+    "    \"                   K-Nearest Neighbors\": KNeighborsClassifier(),\n",
+    "    \"                         Decision Tree\": DecisionTreeClassifier(),\n",
+    "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
+    "    \"   Support Vector Machine (RBF Kernel)\": SVC(),\n",
+    "    \"                        Neural Network\": MLPClassifier(),\n",
+    "    \"                         Random Forest\": RandomForestClassifier(),\n",
+    "    \"                     Gradient Boosting\": GradientBoostingClassifier()\n",
+    "}\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    model.fit(X_train, y_train)\n",
+    "    print(name + \" trained.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b7290d23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   Logistic Regression: 86.62%\n",
+      "                   K-Nearest Neighbors: 82.09%\n",
+      "                         Decision Tree: 78.00%\n",
+      "Support Vector Machine (Linear Kernel): 85.49%\n",
+      "   Support Vector Machine (RBF Kernel): 84.81%\n",
+      "                        Neural Network: 84.35%\n",
+      "                         Random Forest: 83.90%\n",
+      "                     Gradient Boosting: 85.49%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Choosing model\n",
+    "for name, model in models.items():\n",
+    "    print(name + \": {:.2f}%\".format(model.score(X_test, y_test) * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "410c0f55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression()\n",
+      "[[352  12]\n",
+      " [ 47  30]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.88      0.97      0.92       364\n",
+      "           1       0.71      0.39      0.50        77\n",
+      "\n",
+      "    accuracy                           0.87       441\n",
+      "   macro avg       0.80      0.68      0.71       441\n",
+      "weighted avg       0.85      0.87      0.85       441\n",
+      "\n",
+      "Accuracy: 0.866\n"
+     ]
+    },
+    {
+     "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": [
+    "#Confusion matrix of Logistic Regression model\n",
+    "logmodel = LogisticRegression()\n",
+    "print(logmodel.fit(X_train, y_train) )\n",
+    "\n",
+    "y_pred1 = logmodel.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix = confusion_matrix(y_test, y_pred1)\n",
+    "print(confusion_matrix)\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,y_pred1))\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred1))\n",
+    "\n",
+    "# Print the confusion matrix using Matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.matshow(confusion_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
+    "for i in range(confusion_matrix.shape[0]):\n",
+    "    for j in range(confusion_matrix.shape[1]):\n",
+    "        ax.text(x=j, y=i,s=confusion_matrix[i, j], va='center', ha='center', size='xx-large')\n",
+    " \n",
+    "plt.xlabel('Predictions', fontsize=18)\n",
+    "plt.ylabel('Actuals', fontsize=18)\n",
+    "plt.title('Confusion Matrix', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "95a764f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[352  12]\n",
+      " [ 48  29]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.88      0.97      0.92       364\n",
+      "           1       0.71      0.38      0.49        77\n",
+      "\n",
+      "    accuracy                           0.86       441\n",
+      "   macro avg       0.79      0.67      0.71       441\n",
+      "weighted avg       0.85      0.86      0.85       441\n",
+      "\n",
+      "Accuracy: 0.864\n"
+     ]
+    },
+    {
+     "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": [
+    "#Confusion matrix of Support Vector Machine(Linear Kernel) model\n",
+    "from sklearn.svm import SVC\n",
+    "Classifier = SVC (kernel='linear', random_state=0)\n",
+    "Classifier.fit (X_train, y_train)\n",
+    "\n",
+    "y_pred2 = Classifier.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix = confusion_matrix(y_test, y_pred2)\n",
+    "print(confusion_matrix)\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,y_pred2))\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred2))\n",
+    "\n",
+    "# Print the confusion matrix using Matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.matshow(confusion_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
+    "for i in range(confusion_matrix.shape[0]):\n",
+    "    for j in range(confusion_matrix.shape[1]):\n",
+    "        ax.text(x=j, y=i,s=confusion_matrix[i, j], va='center', ha='center', size='xx-large')\n",
+    " \n",
+    "plt.xlabel('Predictions', fontsize=18)\n",
+    "plt.ylabel('Actuals', fontsize=18)\n",
+    "plt.title('Confusion Matrix', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "bf091ab6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[362   2]\n",
+      " [ 65  12]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.85      0.99      0.92       364\n",
+      "           1       0.86      0.16      0.26        77\n",
+      "\n",
+      "    accuracy                           0.85       441\n",
+      "   macro avg       0.85      0.58      0.59       441\n",
+      "weighted avg       0.85      0.85      0.80       441\n",
+      "\n",
+      "Accuracy: 0.848\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LI94CYIt6dQGoV2fzNfrWq735aluNxZYuW8Z5V9/A6LHfcWe/y2i/y845rVnyw9KlS+l51J/58KOPeOapwXTs2CHpkvJekiE4CWiW8ropMDm9k7s/4O67u/vutWvVKrfikrRk6TIA5i+Iwm3nbaMz+9NmzFqj79QZM6mT9rksX7GCC6+9hQ8++5KbL7uQA/Zun+OKJR8sX76cY447geEj3uSxQQPp3v3QpEuqEJIMwU+AVmbW0syqAMcBLyVYT7mbNWduxvan/vMqADtv1xqATvvuRbWqVXn21TdYsWLFqn5vf/QJ02bO4k97tFvVtnLlSi696Q7efP8j+l9wDoce2DF3b0DyxsqVK/m/3qfy4kv/4b577ua4Y49JuqQKI7Fjgu6+3MzOA14nGiIz0N2/SaqeJPS7427mzl9A+113pmH9eixY+Dvvf/YFH3z2JW133H7VMcA6m9fiL6ecyK33DaTXRVfSteO+TJ81m8eff4mmjRrQ6+gjVi3zlvse5pWRb7PHLjtRrWpVXhr+1mrr3Ge3XalXR8MlNjYXX3IpQ556mo4dOlBQUMATTw5ebXqXzp1o0KBBQtXlt0THCbr7q8CrSdaQpG4HduCF19/k2VffYM68+Wy66aa0bNaEi8/ozclHHs6mKSP8Tz3mSDavWZNHnx3GrfcNpLB6AQd3/BN/69OLWpv9MfZv7A8/AvDJ6DEZT5g8eseNCsGN0OdffAnA2++8w9vvvLPG9LdGvKEQXAtdNicbjZAvm5OS6bI5EZG1UAiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgErdQhaGbtzez0tLYeZva1mf1mZjeWfXkiIrmVzZZgP+Dw4hdm1hwYAjQE5gGXmtkpZVueiEhuZROCuwDvp7w+DjBgV3ffAXgDOKMMaxMRyblsQrAuMDXl9cHAO+7+W/z6JaBVWRUmIlIesgnBuUADADOrCuwFvJMy3YGCMqtMRKQcVM6i75dAHzMbAfQEqgGvp0xvCUwru9JERHIvmxC8jui43/+IjgUOd/dPU6Z3Bz4uw9pERHKu1CHo7h+YWTuiY4HzgKeKp5lZXaKAfKHMKxQRyaFstgRx93HAuAzts4ALy6ooEZHyoitGRCRoa90SNLOR67E8d/dOG1CPiEi5Kml3eCuiYS8iIhuttYagu7coxzpERBKhY4IiEjSFoIgELashMmZWGzgN2BOozZohqhMjIlKhlDoEzWxLorvINCYaLF0TmM0fYTgT+D0HNYqI5Ew2u8PXA5sDnYjuFmPAsURheBOwANivjOsTEcmpbEKwE/Cgu7/FH0NnzN0XufuVwNfALWVdoIhILmV7P8Ex8c/L4ufUW2cNB7qURVEiIuUlmxCcAdSJf14ALAZapEyvgu4nKCIVTDYh+A3RLfZxdye6pdY5ZtbczFoQ3Vr/uzKvUEQkh7IZIvMicJGZFbh7EXAt0U1VJ8TTHTiyjOsTEcmpbO4neA9wT8rrkWa2N3ACsAJ4wd0/KPsSRURyJ6vB0uniO0t/us6OIiJ5SpfNiUjQsrliZGApurm7n7YB9YiIlKtsdod7l6KPE11bLCJSIZR6d9jdK6U/gE2BbYEHgY+IriMWEakwNuiYoLuvcPcf3P1MYBa6bE5EKpgNOjuc5r9Af+DsMlzmaqoVFtK6/Z65WrxUcL5yRdIlSL4q4Q+FlOXZ4bpAjTJcnohIzm3wlqCZbQ50Jvq7w59t6PJERMpTNkNkVrL2jUojusHq38qiKBGR8pLNluBjrBmCThR+44Ah7r6grAoTESkP2Vw73DuHdYiIJKLUJ0bM7Goz26mE6Tua2dVlU5aISPnI5uxwf6BNCdN3AvptUDUiIuWsLIfIVAOWl+HyRERyrsRjgmZWk+gvzBWra2bNM3StA5wI/Fp2pYmI5N66ToxcCBQf53PgzviRiQF9y6QqEZFysq4QHBU/G1EYvgB8ldbHgYXAR7qztIhUNCWGoLu/DbwNYGZbAve5+8flUZiISHnIZpzgKbksREQkCdmMEzzXzEaUMP0NMzuzbMoSESkf2QyR6Q38UML0ccCpG1SNiEg5yyYEWwFflzD9m7iPiEiFkU0Ibko0IHptqq1juohI3skmBMcBXUqYfhDw44aVIyJSvrIJwSHAQWZ2nZlVKW40s03N7BqiEBxc1gWKiORSNvcT/CdwCHAlcLaZfUc0UHp7osvm3gVuL/MKRURyKJs/ubmMaGvvMmAS0BZoR3S9cF+gE9GVJSIiFUZWd5Fx92Xufqu77+ruhfGjLfAWcBcwOSdViojkyHr/oSUzqwOcBJxGdC9BIzp5IiJSYWR9P0EzO9jMngZ+IzpOWAW4BtjZ3bcr4/pERHKqVFuCZtYSOAXoBTQFZgDPAicAV7r78zmrUEQkh0rcEjSzE8zsTaLL5foCnwI9gSZEW386ESIiFdq6tgSfAH4CLgAGu/vs4glmtra/QSwiUmGs65jgUqAF0AM4xMwKcl6RiEg5WlcINiTaCqwLPA5MM7OHzawD2hUWkY1AiSHo7nPd/W53bwfsThSERxCNC3yP6IqRWrkuUkQkV7K5YuRzdz8XaAycTHTrLICHzOxLM7vKzHbMRZEiIrmS9ThBd1/i7oPdvROwNXADUBu4FhhdxvWJiOTUBv3xdXef6O5XE5086QZovKCIVCjrfdlcKnd34LX4ISJSYWzQlqCISEWnEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEBSRoCkERSRoCkERCZpCUESCphAUkaApBEUkaApBEQmaQlBEgqYQFJGgKQRFJGgKQREJmkJQRIKmEMxjk6dM5Yzz/kaz1m2oVqcJzVq34egTejN//gIAJv78C5Vq1M/46HPuBckWLzmzcOFC+l97PYcfcRSNm7ekUpXqnHLaGWv0++zzz7ngokvYpV17atbZgkbNWtD54G6MeHNkAlXnr8pJFyCZfff9D+zftQebbVbIGaf2oknjhkyfMZP3P/yYRUWLqFlzs1V9e3Q/hKOOOGy1+bfZqmV5lyzlZObMWVx7/Y00atSQ3du14+VX/5ux3z9uv5M33xrFkT17cO45Z7Jw4e8MevRxDjqkOwP+fSdnn7lmcIZIIZiH3J2T+5xN0yaNGPXai9SoUaPE/jtuvx0nHffncqpOktaoUUN+nfADTZo0Yfny5VSpXjNjv/PPO4dHHn6AatWqrWo7+8zTabv7Xlx19TWcftqpVK6sCNDucB4aOepdPvtiNP2vvJQaNWpQVFTEsmXLSpynqKiIoqKicqpQklS1alWaNGmyzn777L3XagEIUFBQwKGHHsKcOXOYOnVqrkqsUBILQTMbaGbTzWxMUjXkq9fffAuAwurV2fuArhTWb05B3aZ06taTb8Z+t0b/u+59gML6zSms35zWu7RnwP0Pl3fJUoFMmTyFypUrU7t27aRLyQtJbgkOAromuP689cP4HwE49v/60LRxI4Y+/jC333QtX40ZS8euh/Pb5CkAVKpUiU77d+DG/lfx4tAnuPdf/2DzWrU4/6LLuOTK/gm+A8lXY8d+y/PDXuTw7odSWFiYdDl5IbEDAu7+jpm1SGr9+Wzhwt8BaLvLzjzz5COr2ndvtyv7denO7Xfdwx03X0fzZk0Z/vJzq83bp/dJdOrWk3/++17OOq0XW+sEicTmz5/PMcefRPXq1bnjH7cmXU7e0DHBPFRQEB3HOeHYo1Zr33fvPWmxZXPeee+Dtc67ySabcNFfz2XlypW8OerdnNYpFUdRURGH9zyanyZM4IVnn6Z582ZJl5Q38j4EzewMM/vUzD6dMXNW0uWUi0YNGwLQsMEWa0xrsEV95sydV+L8WzZvCsDMWWF8XlKypUuXcuSfj+XDjz5m6JAn6Nhhv6RLyit5H4Lu/oC77+7uu9evVzfpcsrFHrvtCsCk3yavMW3Sb5NZ1+cw/scJAGxRv16Z1yYVy/Llyzn2+JMYPmIkjw58iO6Hdku6pLyT9yEYoh6HHkJBQQEDHxvMihUrVrW/+vpwfps8hYM6HQDA7Nlz1ph38eLF3PSPO6lcufKqfhKmlStX0uuUPrz4n5e5d8BdHHesxpJmktiJETMbAuwP1DOzSUA/d9fYDqB+/Xpc+/fLuOSKfnTq1pOjex7O5KlTueueB2nZYksuPO8sAC6+4mp++fU39tmrPc2aNmHa9Ok8PmQoP4z/ieuuvpzmzZom/E4kV+6+517mzp3HypUrAfjq66+5/sabATi8+6G0abMzF196OUOeHkrHDvtRUK2AJ54cstoyunQ+kAYNGpR77fnG3D3pGkpt93a7+ifvjki6jHIz6Ikh3Hn3/Xz/w3g2q1GDQ7t25sZrrlp1zHDI0Od58JHH+Pb7ccyeM5fq1Qto22ZnzjurD0f26J5w9QmoEs6Qj5attuPnn3/JOG3gQ/fT+/9O5oDOB/P2O2s/OTZy+Gvs37FDrkrMK3W3aDx+9pw5rTJNUwjKxiOgEJTslBSCOiYoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0BSCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgETSEoIkFTCIpI0Mzdk66h1MxsBvBz0nXkkXrAzKSLkLyk343Vbenu9TNNqFAhKKszs0/dffek65D8o9+N0tPusIgETSEoIkFTCFZsDyRdQC6YWQszczPrX1Jbrta1kdgofzdyQSFYgbl7mf6im9n+cSCkPhaa2Wdm9lcz26Qs11de4qDrb2a7Jl1LeSnr342NWeWkC5C8NAR4FTCgMdAbuBPYETgjoZp+BgqA5esxbwugHzAR+LIMlysbAYWgZPK5uz9R/MLM7gW+BfqY2d/dfVr6DGa2mbsvyFVBHg1jWFxRlisVh3aHZZ3cfT7wIdGW4VZmNtHMRplZWzN73czmAV8V9zezVmb2uJlNMbOlcf/bzKwwfdlm9icze9/MisxsmpndDdTI0G+tx+7M7Cgze8vM5prZIjP73szuMrMqZtYbeCvu+kjKbv6okpZrZpXN7FIzG2tmi81slpm9YGY7r60uM+tuZp/E/afE77lyWv8dzewZM/vNzJaY2dS49kNL8U8hOaAtQVknMzNgm/hl8QDc5sBI4BngOeLgMrPd4va5wP3Ab8AuwF+Afc2so7svi/vuCYwAFgC3xPMcBzyWRW03AFcAY4F/AlOArYGjgKuBd4Ab4z4PAO/Gs66xNZvmSeAYYDhwL9AQOBf40Mz2c/cv0vp3A84B7gMGAj2Ai4E58foxs7pEnw1xv5+JBjXvDuwJvFLa9y1lyN310AN3B9gfcKLwqAfUB9oAD8btH8b9Jsav+2RYxmjgO2CztPae8Ty9U9o+AJYCrVPaqgD/i/v2T2lvkaGtfdw2EqiWtj7jj4sB9k9f9zqW2yVue7p4GXF7G6Jjh+9mmP93oEXa+scAU1LaDo/7HpP0v7Uefzy0OyyZXAPMAKYThdqpwEvAESl9ZgOPpM4U7yq2AQYDVc2sXvEDeI8oKA6K+24B7A286O7jipfh7kuJtuhK48T4+XJ3X+24nsdKuZx0PePnG1KX4e5fAS8DfzKz9Euwhrn7xNT1E+2GNzSz4t37efHzIWZWcz1rkzKmEJRMHiDaGupMFFT13b2Hr35C5Ed3X5E23/bxc3GIpj6mA4VAg7jPVvHzdxnWP7aUdbYi2rIaXcr+pdUSWEl0MijdmJQ+qX7K0HdW/FwXwN3fJtrV7w3MjI+FXmNmO2xwxbLedExQMvnB3Ueso8+iDG0WP98OvLaW+eak9c20tWYZ2jKxtcy/oUq7/lTp/yFkXJ679zKz24iOIf4JuAi40swucPe712O9soEUglKWfoifV5QiRH+Mn7fPMC1TWybfA12JdsH/V0K/bIPyR+DguI6v0qYVb7VNyHKZfxTjPoZoi/JWM9sc+Bi42cwGbMAuvKwn7Q5LWfqC6Mt9lpltlT4xHnZSB8DdpwMfAT3MrHVKnyrAhaVc3+D4+UYzq5phfcVbYAvj5zqlXO6w+PnylGVgZjsRndx4z91nlHJZqfXUMbPVvnPuPpcoUKsD1bJdpmw4bQlKmXF3N7OTic7WfmVmA4FviL7g2wBHApcDg+JZ/gaMAt43swH8MUSmVL+X7v4/M7sFuBT4zMyeBqYSHa87mujs8VyiY4wLgHPMbFHcNt3dR65lucPNbGhcS20ze5k/hsgsJhrusz7+D7jQzF4AxgPLgI5EW51D3b1oPZcrG0AhKGXK3b80s7ZEYXc4cBZRAE0kCr83U/p+aGZdgJuBy4D5ROMO7wW+LuX6LjOz0cB5QF+ivZtfiS77WxT3KTKz44DriS7/qwq8zR9j9jI5Efic6CTG7URntt8G/u7upaotg1FAW6A70IjoOOIEovGEOh6YEN1UVUSCpmOCIhI0haCIBE0hKCJBUwiKSNAUgiISNIWgiARNISgiQVMIikjQFIIiEjSFoIgE7f8B5EpAyHXu7MQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Confusion matrix of Support Vector Machine(RBF Kernel) model\n",
+    "from sklearn.svm import SVC\n",
+    "Classifier = SVC (kernel='rbf', random_state=0)\n",
+    "Classifier.fit (X_train, y_train)\n",
+    "\n",
+    "y_pred3 = Classifier.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix = confusion_matrix(y_test, y_pred3)\n",
+    "print(confusion_matrix)\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,y_pred3))\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred3))\n",
+    "\n",
+    "# Print the confusion matrix using Matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.matshow(confusion_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
+    "for i in range(confusion_matrix.shape[0]):\n",
+    "    for j in range(confusion_matrix.shape[1]):\n",
+    "        ax.text(x=j, y=i,s=confusion_matrix[i, j], va='center', ha='center', size='xx-large')\n",
+    " \n",
+    "plt.xlabel('Predictions', fontsize=18)\n",
+    "plt.ylabel('Actuals', fontsize=18)\n",
+    "plt.title('Confusion Matrix', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f6093b66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_tp_tn_fn_fp(y_test, y_pred):\n",
+    "    tp=sum((y_test == 1) & (y_pred == 1))\n",
+    "    tn=sum((y_test == 0) & (y_pred == 0))\n",
+    "    fn=sum((y_test == 1) & (y_pred == 0))\n",
+    "    fp =sum((y_test == 0) & (y_pred == 1))\n",
+    "    return tp, tn, fp, fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "31fb38e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP for Logistic Regression : 30\n",
+      "TN for Logistic Regression : 352\n",
+      "FP for Logistic Regression : 12\n",
+      "FN for Logistic Regression : 47\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating TP(Total Positive Value),TN(Total Negative Value),FP(False Positive Value),FN(False Negative Value)\n",
+    "#for Logistic Regression model\n",
+    "\n",
+    "tp_lr, tn_lr, fp_lr, fn_lr = compute_tp_tn_fn_fp(y_test, y_pred1)\n",
+    "print('TP for Logistic Regression :', tp_lr)\n",
+    "print('TN for Logistic Regression :', tn_lr)\n",
+    "print('FP for Logistic Regression :', fp_lr)\n",
+    "print('FN for Logistic Regression :', fn_lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1bbf132f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP for Support Vector Machine(Linear Kernel) model : 29\n",
+      "TN for Support Vector Machine(Linear Kernel) model : 352\n",
+      "FP for Support Vector Machine(Linear Kernel) model : 12\n",
+      "FN for Support Vector Machine(Linear Kernel) model : 48\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating TP(Total Positive Value),TN(Total Negative Value),FP(False Positive Value),FN(False Negative Value)\n",
+    "#for Support Vector Machine(Linear Kernel) model\n",
+    "\n",
+    "tp_svml, tn_svml, fp_svml, fn_svml = compute_tp_tn_fn_fp(y_test, y_pred2)\n",
+    "print('TP for Support Vector Machine(Linear Kernel) model :', tp_svml)\n",
+    "print('TN for Support Vector Machine(Linear Kernel) model :', tn_svml)\n",
+    "print('FP for Support Vector Machine(Linear Kernel) model :', fp_svml)\n",
+    "print('FN for Support Vector Machine(Linear Kernel) model :', fn_svml)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "08399b6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP for Support Vector Machine(RBF Kernel) model : 12\n",
+      "TN for Support Vector Machine(RBF Kernel) model : 362\n",
+      "FP for Support Vector Machine(RBF Kernel) model : 2\n",
+      "FN for Support Vector Machine(RBF Kernel) model : 65\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating TP(Total Positive Value),TN(Total Negative Value),FP(False Positive Value),FN(False Negative Value)\n",
+    "#for Support Vector Machine(RBF Kernel) model\n",
+    "\n",
+    "tp_svmr, tn_svmr, fp_svmr, fn_svmr = compute_tp_tn_fn_fp(y_test, y_pred3)\n",
+    "print('TP for Support Vector Machine(RBF Kernel) model :', tp_svmr)\n",
+    "print('TN for Support Vector Machine(RBF Kernel) model :', tn_svmr)\n",
+    "print('FP for Support Vector Machine(RBF Kernel) model :', fp_svmr)\n",
+    "print('FN for Support Vector Machine(RBF Kernel) model :', fn_svmr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ae93f5b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for Logistic Regression : 86.62131519274377\n",
+      "Accuracy for Support Vector Machine(Linear Kernel) model : 86.39455782312925\n",
+      "Accuracy for Support Vector Machine(RBF Kernel) model : 84.80725623582767\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating Accuracy\n",
+    "\n",
+    "print('Accuracy for Logistic Regression :', 100*accuracy_score(y_test, y_pred1))\n",
+    "print('Accuracy for Support Vector Machine(Linear Kernel) model :', 100*accuracy_score(y_test, y_pred2))\n",
+    "print('Accuracy for Support Vector Machine(RBF Kernel) model :', 100*accuracy_score(y_test, y_pred3))   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7d8678e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_lr=math.floor(accuracy_score(y_test, y_pred1)*100)\n",
+    "a_svml=math.floor(accuracy_score(y_test, y_pred2)*100)\n",
+    "a_svmr=math.floor(accuracy_score(y_test, y_pred3)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f36ed05b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision for Logistic Regression : 71.42857142857143\n",
+      "Precision for Support Vector Machine(Linear Kernel) model : 70.73170731707317\n",
+      "Precision for Support Vector Machine(RBF Kernel) model : 85.71428571428571\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating Precision\n",
+    "\n",
+    "print('Precision for Logistic Regression :', 100* precision_score(y_test, y_pred1))\n",
+    "print('Precision for Support Vector Machine(Linear Kernel) model :', 100* precision_score(y_test, y_pred2))\n",
+    "print('Precision for Support Vector Machine(RBF Kernel) model :', 100*precision_score(y_test, y_pred3))   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "492c78ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recall for Logistic Regression : 38.961038961038966\n",
+      "Recall for Support Vector Machine(Linear Kernel) model : 37.66233766233766\n",
+      "Recall for Support Vector Machine(RBF Kernel) model : 15.584415584415584\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating Recall\n",
+    "\n",
+    "print('Recall for Logistic Regression :', 100* recall_score(y_test, y_pred1))\n",
+    "print('Recall for Support Vector Machine(Linear Kernel) model :', 100* recall_score(y_test, y_pred2))\n",
+    "print('Recall for Support Vector Machine(RBF Kernel) model :', 100* recall_score(y_test, y_pred3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "4c1a33a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 score for Logistic Regression : 0.5042016806722689\n",
+      "F1 score for Support Vector Machine(Linear Kernel) model : 0.4915254237288135\n",
+      "F1 score for Support Vector Machine(RBF Kernel) model : 0.26373626373626374\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating F1_score\n",
+    "\n",
+    "print('F1 score for Logistic Regression :', f1_score(y_test, y_pred1))\n",
+    "print('F1 score for Support Vector Machine(Linear Kernel) model :', f1_score(y_test, y_pred2))\n",
+    "print('F1 score for Support Vector Machine(RBF Kernel) model :', f1_score(y_test, y_pred3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "24988b03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f1_lr=math.floor(f1_score(y_test, y_pred1)*100)\n",
+    "f1_svml=math.floor(f1_score(y_test, y_pred2)*100)\n",
+    "f1_svmr=math.floor(f1_score(y_test, y_pred3)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "1d5abd22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Models Vs Accuracy')"
+      ]
+     },
+     "execution_count": 50,
+     "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": [
+    "#Graph between model and accuracy\n",
+    "\n",
+    "x=['Logistic Regression', 'SVM Linear Kernel model','SVM RBF Kernel model']\n",
+    "y=[a_lr, a_svml, a_svmr]\n",
+    "plt.plot(x, y)\n",
+    "plt.xlabel('Models')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.title('Models Vs Accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "18c1bf84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Models Vs F1_Score')"
+      ]
+     },
+     "execution_count": 47,
+     "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": [
+    "#Graph between model and F1_scores\n",
+    "\n",
+    "x=['Logistic Regression', 'SVM Linear Kernel model','SVM RBF Kernel model']\n",
+    "y=[f1_lr, f1_svml, f1_svmr]\n",
+    "plt.plot(x, y)\n",
+    "plt.xlabel('Models')\n",
+    "plt.ylabel('F1_scores')\n",
+    "plt.title('Models Vs F1_Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "83628b9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Age': -0.3260826939081251,\n",
+       " 'Attrition': 0.4611595989277241,\n",
+       " 'BusinessTravel': -0.16221752037720052,\n",
+       " 'DailyRate': 0.43704625417327464,\n",
+       " 'Department': 0.09031489074274253,\n",
+       " 'DistanceFromHome': -0.46444194783905574,\n",
+       " 'Education': 0.1693996226081028,\n",
+       " 'EducationField': 0.08312280236173179,\n",
+       " 'EmployeeCount': -0.31992301434626247,\n",
+       " 'EmployeeNumber': 0.1268657664844335,\n",
+       " 'EnvironmentSatisfaction': -0.283339598349285,\n",
+       " 'Gender': -0.059268098375331216,\n",
+       " 'HourlyRate': 0.07458330191319462,\n",
+       " 'JobInvolvement': 0.3389410390224457,\n",
+       " 'JobLevel': 0.8319700671775443,\n",
+       " 'JobRole': -0.08723729937980407,\n",
+       " 'JobSatisfaction': -0.05379654574453324,\n",
+       " 'MaritalStatus': -0.1733332977645172,\n",
+       " 'MonthlyIncome': -0.18723631073972546,\n",
+       " 'MonthlyRate': -0.3250606149642336,\n",
+       " 'NumCompaniesWorked': -0.22862795791419363,\n",
+       " 'Over18': -0.3397720708785684,\n",
+       " 'OverTime': 0.4701244109341639,\n",
+       " 'PercentSalaryHike': -0.4432969594766933,\n",
+       " 'PerformanceRating': 0.536328227110093,\n",
+       " 'RelationshipSatisfaction': -0.5423546391120876,\n",
+       " 'StandardHours': -0.3576498209242578,\n",
+       " 'StockOptionLevel': 0.06182657968233347,\n",
+       " 'TotalWorkingYears': 0.09253293837114485,\n",
+       " 'TrainingTimesLastYear': 0.15966620084268723,\n",
+       " 'WorkLifeBalance': -0.10423383110560046,\n",
+       " 'YearsAtCompany': 0.12370454943996634,\n",
+       " 'YearsInCurrentRole': -0.11911005038701562,\n",
+       " 'YearsSinceLastPromotion': -0.1194460166400527,\n",
+       " 'YearsWithCurrManager': 0.26542095622421474}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Example to show the accuracy of predictions of employee attrition\n",
+    "\n",
+    "characteristic_dict=dict(zip(data.columns,list(logmodel.coef_[0])))\n",
+    "characteristic_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "bcd924dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'characteristic Importance'}>"
+      ]
+     },
+     "execution_count": 64,
+     "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": [
+    "df=pd.DataFrame(characteristic_dict,index=[0])\n",
+    "df.T.plot(kind=\"bar\",legend=False,title=\"characteristic Importance\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0c1f23a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "From above graph, We can clearly infer that job level, overtime and PerformanceRating are the main reasons for employee \n",
+    "attrition. So company should try to improve these areas so as to reduce attrition."
+   ]
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Major Project Submissions/SOLUTION.md b/Major Project Submissions/SOLUTION.md
index c032ba2..34caf4e 100644
--- a/Major Project Submissions/SOLUTION.md	
+++ b/Major Project Submissions/SOLUTION.md	
@@ -1 +1,16 @@
 # SOLUTION
+Team members name: Saloni Choudhary, Kirti Sharma , Janvi Gupta
+
+Ans.1 After performing necessary calculations in the jupyter notebook we get to know that Logistic regression, Support Vector Machine (Linear Kernel) and Support Vector Machine (RBF Kernel) models were the most suitable models for prediction of attrition.
+
+Ans.3 After plotting the graph to study accuracy and f1 scores of these three models we get to know that logistic regression is the best model.
+
+Ans.4 Logistic regression is a statistical analysis method which is used to predict a binary outcome, such as yes or no, based on prior observations of a data set. It predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.
+
+Logistic regression is the best model because:
+1) It has the best accuracy that is upto 86.62% .
+2) It is neither overfitted nor under fitted where underfitted means that your model makes accurate, but initially gives incorrect predictions and Overfitting means that your model does not make accurate predictions.
+3) It is also used to make a prediction about a categorical variable versus a continuous one.
+4) It is used to predict the likelihood of an event. 
+
+Ans.5 From the graph given in jupyter notebook ,We can clearly infer that job level, overtime and PerformanceRating are the main reasons for employee attrition. So company should try to improve these areas so as to reduce attrition.
diff --git a/Major Project with previous problem statements.ipynb b/Major Project with previous problem statements.ipynb
new file mode 100644
index 0000000..6b9a879
--- /dev/null
+++ b/Major Project with previous problem statements.ipynb	
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "301b9cfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing python libraries\n",
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "782c988a",
+   "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": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Reading file in the directory\n",
+    "data = pd.read_csv(\"C:\\\\Users\\\\Kirti\\\\Downloads\\\\WA_Fn-UseC_-HR-Employee-Attrition.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "a6fc9d8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No. of employees between the age of 50 and 60 given that department should be sales and gender should be female: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Q1\n",
+    "#Determine the no. of employees between age 50 and 60 given that department should be sales and gender should be female\n",
+    "a=data[(data['Age']>50) & (data['Department']==\"Sales\") & (data['Gender']==\"Female\")]\n",
+    "n=len(a.value_counts(['Age','Department','Gender']))\n",
+    "print(\"No. of employees between the age of 50 and 60 given that department should be sales and gender should be female:\",n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "def5d0fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Employees with education equal to 1 in Sales Department: 50\n",
+      "Employees with education equal to 1 in Research & Development Department: 115\n",
+      "Employees with education equal to 1 in Human Resources Department: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Q2\n",
+    "#Plot a graph showing relation between education and department given education should be equal to 1 and hence show no. of employees in each department wrt their education which should be equal to 1\n",
+    "a=len(data[(data['Education']==1)&(data['Department']=='Sales')])\n",
+    "b=len(data[(data['Education']==1)&(data['Department']=='Research & Development')])\n",
+    "c=len(data[(data['Education']==1)&(data['Department']=='Human Resources')])\n",
+    "y = np.array([a, b, c])\n",
+    "mylabels = [\"Sales\", \"Research & Development\", \"Human Resources\"]\n",
+    "\n",
+    "plt.pie(y, labels = mylabels)\n",
+    "plt.show() \n",
+    "print(\"Employees with education equal to 1 in Sales Department:\",a)\n",
+    "print(\"Employees with education equal to 1 in Research & Development Department:\",b)\n",
+    "print(\"Employees with education equal to 1 in Human Resources Department:\",c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80613c6c",
+   "metadata": {},
+   "source": [
+    "# Explanation:\n",
+    "1)The blue part of the pie chart is showing employees having education 1 and no. of employees in Sales Department i.e. 50.\n",
+    "2)The orange part of the pie chart is showing employees having education 1 and no. of employees in Research & Development Department i.e.115.\n",
+    "3)The green part of the pie chart is showing employees having education 1 and no. of employees in Human Resource Department i.e.5 .\n",
+    "# Conclusion:\n",
+    "From the above pie chart we can see that most number of employee are from Research and Development Department followed by Sales department and Human Resources Department respectively. All employees have education equal to 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "7f2e02bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gender\n",
+      "Female    3.159864\n",
+      "Male      3.149660\n",
+      "Name: PerformanceRating, dtype: float64\n",
+      "Female have higher Performance Rating as compared to Male\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Q3\n",
+    "#Make a suitable plot showing relationship between performance rating and education. Also compute which gender has a very high performance rating\n",
+    "#Education vs Performance Rating\n",
+    "pd.crosstab(data['Education'],data['PerformanceRating']).plot(kind=\"bar\",figsize=(10,6))\n",
+    "plt.title(\"Education vs Performance Rating\")\n",
+    "plt.xlabel(\"Education\")\n",
+    "plt.legend([\"Excellent\",\"Outstanding\"])\n",
+    "plt.xticks(rotation=0)\n",
+    "#Gender vs Performance Rating\n",
+    "f=data.groupby('Gender')['PerformanceRating'].mean()\n",
+    "print(f)\n",
+    "print('Female have higher Performance Rating as compared to Male')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70706658",
+   "metadata": {},
+   "source": [
+    "# Explanation\n",
+    "The above bar graph shows the relationship between performance rating and education of the employees.\n",
+    "It also shows how many employees with a certain education are performing.\n",
+    "# Conclusion\n",
+    "From The above graph we can conclude that employees having education have high performance rating.\n",
+    "it can also be concluded that Female employees have high performance rating than Male employees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "11e51920",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median\n",
+      " Gender\n",
+      "Female    5081.5\n",
+      "Male      4837.5\n",
+      "Name: MonthlyIncome, dtype: float64\n",
+      "Mean\n",
+      " Gender\n",
+      "Female    6686.566327\n",
+      "Male      6380.507937\n",
+      "Name: MonthlyIncome, dtype: float64\n",
+      "Mode\n",
+      " Gender\n",
+      "Female    4011\n",
+      "Male      2451\n",
+      "Name: MonthlyIncome, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Q4\n",
+    "#Compute the median, average, modal monthly income for both male and female\n",
+    "import statistics\n",
+    "a=data.groupby('Gender')['MonthlyIncome'].median()\n",
+    "b=data.groupby('Gender')['MonthlyIncome'].mean()\n",
+    "c=data.groupby('Gender')['MonthlyIncome'].apply(statistics.mode)\n",
+    "print('Median\\n',a)\n",
+    "print('Mean\\n',b)\n",
+    "print('Mode\\n',c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "6795caa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Job Satisfaction by most of the people= 4 which means that they are highly satisfied.\n",
+      "5081.5    0.5\n",
+      "4837.5    0.5\n",
+      "Name: MonthlyIncome, dtype: float64\n",
+      "People of Sales department are most satisfied.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Q5\n",
+    "#Determine the level of job satisfaction by most of the people and hence compute people of which department are most satisfied\n",
+    "m=data[\"JobSatisfaction\"].max()\n",
+    "print(\"Job Satisfaction by most of the people=\",m,\"which means that they are highly satisfied.\")\n",
+    "d=data[(data[\"JobSatisfaction\"]==m)]\n",
+    "d1=a.value_counts(['Department'])\n",
+    "print(d1)\n",
+    "print(\"People of Sales department are most satisfied.\")"
+   ]
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Major project.ipynb b/Major project.ipynb
new file mode 100644
index 0000000..36c3cc9
--- /dev/null
+++ b/Major project.ipynb	
@@ -0,0 +1,2122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "ffc94891",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn import datasets\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.svm import LinearSVC, SVC\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import precision_score, recall_score\n",
+    "from sklearn.metrics import f1_score\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "232c9185",
+   "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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"EmployeeAttrition.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f419585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Analysing/Exploring dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2180f3ec",
+   "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>1465</th>\n",
+       "      <td>36</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Frequently</td>\n",
+       "      <td>884</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>23</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2061</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1466</th>\n",
+       "      <td>39</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>613</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1467</th>\n",
+       "      <td>27</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>155</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Life Sciences</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2064</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1468</th>\n",
+       "      <td>49</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Frequently</td>\n",
+       "      <td>1023</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2065</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1469</th>\n",
+       "      <td>34</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>628</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2068</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</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",
+       "1465   36        No  Travel_Frequently        884  Research & Development   \n",
+       "1466   39        No      Travel_Rarely        613  Research & Development   \n",
+       "1467   27        No      Travel_Rarely        155  Research & Development   \n",
+       "1468   49        No  Travel_Frequently       1023                   Sales   \n",
+       "1469   34        No      Travel_Rarely        628  Research & Development   \n",
+       "\n",
+       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
+       "1465                23          2        Medical              1   \n",
+       "1466                 6          1        Medical              1   \n",
+       "1467                 4          3  Life Sciences              1   \n",
+       "1468                 2          3        Medical              1   \n",
+       "1469                 8          3        Medical              1   \n",
+       "\n",
+       "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
+       "1465            2061  ...                         3            80   \n",
+       "1466            2062  ...                         1            80   \n",
+       "1467            2064  ...                         2            80   \n",
+       "1468            2065  ...                         4            80   \n",
+       "1469            2068  ...                         1            80   \n",
+       "\n",
+       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
+       "1465                 1                 17                      3   \n",
+       "1466                 1                  9                      5   \n",
+       "1467                 1                  6                      0   \n",
+       "1468                 0                 17                      3   \n",
+       "1469                 0                  6                      3   \n",
+       "\n",
+       "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
+       "1465               3               5                  2   \n",
+       "1466               3               7                  7   \n",
+       "1467               3               6                  2   \n",
+       "1468               2               9                  6   \n",
+       "1469               4               4                  3   \n",
+       "\n",
+       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
+       "1465                        0                     3  \n",
+       "1466                        1                     7  \n",
+       "1467                        0                     3  \n",
+       "1468                        0                     8  \n",
+       "1469                        1                     2  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a47aea72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1470, 35)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3e382536",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bd4df800",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
+       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
+       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
+       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
+       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
+       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
+       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
+       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
+       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
+       "       'YearsWithCurrManager'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0ba4404c",
+   "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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c22c7fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Male      882\n",
+       "Female    588\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender=data[\"Gender\"].value_counts()\n",
+    "gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0da23561",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1]), [Text(0, 0, 'Female'), Text(1, 0, 'Male')])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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T4SCpyXCQ1GQ4SGoyHCQ1GQ6SmgwHSU2Gg6Qmw0FSk+EgqclwkNRkOEhqMhwkNRkOkpoMB0lNhoOkJsNBUpPhIKnJcJDUZDhIajIcJDUZDpKaDAdJTYaDpCbDQVKT4SCpadJwSLIgyZeTrEtyQ5I399MPSXJJkpv724MHljknyfokNyU5eZR3QNJoDHPksAV4S1U9GzgROCvJccDZwJqqWgSs6cfp5y0FjgdOAc5LMmcUxUsanUnDoao2VdU1/fD9wDrgSOBUYGXfbCVwWj98KnBhVT1cVbcC64El01y3pBGb0jmHJAuBFwBXAodV1SboAgQ4tG92JLBhYLGN/TRJu5ChwyHJfsBfA79VVd/bUdPGtGqs78wka5Os3bx587BlSJohQ4VDkt3pguGTVXVRP/nOJIf38w8H7uqnbwQWDCx+FHDHxHVW1flVtbiqFs+fP39n65c0IsNcrQjwEWBdVb1vYNZqYFk/vAy4eGD60iR7JjkGWARcNX0lS5oJc4do8xLgdcDXklzXT/tvwLuBVUnOAG4HTgeoqhuSrAJupLvScVZVbZ3uwiWN1qThUFWX0z6PAHDSdpZZAax4AnVJGjPfISmpyXCQ1GQ4SGoyHCQ1GQ6SmgwHSU2Gg6Qmw0FSk+EgqclwkNRkOEhqMhwkNRkOkpoMB0lNhoOkJsNBUpPhIKnJcJDUZDhIajIcJDUZDpKaDAdJTYaDpCbDQVKT4SCpyXCQ1GQ4SGoyHCQ1GQ6SmgwHSU2Gg6Qmw0FSk+EgqclwkNRkOEhqMhwkNRkOkpoMB0lNhoOkJsNBUpPhIKnJcJDUZDhIajIcJDUZDpKaDAdJTYaDpCbDQVKT4SCpyXCQ1DSycEhySpKbkqxPcvaotiNpNEYSDknmAB8Cfg44Dnh1kuNGsS1JozGqI4clwPqquqWqfghcCJw6om1JGoFRhcORwIaB8Y39NEm7iLkjWm8a0+pxDZIzgTP70QeS3DSiWp4M5gF3z9TGlmf5TG1qtpjRx++cmdrQY45uTRxVOGwEFgyMHwXcMdigqs4Hzh/R9p9UkqytqsXjrkM7Z7Y+fqPqVvwzsCjJMUn2AJYCq0e0LUkjMJIjh6rakuQ3gC8Cc4C/rKobRrEtSaMxqm4FVfV54POjWv8uZlZ0n57CZuXjl6qavJWkWce3T0tqMhwkNRkOkpoMhxFKsneSZ427Dk1dOq9N8nv9+DOSLBl3XTPJcBiRJD8PXAd8oR9/fhLf67HrOA94MfDqfvx+ug8TzhqGw+gsp/sA2r0AVXUdsHBs1WiqTqiqs4CHAKrqu8Ae4y1pZhkOo7Olqu4bdxHaaY/0Xz1QAEnmA4+Ot6SZZTiMzvVJXgPMSbIoyQeAK8ZdlIZ2LvAZ4NAkK4DLgT8Yb0kzyzdBjUiSfYD/Dvws3adUvwi8s6oeGmthGlqSY4GT6B6/NVW1bswlzSjDQRqQ5JAdza+qe2aqlnEzHKZZks8y4bsrBlXVL8xgOZqiJLfSPX6D30mybbyq6kfGUtgYGA7TLMlP7Wh+VV02U7VIT4ThIG1HkoOBRcBe26ZV1f8ZX0Uza2Qf2Z7tkiwC3kX37duD/1yz5rB0V5bkDcCb6b7F7DrgROAfgX8zxrJmlJcyR+ejwIeBLcBPAx8H/mqsFWkq3gy8CLitqn4aeAGwebwlzSzDYXT2rqo1dF2326pqObPoVecp4KFtl52T7FlVXwdm1edk7FaMzkNJdgNu7r8y71vAoWOuScPbmOQg4G+AS5J8lwlfkvxU5wnJEUnyImAdcBDwTuBA4A+r6p/GWZemrr8CdSDwhf5HmmYFw0Hajv5qxQIGjrCr6prxVTSz7FZMs8k+lu2boHYNSd4JvB64hcc+cFXMovNGhsP0ezHdTwFeAFxJ+9e/9OT3y8AzZ1M3YiLDYfo9HfgZui8JeQ3wOeACf7djl3M93fmiu8Zcx9h4zmGEkuxJFxJ/BLyjqj4w5pI0pCSLgYvpQuLhbdNnU7fQI4cR6EPhFXTBsJDuuwEuGmdNmrKVwHuArzHLvuRlG48cplmSlcBzgL8DLqyq68dcknZCksuqaocfonuqMxymWZJHgQf70cGdu+0jvwfMfFWaqiTvo+tOrObx3YpZcynTcJAakny5MbmqatZcyjQcJDX5wSupIclhST6S5O/68eOSnDHuumaS4SC1fYzuS4GP6Me/AfzWuIoZB8NBaptXVavoL2NW1RZg63hLmlmGg9T2YJKn8diP2pwIzKofKfJNUFLb79Bdxnxmkq8A84FXjbekmeXVCmlAkmdU1e398Fy6b38KcFNVPTLW4maY3Qrp8f5mYPhTVXVDVV0/24IBDAdposGP2M/qbwo3HKTHq+0Mzzqec5AGJNlK99mYAHsD3982i1n22RjDQVKT3QpJTYaDpCbDQVKT4SCpyXCQ1PT/APAGYL5yIvOsAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data['Gender'].value_counts().plot(kind='bar',color=['purple','skyblue'],title=\"Graph For Different genders\",figsize=(4,5))\n",
+    "pd.crosstab(data['Gender'],data['Attrition']).plot(kind=\"bar\",color=['purple','skyblue'],figsize=(5,6))\n",
+    "plt.title(\"Staying/left the company vs Gender\")\n",
+    "plt.xlabel(\"Stay/Left\")\n",
+    "plt.ylabel(\"No of people who left the company based on gender\")\n",
+    "plt.legend([\"Left\",\"Stay\"])\n",
+    "plt.xticks(rotation=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "db9b66e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Research & Development    961\n",
+       "Sales                     446\n",
+       "Human Resources            63\n",
+       "Name: Department, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dep=data[\"Department\"].value_counts()\n",
+    "dep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "01f04007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1, 2]),\n",
+       " [Text(0, 0, 'Human Resources'),\n",
+       "  Text(1, 0, 'Research & Development'),\n",
+       "  Text(2, 0, 'Sales')])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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khqEoSQ1DUZIaE4dikiVJLkrywX783kk+muSK/nWnZt7jkqxMcnmSwyZdtyQNbYgtxZcBlzbjxwLnVtV+wLn9OEkOAI4EDgSeDPx1kiUDrF+SBjNRKCbZA3gq8HdN8+HAaf3wacARTfsZVXVbVV0JrAQOnmT9kjS0SbcU3wL8IfCjpm3XqroGoH/dpW/fHVjVzLe6b5Okjca8QzHJ04DrqurCDV1klrZax3sfk+SCJBesWbNmviVK0pxNsqX4aOCXk3wDOAN4QpJ3Atcm2Q2gf72un381sGez/B7A1bO9cVWdXFUrqmrFsmXLJihRkuZm3qFYVcdV1R5VtZzuBMq/V9VvAmcDR/WzHQWc1Q+fDRyZZKsk+wD7AefPu3JJWgAL0fP264EzkxwNXAU8E6CqLklyJvAV4A7gxVV15wKsX5LmbZBQrKrzgPP64e8Ah65jvpOAk4ZYpyQtBO9okaSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUmPeoZhkzyQfT3JpkkuSvKxvv3eSjya5on/dqVnmuCQrk1ye5LAhfgFJGtIkW4p3AK+oqgcChwAvTnIAcCxwblXtB5zbj9NPOxI4EHgy8NdJlkxSvCQNbd6hWFXXVNXn++GbgUuB3YHDgdP62U4DjuiHDwfOqKrbqupKYCVw8HzXL0kLYZBjikmWAw8DPgvsWlXXQBecwC79bLsDq5rFVvdts73fMUkuSHLBmjVrhihRkjbIxKGYZDvgvcDvVdVNdzfrLG0124xVdXJVraiqFcuWLZu0REnaYBOFYpJ70AXiu6rqfX3ztUl266fvBlzXt68G9mwW3wO4epL1S9LQJjn7HOAU4NKqelMz6WzgqH74KOCspv3IJFsl2QfYDzh/vuuXpIWwdIJlHw08B/hSkov7tj8CXg+cmeRo4CrgmQBVdUmSM4Gv0J25fnFV3TnB+iVpcPMOxar6T2Y/Tghw6DqWOQk4ab7rlKSF5h0tktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1DEVJahiKktQwFCWpYShKUsNQlKSGoShJDUNRkhqGoiQ1lo5dgKSFdWJOHLuEeTm+jh9lvW4pSlLDUJSkhqEoSQ1DUZIahqIkNQxFSWoYipLUMBQlqWEoSlLDUJSkhqEoSQ1DUZIahqIkNQxFSWoYipLUWPRQTPLkJJcnWZnk2MVevyTdnUUNxSRLgL8CngIcAPx6kgMWswZJujuLvaV4MLCyqr5eVT8EzgAOX+QaJGmdFjsUdwdWNeOr+zZJ2iikqhZvZckzgcOq6gX9+HOAg6vqJWvNdwxwTD+6P3D5ohU5nJ2B68cuYjPjZ774NuXPfO+qWrZ242I/uGo1sGczvgdw9dozVdXJwMmLVdRCSHJBVa0Yu47NiZ/54pvGz3yxd58/B+yXZJ8kWwJHAmcvcg2StE6LuqVYVXck+V/AOcAS4O+r6pLFrEGS7s6iP/e5qv4N+LfFXu8INund/02Un/nim7rPfFFPtEjSxs7b/CSpYShKUsNQHEiSP9+QNmlTluR+Sbbqhx+f5KVJdhy5rEEZisP5xVnanrLoVWzGkmyRZPux65hy7wXuTHJ/4BRgH+Dd45Y0LENxQkl+J8mXgP2TfLH5uRL44tj1Tbsk706yfZJtga8Alyf5g7HrmmI/qqo7gF8B3lJVvw/sNnJNg1r0S3Km0LuBDwF/BrRdod1cVd8dp6TNygFVdVOSZ9Nd6vVK4ELgDeOWNbVuT/LrwFHA0/u2e4xYz+DcUpxQVd1YVd+oql+nu43xdqCA7ZLsNW51m4V7JLkHcARwVlXNfP5aGM8DHgmcVFVXJtkHeOfINQ3K6xQH0t+pcwJwLfCjvrmq6qDRitoMJHkp3dbhF4CnAnsB76yqx45a2BRLsjWwV1Vtih21rJehOJAkK4Gfq6rvjF3L5i7J0v64lwaW5OnAG4Etq2qfJA8FXlNVvzxuZcNx93k4q4Abxy5ic5Nk1ySnJPlQP34A3fEuLYwT6DqLvgGgqi6mOwM9NTzRMpyvA+cl+VfgtpnGqnrTeCVtFk4F3gG8qh//KvBPdJeLaHh3VNWNSdq2qdrddEtxOFcBHwW2BO7V/Ghh7VxVZ9Ifx+13m+8ct6Sp9uUkvwEsSbJfkrcDnxq7qCG5pTiQqjoRIMm2VfX9sevZjHw/yX3ot1aSHIKHMRbSS+i2ym+juxztHOB1o1Y0ME+0DCTJI+l22barqr2SPAR4YVX97silTbUkDwfeDjwI+DKwDHhGVXnhvObFUBxIks8CzwDOrqqH9W1frqoHjVvZ9EuylO5ZPgEu769V1AJI8lHgmVV1Qz++E3BGVR02amEDcvd5QFW1aq0D0B7bWiBJfnUdkx6QhKp636IWtPnYeSYQAarqe0l2GbGewRmKw1mV5FFA9c+feSlw6cg1TbOn3820AgzFhfGjJHtV1VUASfZmys4+u/s8kCQ7A28Fnki3G/cR4GVezK1pkuQw4G+BT/RNjwOOqapzxqtqWIaiNnlJngocCNxzpq2qXjNeRdMpyRZ0x83/HTiE7j//T1fVpvrc51kZigPpb4x/CbCc5rDENN3+tDFK8jfANsAvAH9H96U9v6qOHrWwKZXkk1X1uLHrWEiG4kCSfIHukpwv8ZMOIaiqT6xzIU0syRer6qDmdTvgfVX1pLFrm0ZJ/hi4le6uoR9fjztN3eR5omU4P6iqt41dxGbo1v71liT3Bb7LlN2Lu5F5fv/64qatgH1HqGVBGIrDeWuS4+lOsLT3Pn9+vJI2Cx/snxHyF3Sdy0K3G60FUFVT/x+OoTicBwPPAZ5A059iP66BJXkEsKqqXtuPb0d36OIy4M1j1jbNkjx3tvaqOn2xa1koHlMcSJLLgIOq6odj17I5SPJ54IlV9d0kjwPOoDvR9VDggVX1jDHrm1Z9BxAz7gkcCnx+mj5vtxSH8wVgR+C6kevYXCxpDu7/GnByVb0XeG+Si8cra7pV1Uva8SQ7AP8wUjkLwlAczq7AZUk+x12PKXpJzsJY0vSwfShwTDPNv+vFcwuw39hFDMk/nuEcP3YBm5l/BD6R5Hq6M9D/AdA/j9iuwxZIkg/wk9v6lgAPBM4cr6LheUxxQEl+hq6r9gI+V1XfHrmkqdb3nbgb8JGZPiyTPICu+zbP+i+AJD/fjN4BfLOqVo9Vz0IwFAeS5AXAn9DdAhXg5+ke6PP3oxYmDSzJrsAj+tHzq2qqjqMbigNJcjnwqJkOIPreoD9VVfuPW5k0nCTPAt4AnEf3n/9jgT+oqveMWdeQPKY4nNXAzc34zXRP+JOmyauAR8xsHSZZBnwMMBT1U74FfDbJWXTHFA8Hzk/ycvCpfpoaW6y1u/wdpuwBeIbicL7W/8w4q3/1iX6aJh9Ocg7d2X/orhH9txHrGZzHFAeW5F5AVdV/j12LtBD6R0E8hu6Y4ier6v0jlzQoQ3EgSR5Ed2X/vfum64HnVtUl41UlDSvJtnQ9Qt2ZZH+6B4Z9aJoeFmYoDiTJp4BXVdXH+/HHA39aVY8asy5pSEkupDvjvBPwGeAC4JaqevaohQ1oqg6QjmzbmUAEqKrzgG3HK0daEKmqW4BfBd5eVb8CHDByTYMyFIfz9SR/nGR5//Nq4Mqxi5IGliSPBJ4N/GvfNlUnbA3F4TwfWEb3aM3398PPG7UiaXi/BxwHvL+qLkmyL/Dxu19k0+IxRUlzlmTbmfvNp81UbfaOYa1eQ36KXYdpmvS7zqcA2wF7JXkI8MKq+t1xKxuOoTi5N45dgLSI3gIcBpwNUFVf6Hs+nxqG4oTaR5gm2RrYq6ouH7EkaUFV1aokbdOdY9WyEDzRMpAkTwcuBj7cjz80ydmjFiUNb1WSRwGVZMsk/xu4dOyihmQoDucEug5mbwCoqouB5aNVIy2MF9E983l3up6hHgpMzfFEcPd5SHdU1Y1r7VZIU6Wqrqe7RhGAJDvRheJJoxU1MLcUh/PlJL9B90Cl/fpHQX5q7KKkISTZM8nJST6Y5Ogk2yR5I3A5sMvY9Q3JUBzOS4AD6Z7k9266hyf93pgFSQM6HbgaeDvwILr7nnene9b5y8YsbGhevD2QJA+rqovGrkNaCEm+UFUPacavpbvS4ra7WWyT5DHF4bwpyW7APwNn2GWYpk1//HDmoPm3gW36rsSoqu+OVtjA3FIcUP+I02fR9Ua8PfBPVfW6cauSJpfkG8CP+Ekotqqq9l3cihaOobgAkjwY+EPg16pqy7HrkbThPNEykCQPTHJCkkuAv6Q787zHyGVJmiO3FAeS5DN0D/P556q6eux6JM2PoTgg732WNn3uPg/Ee5+1uUiyJMl9k+w18zN2TUPykpzhnEB37/N50N37nGT5iPVIg0vyEuB44Fq6s9HQ9Sd60GhFDcxQHI73Pmtz8DJg/6r6ztiFLBRDcTh3ufcZeCne+6zps4ruFtap5YmWgSTZBngV8CS6C1zPAV5bVT8YtTBpQElOAfane5Lfj2/xq6o3jVbUwAxFSRssyfGztVfViYtdy0IxFAeQ5Cj6Yy1906XA26rq9PGqkjQfHlOcUJLn0nUR9nLg83S7zg8H3pAEg1HTJMkyultYDwTuOdNeVU8YraiBeZ3i5H4X+JWq+nhV3VhVN1TVvwP/kynrpl0C3gVcBuwDnAh8A/jcmAUNzVCc3PZV9Y21G/u27Re9Gmlh3aeqTgFur6pPVNXzgUPGLmpI7j5P7tZ5TpM2Rbf3r9ckeSpdb9xT1fGJJ1omlOQWYOVsk4B9q2rbRS5JWjBJngb8B7An3aMJtgdOrKqpuaXVUJxQkr3vbnpVfXOxapE0OUNR0gZLsg/dQ9qW0xx+q6pfHqumoXlMUdJc/AtwCvABftIhxFRxS1HSBkvy2ar6ubHrWEiG4gJKcp9p7k1Em5++05P9gI9w13ufPz9aUQNz93lgSb5Gd7P8O4FTgQNGLUga1oOB5wBP4K79KU7NHS2G4sCq6n5Jfh/4NPC8seuRBvYrdJea/XDsQhaKd7RMKMlH2stykhwCvAh4IfC00QqTFsYXgB3HLmIhuaU4uV1mrkXsr/B/A/D0qvpqkheOW5o0uF2By5J8jrseU/SSHP3YbX3XYXvS9bb9sKr6VpLtAe9m0bSZtT/FaeLZ5wkluT9wLPBD4GvAU4BPAocD76yqN49YnqQ5MhQHluRhwBOBi6rqY2PXIw0pyc10Z5sBtgTuAXy/qqamRyh3nwdWVRcBF41dh7QQqupe7XiSI+ge7Ts13FKUNJEkn6mqqelT0S1FSRssya82o1sAK/jJ7vRUMBQlzcXTm+E76B5HcPg4pSwMd58HkuTRwAnA3nT/2QSoqtp3zLokzY2hOJAklwG/D1wI3DnTbocQmgZJ3s7d7CZX1UsXsZwF5e7zcG6sqg+NXYS0QC5ohk9kii/idktxQkke3g8+C1gCvI8p7VJJAkhyUVU9bOw6FopbipP7P2uNr2iGp6pLJak31VtShuKEquoXxq5B0nDsOmwgSf40yY7N+E5JXjdiSdJgktyc5KYkNwEHzQzPtI9d35A8pjiQ2Y6zJPl8VT18XctI2vi4pTicJUm2mhlJsjWw1d3ML2kj5DHF4bwTODfJO+gORD8fOG3ckiTNlbvPA0gSYA/gQLpuwwJ8pKrOGbUwSXNmKA4kyYVV9bNj1yFpMh5THM5nkjxi7CIkTcYtxYEk+QrwAOCbwPf5SYcQB41amKQ5MRQH0j7mtDXzpD9JmwbPPg+keczpLsA9Ry5H0jx5THEgSX45yRXAlcAn6DrftNccaRNjKA7ntcAhwFerah/gUOC/xi1J0lwZisO5ve9QdoskW1TVx4GHjlyTpDnymOJwbkiyHfAfwLuSXEf3DAtJmxDPPg8kybbArXRb388GdgDe5eMIpE2LoTig/rKc/arqY0m2AZZU1c1j1yVpw3lMcSBJfht4D/D/+qbdgX8ZrSBJ82IoDufFwKOBmwCq6gpgl1ErkjRnhuJwbquqH86MJFnKlD/LQppGhuJwPpHkj4Ctk/wi8M/AB0auSdIceaJlIEm2AI4GnkTXGcQ5wN+VH7C0STEUF0CSewN7VNUXx65F0ty4+zyQJOcl2b4PxIuBdyR508hlSZojQ3E4O1TVTcCvAu/oe+F+4sg1SZojQ3E4S5PsBjwL+ODYxUiaH0NxOK+hO7nytar6XJJ9gStGrknSHHmiRZIabikOJMkDkpyb5Mv9+EFJXj12XZLmxlAczt8CxwG3A/SX4xw5akWS5sxQHM42VXX+Wm32pyhtYgzF4Vyf5H709zsneQZwzbglSZorT7QMpD/bfDLwKOB7dA+weraPOJU2LYbiwPoeuLeg64X716rqXSOXJGkO3H2eUH9r33FJ/rLvHecW4ChgJd2F3JI2IW4pTijJWXS7y5+me6zpTsCWwMuq6uIRS5M0D4bihJJ8qaoe3A8vAa4H9vLZLNKmyd3nyd0+M1BVdwJXGojSpsstxQkluRP4/swosDXdccUAVVXbj1WbpLkzFCWp4e6zJDUMRUlqGIqS1DAUJalhKEpSw1CUpMb/B3xV4CYucZVLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 360x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data['Department'].value_counts().plot(kind='bar',color=['purple','skyblue'],title=\"Departments in the company\",figsize=(5,6))\n",
+    "pd.crosstab(data['Department'],data['Attrition']).plot(kind=\"bar\",figsize=(5,6),color=['purple','skyblue'])\n",
+    "plt.title(\"Staying/left the company vs Department\")\n",
+    "plt.xlabel(\"Stay/Left\")\n",
+    "plt.ylabel(\"No of people who left the company based on department\")\n",
+    "plt.legend([\"Left\",\"Stay\"])\n",
+    "plt.xticks(rotation=360)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c882c442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Age group of the company'}>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data['Age'].value_counts().plot(kind='bar',color=['purple','skyblue'],title=\"Age group of the company\",figsize=(12,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f0bd561",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "        34, 35, 36, 37, 38, 39, 40, 41, 42]),\n",
+       " [Text(0, 0, '18'),\n",
+       "  Text(1, 0, '19'),\n",
+       "  Text(2, 0, '20'),\n",
+       "  Text(3, 0, '21'),\n",
+       "  Text(4, 0, '22'),\n",
+       "  Text(5, 0, '23'),\n",
+       "  Text(6, 0, '24'),\n",
+       "  Text(7, 0, '25'),\n",
+       "  Text(8, 0, '26'),\n",
+       "  Text(9, 0, '27'),\n",
+       "  Text(10, 0, '28'),\n",
+       "  Text(11, 0, '29'),\n",
+       "  Text(12, 0, '30'),\n",
+       "  Text(13, 0, '31'),\n",
+       "  Text(14, 0, '32'),\n",
+       "  Text(15, 0, '33'),\n",
+       "  Text(16, 0, '34'),\n",
+       "  Text(17, 0, '35'),\n",
+       "  Text(18, 0, '36'),\n",
+       "  Text(19, 0, '37'),\n",
+       "  Text(20, 0, '38'),\n",
+       "  Text(21, 0, '39'),\n",
+       "  Text(22, 0, '40'),\n",
+       "  Text(23, 0, '41'),\n",
+       "  Text(24, 0, '42'),\n",
+       "  Text(25, 0, '43'),\n",
+       "  Text(26, 0, '44'),\n",
+       "  Text(27, 0, '45'),\n",
+       "  Text(28, 0, '46'),\n",
+       "  Text(29, 0, '47'),\n",
+       "  Text(30, 0, '48'),\n",
+       "  Text(31, 0, '49'),\n",
+       "  Text(32, 0, '50'),\n",
+       "  Text(33, 0, '51'),\n",
+       "  Text(34, 0, '52'),\n",
+       "  Text(35, 0, '53'),\n",
+       "  Text(36, 0, '54'),\n",
+       "  Text(37, 0, '55'),\n",
+       "  Text(38, 0, '56'),\n",
+       "  Text(39, 0, '57'),\n",
+       "  Text(40, 0, '58'),\n",
+       "  Text(41, 0, '59'),\n",
+       "  Text(42, 0, '60')])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.crosstab(data['Age'],data['Attrition']).plot(kind=\"bar\",figsize=(15,6))\n",
+    "plt.title(\"Staying/left the company vs Age\")\n",
+    "plt.xlabel(\"Stay/Left\")\n",
+    "plt.ylabel(\"No of people who left the company based on age\")\n",
+    "plt.legend([\"Left\",\"Stay\"])\n",
+    "plt.xticks(rotation=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "64a4c032",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def onehot_encode(df, column):\n",
+    "    df = df.copy()\n",
+    "    dummies = pd.get_dummies(df[column], prefix=column)\n",
+    "    df = pd.concat([df, dummies], axis=1)\n",
+    "    df = df.drop(column, axis=1)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d63fca5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_inputs(df):\n",
+    "    df = df.copy()\n",
+    "    \n",
+    "    # Drop single-value columns and id columns\n",
+    "    df = df.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis=1)\n",
+    "    \n",
+    "    # Binary-encode binary columns\n",
+    "    df['Gender'] = df['Gender'].replace({'Female': 0, 'Male': 1})\n",
+    "    df['OverTime'] = df['OverTime'].replace({'No': 0, 'Yes': 1})\n",
+    "    df['Attrition'] = df['Attrition'].replace({'No': 0, 'Yes': 1})\n",
+    "    \n",
+    "    # Ordinal-encode the BusinessTravel column\n",
+    "    df['BusinessTravel'] = df['BusinessTravel'].replace({'Non-Travel': 0, 'Travel_Rarely': 1, 'Travel_Frequently': 2})\n",
+    "    \n",
+    "    # One-hot encoding\n",
+    "    for column in ['Department', 'EducationField', 'JobRole', 'MaritalStatus']:\n",
+    "        df = onehot_encode(df, column=column)\n",
+    "    \n",
+    "    # Split df into X and y\n",
+    "    y = df['Attrition']\n",
+    "    X = df.drop('Attrition', axis=1)\n",
+    "    \n",
+    "    # Train-test split\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=1)\n",
+    "    \n",
+    "    # Scale X\n",
+    "    scaler = StandardScaler()\n",
+    "    scaler.fit(X_train)\n",
+    "    X_train = pd.DataFrame(scaler.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
+    "    X_test = pd.DataFrame(scaler.transform(X_test), index=X_test.index, columns=X_test.columns)\n",
+    "    \n",
+    "    return X_train, X_test, y_train, y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f0fd71e8",
+   "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>BusinessTravel</th>\n",
+       "      <th>DailyRate</th>\n",
+       "      <th>DistanceFromHome</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>EnvironmentSatisfaction</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>HourlyRate</th>\n",
+       "      <th>JobInvolvement</th>\n",
+       "      <th>JobLevel</th>\n",
+       "      <th>...</th>\n",
+       "      <th>JobRole_Laboratory Technician</th>\n",
+       "      <th>JobRole_Manager</th>\n",
+       "      <th>JobRole_Manufacturing Director</th>\n",
+       "      <th>JobRole_Research Director</th>\n",
+       "      <th>JobRole_Research Scientist</th>\n",
+       "      <th>JobRole_Sales Executive</th>\n",
+       "      <th>JobRole_Sales Representative</th>\n",
+       "      <th>MaritalStatus_Divorced</th>\n",
+       "      <th>MaritalStatus_Married</th>\n",
+       "      <th>MaritalStatus_Single</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0.762337</td>\n",
+       "      <td>-2.005534</td>\n",
+       "      <td>-0.816540</td>\n",
+       "      <td>1.742140</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>-0.669480</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>0.055811</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.128910</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>785</th>\n",
+       "      <td>0.326292</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>1.652087</td>\n",
+       "      <td>1.366162</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>-1.586784</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>-0.239963</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>0.838814</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>918</th>\n",
+       "      <td>1.525416</td>\n",
+       "      <td>1.722911</td>\n",
+       "      <td>-1.436773</td>\n",
+       "      <td>-0.012423</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>0.844541</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>2.642657</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>1.884966</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1335</th>\n",
+       "      <td>0.217281</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>0.035051</td>\n",
+       "      <td>1.240836</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>-1.225876</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>2.017168</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>1.884966</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>-0.109753</td>\n",
+       "      <td>-2.005534</td>\n",
+       "      <td>0.180264</td>\n",
+       "      <td>-1.015031</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>-1.620241</td>\n",
+       "      <td>-0.998124</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>2.982320</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>715</th>\n",
+       "      <td>-0.436787</td>\n",
+       "      <td>1.722911</td>\n",
+       "      <td>0.015361</td>\n",
+       "      <td>-1.015031</td>\n",
+       "      <td>1.078220</td>\n",
+       "      <td>0.247824</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>0.893837</td>\n",
+       "      <td>1.766532</td>\n",
+       "      <td>-0.063108</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>905</th>\n",
+       "      <td>-0.872832</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>-0.311985</td>\n",
+       "      <td>-1.015031</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>1.041724</td>\n",
+       "      <td>-0.998124</td>\n",
+       "      <td>1.740736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>1.884966</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1096</th>\n",
+       "      <td>0.326292</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>0.190109</td>\n",
+       "      <td>-0.388401</td>\n",
+       "      <td>-0.892452</td>\n",
+       "      <td>0.247824</td>\n",
+       "      <td>0.843888</td>\n",
+       "      <td>-1.373763</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>1.740736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>-0.918742</td>\n",
+       "      <td>1.448930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>0.653326</td>\n",
+       "      <td>-0.141311</td>\n",
+       "      <td>0.524838</td>\n",
+       "      <td>0.864859</td>\n",
+       "      <td>0.092884</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>0.696654</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>1.740736</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>3.915780</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>-0.237630</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1061</th>\n",
+       "      <td>-1.417888</td>\n",
+       "      <td>-2.005534</td>\n",
+       "      <td>0.022744</td>\n",
+       "      <td>0.488881</td>\n",
+       "      <td>-0.892452</td>\n",
+       "      <td>1.165128</td>\n",
+       "      <td>-1.184992</td>\n",
+       "      <td>0.598063</td>\n",
+       "      <td>0.384204</td>\n",
+       "      <td>-0.965030</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.469724</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.335309</td>\n",
+       "      <td>-0.255377</td>\n",
+       "      <td>-0.495745</td>\n",
+       "      <td>-0.533521</td>\n",
+       "      <td>4.208217</td>\n",
+       "      <td>-0.530514</td>\n",
+       "      <td>1.088445</td>\n",
+       "      <td>-0.690165</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1029 rows × 47 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Age  BusinessTravel  DailyRate  DistanceFromHome  Education  \\\n",
+       "99    0.762337       -2.005534  -0.816540          1.742140   0.092884   \n",
+       "785   0.326292       -0.141311   1.652087          1.366162   1.078220   \n",
+       "918   1.525416        1.722911  -1.436773         -0.012423   0.092884   \n",
+       "1335  0.217281       -0.141311   0.035051          1.240836   1.078220   \n",
+       "1182 -0.109753       -2.005534   0.180264         -1.015031   1.078220   \n",
+       "...        ...             ...        ...               ...        ...   \n",
+       "715  -0.436787        1.722911   0.015361         -1.015031   1.078220   \n",
+       "905  -0.872832       -0.141311  -0.311985         -1.015031   0.092884   \n",
+       "1096  0.326292       -0.141311   0.190109         -0.388401  -0.892452   \n",
+       "235   0.653326       -0.141311   0.524838          0.864859   0.092884   \n",
+       "1061 -1.417888       -2.005534   0.022744          0.488881  -0.892452   \n",
+       "\n",
+       "      EnvironmentSatisfaction    Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
+       "99                  -0.669480  0.843888    0.055811        0.384204 -0.063108   \n",
+       "785                 -1.586784  0.843888   -0.239963        0.384204  0.838814   \n",
+       "918                  1.165128  0.843888    0.844541        0.384204  2.642657   \n",
+       "1335                 1.165128  0.843888   -1.225876        0.384204 -0.063108   \n",
+       "1182                 1.165128 -1.184992   -1.620241       -0.998124 -0.063108   \n",
+       "...                       ...       ...         ...             ...       ...   \n",
+       "715                  0.247824 -1.184992    0.893837        1.766532 -0.063108   \n",
+       "905                  1.165128 -1.184992    1.041724       -0.998124  1.740736   \n",
+       "1096                 0.247824  0.843888   -1.373763        0.384204  1.740736   \n",
+       "235                  1.165128 -1.184992    0.696654        0.384204  1.740736   \n",
+       "1061                 1.165128 -1.184992    0.598063        0.384204 -0.965030   \n",
+       "\n",
+       "      ...  JobRole_Laboratory Technician  JobRole_Manager  \\\n",
+       "99    ...                       2.128910        -0.255377   \n",
+       "785   ...                      -0.469724        -0.255377   \n",
+       "918   ...                      -0.469724         3.915780   \n",
+       "1335  ...                      -0.469724        -0.255377   \n",
+       "1182  ...                      -0.469724        -0.255377   \n",
+       "...   ...                            ...              ...   \n",
+       "715   ...                      -0.469724        -0.255377   \n",
+       "905   ...                      -0.469724        -0.255377   \n",
+       "1096  ...                      -0.469724         3.915780   \n",
+       "235   ...                      -0.469724         3.915780   \n",
+       "1061  ...                      -0.469724        -0.255377   \n",
+       "\n",
+       "      JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
+       "99                         -0.335309                  -0.255377   \n",
+       "785                        -0.335309                  -0.255377   \n",
+       "918                        -0.335309                  -0.255377   \n",
+       "1335                       -0.335309                  -0.255377   \n",
+       "1182                        2.982320                  -0.255377   \n",
+       "...                              ...                        ...   \n",
+       "715                        -0.335309                  -0.255377   \n",
+       "905                        -0.335309                   3.915780   \n",
+       "1096                       -0.335309                  -0.255377   \n",
+       "235                        -0.335309                  -0.255377   \n",
+       "1061                       -0.335309                  -0.255377   \n",
+       "\n",
+       "      JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
+       "99                     -0.495745                -0.533521   \n",
+       "785                    -0.495745                -0.533521   \n",
+       "918                    -0.495745                -0.533521   \n",
+       "1335                    2.017168                -0.533521   \n",
+       "1182                   -0.495745                -0.533521   \n",
+       "...                          ...                      ...   \n",
+       "715                    -0.495745                -0.533521   \n",
+       "905                    -0.495745                -0.533521   \n",
+       "1096                   -0.495745                -0.533521   \n",
+       "235                    -0.495745                -0.533521   \n",
+       "1061                   -0.495745                -0.533521   \n",
+       "\n",
+       "      JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
+       "99                       -0.237630               -0.530514   \n",
+       "785                      -0.237630               -0.530514   \n",
+       "918                      -0.237630                1.884966   \n",
+       "1335                     -0.237630                1.884966   \n",
+       "1182                     -0.237630               -0.530514   \n",
+       "...                            ...                     ...   \n",
+       "715                      -0.237630               -0.530514   \n",
+       "905                      -0.237630                1.884966   \n",
+       "1096                     -0.237630               -0.530514   \n",
+       "235                      -0.237630               -0.530514   \n",
+       "1061                      4.208217               -0.530514   \n",
+       "\n",
+       "      MaritalStatus_Married  MaritalStatus_Single  \n",
+       "99                 1.088445             -0.690165  \n",
+       "785                1.088445             -0.690165  \n",
+       "918               -0.918742             -0.690165  \n",
+       "1335              -0.918742             -0.690165  \n",
+       "1182               1.088445             -0.690165  \n",
+       "...                     ...                   ...  \n",
+       "715                1.088445             -0.690165  \n",
+       "905               -0.918742             -0.690165  \n",
+       "1096              -0.918742              1.448930  \n",
+       "235                1.088445             -0.690165  \n",
+       "1061               1.088445             -0.690165  \n",
+       "\n",
+       "[1029 rows x 47 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Training and testing data\n",
+    "X_train, X_test, y_train, y_test = preprocess_inputs(data)\n",
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "dfd1083f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "99      0\n",
+       "785     0\n",
+       "918     0\n",
+       "1335    0\n",
+       "1182    0\n",
+       "       ..\n",
+       "715     0\n",
+       "905     0\n",
+       "1096    0\n",
+       "235     0\n",
+       "1061    0\n",
+       "Name: Attrition, Length: 1029, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6ff446f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   Logistic Regression trained.\n",
+      "                   K-Nearest Neighbors trained.\n",
+      "                         Decision Tree trained.\n",
+      "Support Vector Machine (Linear Kernel) trained.\n",
+      "   Support Vector Machine (RBF Kernel) trained.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Inno\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:985: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "  warnings.warn(\"Liblinear failed to converge, increase \"\n",
+      "C:\\Users\\Inno\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                        Neural Network trained.\n",
+      "                         Random Forest trained.\n",
+      "                     Gradient Boosting trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "models = {\n",
+    "    \"                   Logistic Regression\": LogisticRegression(),\n",
+    "    \"                   K-Nearest Neighbors\": KNeighborsClassifier(),\n",
+    "    \"                         Decision Tree\": DecisionTreeClassifier(),\n",
+    "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
+    "    \"   Support Vector Machine (RBF Kernel)\": SVC(),\n",
+    "    \"                        Neural Network\": MLPClassifier(),\n",
+    "    \"                         Random Forest\": RandomForestClassifier(),\n",
+    "    \"                     Gradient Boosting\": GradientBoostingClassifier()\n",
+    "}\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    model.fit(X_train, y_train)\n",
+    "    print(name + \" trained.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b7290d23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   Logistic Regression: 86.62%\n",
+      "                   K-Nearest Neighbors: 82.09%\n",
+      "                         Decision Tree: 78.00%\n",
+      "Support Vector Machine (Linear Kernel): 85.49%\n",
+      "   Support Vector Machine (RBF Kernel): 84.81%\n",
+      "                        Neural Network: 84.35%\n",
+      "                         Random Forest: 83.90%\n",
+      "                     Gradient Boosting: 85.49%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Choosing model\n",
+    "for name, model in models.items():\n",
+    "    print(name + \": {:.2f}%\".format(model.score(X_test, y_test) * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "410c0f55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression()\n",
+      "[[352  12]\n",
+      " [ 47  30]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.88      0.97      0.92       364\n",
+      "           1       0.71      0.39      0.50        77\n",
+      "\n",
+      "    accuracy                           0.87       441\n",
+      "   macro avg       0.80      0.68      0.71       441\n",
+      "weighted avg       0.85      0.87      0.85       441\n",
+      "\n",
+      "Accuracy: 0.866\n"
+     ]
+    },
+    {
+     "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": [
+    "#Confusion matrix of Logistic Regression model\n",
+    "logmodel = LogisticRegression()\n",
+    "print(logmodel.fit(X_train, y_train) )\n",
+    "\n",
+    "y_pred1 = logmodel.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix = confusion_matrix(y_test, y_pred1)\n",
+    "print(confusion_matrix)\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,y_pred1))\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred1))\n",
+    "\n",
+    "# Print the confusion matrix using Matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.matshow(confusion_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
+    "for i in range(confusion_matrix.shape[0]):\n",
+    "    for j in range(confusion_matrix.shape[1]):\n",
+    "        ax.text(x=j, y=i,s=confusion_matrix[i, j], va='center', ha='center', size='xx-large')\n",
+    " \n",
+    "plt.xlabel('Predictions', fontsize=18)\n",
+    "plt.ylabel('Actuals', fontsize=18)\n",
+    "plt.title('Confusion Matrix', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "95a764f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[352  12]\n",
+      " [ 48  29]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.88      0.97      0.92       364\n",
+      "           1       0.71      0.38      0.49        77\n",
+      "\n",
+      "    accuracy                           0.86       441\n",
+      "   macro avg       0.79      0.67      0.71       441\n",
+      "weighted avg       0.85      0.86      0.85       441\n",
+      "\n",
+      "Accuracy: 0.864\n"
+     ]
+    },
+    {
+     "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": [
+    "#Confusion matrix of Support Vector Machine(Linear Kernel) model\n",
+    "from sklearn.svm import SVC\n",
+    "Classifier = SVC (kernel='linear', random_state=0)\n",
+    "Classifier.fit (X_train, y_train)\n",
+    "\n",
+    "y_pred2 = Classifier.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix = confusion_matrix(y_test, y_pred2)\n",
+    "print(confusion_matrix)\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,y_pred2))\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred2))\n",
+    "\n",
+    "# Print the confusion matrix using Matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.matshow(confusion_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
+    "for i in range(confusion_matrix.shape[0]):\n",
+    "    for j in range(confusion_matrix.shape[1]):\n",
+    "        ax.text(x=j, y=i,s=confusion_matrix[i, j], va='center', ha='center', size='xx-large')\n",
+    " \n",
+    "plt.xlabel('Predictions', fontsize=18)\n",
+    "plt.ylabel('Actuals', fontsize=18)\n",
+    "plt.title('Confusion Matrix', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "bf091ab6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[362   2]\n",
+      " [ 65  12]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.85      0.99      0.92       364\n",
+      "           1       0.86      0.16      0.26        77\n",
+      "\n",
+      "    accuracy                           0.85       441\n",
+      "   macro avg       0.85      0.58      0.59       441\n",
+      "weighted avg       0.85      0.85      0.80       441\n",
+      "\n",
+      "Accuracy: 0.848\n"
+     ]
+    },
+    {
+     "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": [
+    "#Confusion matrix of Support Vector Machine(RBF Kernel) model\n",
+    "from sklearn.svm import SVC\n",
+    "Classifier = SVC (kernel='rbf', random_state=0)\n",
+    "Classifier.fit (X_train, y_train)\n",
+    "\n",
+    "y_pred3 = Classifier.predict(X_test)\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix = confusion_matrix(y_test, y_pred3)\n",
+    "print(confusion_matrix)\n",
+    "\n",
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,y_pred3))\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred3))\n",
+    "\n",
+    "# Print the confusion matrix using Matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(5, 5))\n",
+    "ax.matshow(confusion_matrix, cmap=plt.cm.Oranges, alpha=0.3)\n",
+    "for i in range(confusion_matrix.shape[0]):\n",
+    "    for j in range(confusion_matrix.shape[1]):\n",
+    "        ax.text(x=j, y=i,s=confusion_matrix[i, j], va='center', ha='center', size='xx-large')\n",
+    " \n",
+    "plt.xlabel('Predictions', fontsize=18)\n",
+    "plt.ylabel('Actuals', fontsize=18)\n",
+    "plt.title('Confusion Matrix', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f6093b66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_tp_tn_fn_fp(y_test, y_pred):\n",
+    "    tp=sum((y_test == 1) & (y_pred == 1))\n",
+    "    tn=sum((y_test == 0) & (y_pred == 0))\n",
+    "    fn=sum((y_test == 1) & (y_pred == 0))\n",
+    "    fp =sum((y_test == 0) & (y_pred == 1))\n",
+    "    return tp, tn, fp, fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "31fb38e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP for Logistic Regression : 30\n",
+      "TN for Logistic Regression : 352\n",
+      "FP for Logistic Regression : 12\n",
+      "FN for Logistic Regression : 47\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating TP(Total Positive Value),TN(Total Negative Value),FP(False Positive Value),FN(False Negative Value)\n",
+    "#for Logistic Regression model\n",
+    "\n",
+    "tp_lr, tn_lr, fp_lr, fn_lr = compute_tp_tn_fn_fp(y_test, y_pred1)\n",
+    "print('TP for Logistic Regression :', tp_lr)\n",
+    "print('TN for Logistic Regression :', tn_lr)\n",
+    "print('FP for Logistic Regression :', fp_lr)\n",
+    "print('FN for Logistic Regression :', fn_lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1bbf132f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP for Support Vector Machine(Linear Kernel) model : 29\n",
+      "TN for Support Vector Machine(Linear Kernel) model : 352\n",
+      "FP for Support Vector Machine(Linear Kernel) model : 12\n",
+      "FN for Support Vector Machine(Linear Kernel) model : 48\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating TP(Total Positive Value),TN(Total Negative Value),FP(False Positive Value),FN(False Negative Value)\n",
+    "#for Support Vector Machine(Linear Kernel) model\n",
+    "\n",
+    "tp_svml, tn_svml, fp_svml, fn_svml = compute_tp_tn_fn_fp(y_test, y_pred2)\n",
+    "print('TP for Support Vector Machine(Linear Kernel) model :', tp_svml)\n",
+    "print('TN for Support Vector Machine(Linear Kernel) model :', tn_svml)\n",
+    "print('FP for Support Vector Machine(Linear Kernel) model :', fp_svml)\n",
+    "print('FN for Support Vector Machine(Linear Kernel) model :', fn_svml)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "08399b6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP for Support Vector Machine(RBF Kernel) model : 12\n",
+      "TN for Support Vector Machine(RBF Kernel) model : 362\n",
+      "FP for Support Vector Machine(RBF Kernel) model : 2\n",
+      "FN for Support Vector Machine(RBF Kernel) model : 65\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating TP(Total Positive Value),TN(Total Negative Value),FP(False Positive Value),FN(False Negative Value)\n",
+    "#for Support Vector Machine(RBF Kernel) model\n",
+    "\n",
+    "tp_svmr, tn_svmr, fp_svmr, fn_svmr = compute_tp_tn_fn_fp(y_test, y_pred3)\n",
+    "print('TP for Support Vector Machine(RBF Kernel) model :', tp_svmr)\n",
+    "print('TN for Support Vector Machine(RBF Kernel) model :', tn_svmr)\n",
+    "print('FP for Support Vector Machine(RBF Kernel) model :', fp_svmr)\n",
+    "print('FN for Support Vector Machine(RBF Kernel) model :', fn_svmr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ae93f5b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for Logistic Regression : 86.62131519274377\n",
+      "Accuracy for Support Vector Machine(Linear Kernel) model : 86.39455782312925\n",
+      "Accuracy for Support Vector Machine(RBF Kernel) model : 84.80725623582767\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating Accuracy\n",
+    "\n",
+    "print('Accuracy for Logistic Regression :', 100*accuracy_score(y_test, y_pred1))\n",
+    "print('Accuracy for Support Vector Machine(Linear Kernel) model :', 100*accuracy_score(y_test, y_pred2))\n",
+    "print('Accuracy for Support Vector Machine(RBF Kernel) model :', 100*accuracy_score(y_test, y_pred3))   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7d8678e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a_lr=math.floor(accuracy_score(y_test, y_pred1)*100)\n",
+    "a_svml=math.floor(accuracy_score(y_test, y_pred2)*100)\n",
+    "a_svmr=math.floor(accuracy_score(y_test, y_pred3)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f36ed05b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision for Logistic Regression : 71.42857142857143\n",
+      "Precision for Support Vector Machine(Linear Kernel) model : 70.73170731707317\n",
+      "Precision for Support Vector Machine(RBF Kernel) model : 85.71428571428571\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating Precision\n",
+    "\n",
+    "print('Precision for Logistic Regression :', 100* precision_score(y_test, y_pred1))\n",
+    "print('Precision for Support Vector Machine(Linear Kernel) model :', 100* precision_score(y_test, y_pred2))\n",
+    "print('Precision for Support Vector Machine(RBF Kernel) model :', 100*precision_score(y_test, y_pred3))   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "492c78ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recall for Logistic Regression : 38.961038961038966\n",
+      "Recall for Support Vector Machine(Linear Kernel) model : 37.66233766233766\n",
+      "Recall for Support Vector Machine(RBF Kernel) model : 15.584415584415584\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating Recall\n",
+    "\n",
+    "print('Recall for Logistic Regression :', 100* recall_score(y_test, y_pred1))\n",
+    "print('Recall for Support Vector Machine(Linear Kernel) model :', 100* recall_score(y_test, y_pred2))\n",
+    "print('Recall for Support Vector Machine(RBF Kernel) model :', 100* recall_score(y_test, y_pred3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "4c1a33a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 score for Logistic Regression : 0.5042016806722689\n",
+      "F1 score for Support Vector Machine(Linear Kernel) model : 0.4915254237288135\n",
+      "F1 score for Support Vector Machine(RBF Kernel) model : 0.26373626373626374\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating F1_score\n",
+    "\n",
+    "print('F1 score for Logistic Regression :', f1_score(y_test, y_pred1))\n",
+    "print('F1 score for Support Vector Machine(Linear Kernel) model :', f1_score(y_test, y_pred2))\n",
+    "print('F1 score for Support Vector Machine(RBF Kernel) model :', f1_score(y_test, y_pred3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "24988b03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f1_lr=math.floor(f1_score(y_test, y_pred1)*100)\n",
+    "f1_svml=math.floor(f1_score(y_test, y_pred2)*100)\n",
+    "f1_svmr=math.floor(f1_score(y_test, y_pred3)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "1d5abd22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Models Vs Accuracy')"
+      ]
+     },
+     "execution_count": 50,
+     "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": [
+    "#Graph between model and accuracy\n",
+    "\n",
+    "x=['Logistic Regression', 'SVM Linear Kernel model','SVM RBF Kernel model']\n",
+    "y=[a_lr, a_svml, a_svmr]\n",
+    "plt.plot(x, y)\n",
+    "plt.xlabel('Models')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.title('Models Vs Accuracy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "18c1bf84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Models Vs F1_Score')"
+      ]
+     },
+     "execution_count": 47,
+     "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": [
+    "#Graph between model and F1_scores\n",
+    "\n",
+    "x=['Logistic Regression', 'SVM Linear Kernel model','SVM RBF Kernel model']\n",
+    "y=[f1_lr, f1_svml, f1_svmr]\n",
+    "plt.plot(x, y)\n",
+    "plt.xlabel('Models')\n",
+    "plt.ylabel('F1_scores')\n",
+    "plt.title('Models Vs F1_Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "83628b9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Age': -0.3260826939081251,\n",
+       " 'Attrition': 0.4611595989277241,\n",
+       " 'BusinessTravel': -0.16221752037720052,\n",
+       " 'DailyRate': 0.43704625417327464,\n",
+       " 'Department': 0.09031489074274253,\n",
+       " 'DistanceFromHome': -0.46444194783905574,\n",
+       " 'Education': 0.1693996226081028,\n",
+       " 'EducationField': 0.08312280236173179,\n",
+       " 'EmployeeCount': -0.31992301434626247,\n",
+       " 'EmployeeNumber': 0.1268657664844335,\n",
+       " 'EnvironmentSatisfaction': -0.283339598349285,\n",
+       " 'Gender': -0.059268098375331216,\n",
+       " 'HourlyRate': 0.07458330191319462,\n",
+       " 'JobInvolvement': 0.3389410390224457,\n",
+       " 'JobLevel': 0.8319700671775443,\n",
+       " 'JobRole': -0.08723729937980407,\n",
+       " 'JobSatisfaction': -0.05379654574453324,\n",
+       " 'MaritalStatus': -0.1733332977645172,\n",
+       " 'MonthlyIncome': -0.18723631073972546,\n",
+       " 'MonthlyRate': -0.3250606149642336,\n",
+       " 'NumCompaniesWorked': -0.22862795791419363,\n",
+       " 'Over18': -0.3397720708785684,\n",
+       " 'OverTime': 0.4701244109341639,\n",
+       " 'PercentSalaryHike': -0.4432969594766933,\n",
+       " 'PerformanceRating': 0.536328227110093,\n",
+       " 'RelationshipSatisfaction': -0.5423546391120876,\n",
+       " 'StandardHours': -0.3576498209242578,\n",
+       " 'StockOptionLevel': 0.06182657968233347,\n",
+       " 'TotalWorkingYears': 0.09253293837114485,\n",
+       " 'TrainingTimesLastYear': 0.15966620084268723,\n",
+       " 'WorkLifeBalance': -0.10423383110560046,\n",
+       " 'YearsAtCompany': 0.12370454943996634,\n",
+       " 'YearsInCurrentRole': -0.11911005038701562,\n",
+       " 'YearsSinceLastPromotion': -0.1194460166400527,\n",
+       " 'YearsWithCurrManager': 0.26542095622421474}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Example to show the accuracy of predictions of employee attrition\n",
+    "\n",
+    "characteristic_dict=dict(zip(data.columns,list(logmodel.coef_[0])))\n",
+    "characteristic_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "bcd924dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'characteristic Importance'}>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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SBKNHCPwgCOYKYrIwfEKlEwRBMCCEwA+CIBgQQuAHQRAMCCHwgyAIBoQQ+EEQBANCXwS+pC0k3SHpLkn7VeyXpJ+k/TdJWq8f5w2CIAjqM2yBL2ks8DNgS2BtYCdJa5eabQmsnl67AT8f7nmDIAiC3ujHDH9D4C4zu9vMXgJOAbYptdkG+KU51wKLS1quD+cOgiAIaiIzG14H0geBLczsk+n9R4E3mdmeWZtzgUPN7Mr0/hLgK2Z2Q0V/u+GrACZMmLD+fffd19eAi9EI3pjd5+z3+epU85lTg2Lqjmuo7ebUvtq1q8No9DWnVoyaG6+FpMlmNqlqXz9m+KrYVn6K1GnjG82OMbNJZjZp/Pjxwx5cEARB4PRD4D8IrJS9XxF4aAhtgiAIghGkHwL/emB1SatImg/YETin1OYc4GPJW+fNwLNm9nAfzh0EQRDUZNjJ08xspqQ9gQuBscAJZnaLpN3T/qOB84CtgLuAF4Bdh3veIAiCoDf6ki3TzM7DhXq+7ejsfwM+249zBUEQBEMjIm2DIAgGhBD4QRAEA0IUQAkC5hy/7yAYSULgB30hBGYQzPmESicIgmBACIEfBEEwIITAD4IgGBBChx+8ognbQhA0iBl+EATBgBAz/CAIgja80laIMcMPgiAYEGKGHwRBMBuou1oYyVVFzPCDIAgGhBD4QRAEA0II/CAIggEhBH4QBMGAEAI/CIJgQAiBHwRBMCAMnFvmKy2QIgiCoC4DJ/CDYDjEhCGYmxmWSkfSkpL+LOnO9HeJijYrSbpM0m2SbpG0z3DOGQRBEAyN4c7w9wMuMbNDJe2X3n+l1GYm8AUzu1HSosBkSX82s1uHee4gmCOJVUAwpzJco+02wMnp/5OBbcsNzOxhM7sx/T8DuA1YYZjnDYIgCHpkuAJ/WTN7GFywA8t0aixpIvBG4G8d2uwm6QZJNzz++OPDHF4QBEFQ0FWlI+li4FUVu/bv5USSFgHOAPY1s+fatTOzY4BjACZNmmS9nCMIgiBoT1eBb2bvbLdP0qOSljOzhyUtBzzWpt28uLD/jZmdOeTRBkEQBENmuCqdc4Cd0/87A38oN5Ak4HjgNjM7fJjnC4IgCIbIcL10DgVOk/QJ4H5gOwBJywPHmdlWwEbAR4Hpkqam475mZucN89xB8IonPH6CfjIsgW9mTwLvqNj+ELBV+v9KQMM5TxAEQTB8IpdOEATBgBACPwiCYEAIgR8EQTAghMAPgiAYEELgB0EQDAgh8IMgCAaEyIcfdCV8wYPglUHM8IMgCAaEEPhBEAQDQgj8IAiCASEEfhAEwYAQRttXIGFkDYKgirlC4IcAC16JxH0dzG5CpRMEQTAgzBUz/CAIOhOrhaAOMcMPgiAYEELgB0EQDAih0gmCYNQJldTsIWb4QRAEA8KwBL6kJSX9WdKd6e8SHdqOlTRF0rnDOWcQBEEwNIY7w98PuMTMVgcuSe/bsQ9w2zDPFwRBEAyR4Qr8bYCT0/8nA9tWNZK0IvBu4Lhhni8IgiAYIsMV+Mua2cMA6e8ybdr9GPgy8L9hni8IgiAYIl29dCRdDLyqYtf+dU4gaWvgMTObLGmTGu13A3YDmDBhQp1TBEEQBDXoKvDN7J3t9kl6VNJyZvawpOWAxyqabQS8V9JWwALAOEm/NrOPtDnfMcAxAJMmTbI6HyIIgiDoznBVOucAO6f/dwb+UG5gZl81sxXNbCKwI3BpO2EfBEEQjBzDFfiHAptLuhPYPL1H0vKSzhvu4IIgCIL+MaxIWzN7EnhHxfaHgK0qtl8OXD6ccwZBEARDIyJtgyAIBoTIpRMEQROR1+aVS8zwgyAIBoQQ+EEQBANCqHRmA7FEDoJgTiBm+EEQBANCCPwgCIIBIQR+EATBgBACPwiCYEAIgR8EQTAghMAPgiAYEELgB0EQDAgh8IMgCAaEEPhBEAQDQgj8IAiCASFSKwRBMGJEWpE5i5jhB0EQDAgh8IMgCAaEEPhBEAQDQgj8IAiCAWFYAl/SkpL+LOnO9HeJNu0Wl/R7SbdLuk3SW4Zz3iAIgqB3hjvD3w+4xMxWBy5J76s4ArjAzNYC1gVuG+Z5gyAIgh4ZrsDfBjg5/X8ysG25gaRxwP8Djgcws5fM7JlhnjcIgiDokeEK/GXN7GGA9HeZijarAo8DJ0qaIuk4SQsP87xBEARBj3QV+JIulnRzxWubmueYB1gP+LmZvRF4nvaqHyTtJukGSTc8/vjjNU8RBEEQdENmNvSDpTuATczsYUnLAZeb2ZqlNq8CrjWzien924H9zKxrCJ6kx4H7sk1LA0/UGFqddv3sazTOOaf2NRrnnFP7Go1zzql9jcY5B6GvqnYrm9n4ypZmNuQX8H1ceIPP2r/Xpt1fgTXT/wcC3x/i+W7oV7t+9jUa55xT+5rbxx/XIq7F3NRXL+3MbNg6/EOBzSXdCWye3iNpeUnnZe32An4j6SbgDcB3hnneIAiCoEeGlTzNzJ4E3lGx/SFgq+z9VGDScM4VBEEQDI+5LdL2mD6262dfo3HOObWv0TjnnNrXaJxzTu1rNM45CH310m54RtsgCIJg7mFum+EHQRAEQyQEfhAEwYAQAj8IgmAuQdIYSW8d6vEh8IeBpPnrbEvbZ1s6CUnbddomZ6XZNZ66SLqk0zZJR0r6SbtX6bg1JF0i6eb0fh1JXx/5T1EfSQtKWrN7y9nLaI5L0hKS1hnisRsVvzNJH5F0uKSVS23GFPdEP5D0A0mv7dJmWUnHSzo/vV9b0icq2q0h6VhJF0m6tHjlbczsf8APhzzeOdloK2lZ3Gd/eTPbUtLawFvM7PhSuzWALwErk7mamtlmaf+Snc5jZk+V+lsI+AIwwcw+JWl1PHDs3FK7G81svU7b0tP4OGARM5sgaV3g02a2R+m4jfCgtOIzyIdmqw5h/HXGNdnM1u/Ub2o3P/ABYCLN1/agtH8scKGZvbNGX2sAP8dzML0u/bDfC/wAWAi4DNgE/+wA44Dzzew16fidO/VvZkUiPyRdgd8TvzBP6YGkm83sdaUxvR84DM8DJRrXfVzF+PcBTgRm4N/pG/HAw4vqfE4zOyRr8570ueczs1UkvQE4yMzem42r02c9U9KqwNeBh/AYmB8Bb8Gz0X7JzO7Nzlf3nu44rqxd0wM28SweBPSHUtsVaP1t/iXbfzl+H8wDTMVzb11hZp/vdA3KpDifdYF1gF/hCRvfb2Ybl9r9Bviqmd3fpb+u10zSJ4Fd09hPBH5nZs+W+jk/7dvfzNaVNA8wxcxeX2o3DTgamAy8XGw3s8mldt8CbgLOtF4FeN0IrdF4AecD2wPT0vt5gOkV7aYBnwE2BNYvXtn+e4C709/y6+6K/k4FvgzcnN4vCEzN9r8qneM2/Ee/XnptAtxe6utvwErpCy623VxxztuBLXHBs1Txqhj/y3gY9ZPp/3uyPrYEjgQeBX6SvU4Criud72fABjW+gwuy6/GF4lVqcw6wWI2+rkjfUdO1APZJn+0/pe9pGrBnh/4W7rDv+vQ3P9fUinZ3Aa+peT8W9+H/pc+8LnBj3c9ZajMZWKzU5qbs/xPT60/A08AZ6fUU/kMH+At+3++XruMX0r32CeDSXu7puuPKth2Tzr9Xel2e7qlzgB9n7Q4D7gXOA/6YXueU+pqS/n4S+FZ+Tvzh+lx6zcjezwCeK/VzY/r7TeAT+bZSu0vT8Zek8Z5THlMv1yztWxN/6N4H/BbYdAj34uSa9+EM4H/Af9tdi3avYQVezQaWNrPTJH0VwMxmSnq5ot1MM/t5u07MbJUez7uame0gaad0/L8lKdv/f8AuwIrA4dn2GcDXKs7/QPPhVH2GZ83s/KrBFOOXdDR+Y56X3m8J5DPrh4Ab8NlSPiuYAXyu1O2mwO6S7sUT2hUz2/JyekUz26JqXBkvAtMl/Tn1VYx771K7hczsutK1mGlmRwBHSNrLzI7sci7kBXSOBxYB2q2anpC0GmDpmA8CD1d096iZ1a3PUAx8K+BEM5tWui8KKj9nqc1MM3u2+nAws13TuM8F1raUlVaes+pnqdmixX0vaQ8zK5b6x0vas9Rlt3u61rgyXg1sZmYz0/l/DlyER9xPz9pti8+K/9Ohr3nS59oe2D/fYWaLdhtIxowkKz4C/L+0+py3ot23avZX65ql86yVXk/gE5XPS/q0me0IPC9pKRr34pvx1VCZP0raAzgLn/yQztu0gu/xmjQxpwv8vl6o9GV9GFjFzA6WNAF4lZldV+rvJUkLZuddrdTvycDJkj5gZmd0+QwPJLWOSZoP2JvqAjCXSfo+cGbpXDdmbTYws92zfedLOjh7Pw2YJum3ZvbfLuPassv+gqslvd7Mpndo86f06kZHIWxmR6ZrNZHm5f8vS/38mMYsmyR4/1+pzWfxWehakv6Jrxg+UjGmGySdCpxN83U/s6LtZEkXAasAX5W0KD7T6ulzJm6W9CFgbFIV7A1cXdHXxELYJx4F1kj//y+pjxYDFpI0ycxukPRqYGypn4739BDGtQKwMI3f48K46vVlSXm/d+NCt5PA/xZwIXClmV2fVFV3lhtJehuwupmdKGlp/IF3T9ZkB+BD+Oz+kfT7/n65HzO7osNYcrpeM0mH4xOsS4DvZLLkMHlySYDP4/fqapKuAsYDH6w4X6Gy/FI+XDzFfH7OshxbCViuQo61UmcZMFovXE1yFX5TXQX8HVinol1dVc3P8dnRben9EqTlVqndu/Bl+ePAb/Al6aYV7ebHb7Cv4cvIbwLfLLVZOvXxKPAY8GuSqqbU7rKKV3lZfiGus52I60T3x/Xn5b42Av6crlehIqm6Hm8Ddk3/j083ULnNrcBLwB243nA61Uv8BUkJ8jp8n6sCFwMvAP8ErsQz+xX7f4ULl6Nw1dSRwE8q+vmbtS6Rp7U558K4YGg3phMrXie0aTsm3ZOLp/dLtbkfqz7nxFKbhYBvA9en1yHA/BV9/TR977vgAuF84Mi07x3pe7ktfZdn4Cqqx4BtSv1sXnFPb1JxvvK4vt1mXJ9I99WJuMrwblwlszBZcsRsTL8gUzNm+8cCn6shCw7A1UF/T++XB64q9XNxTbny5vTZ/pXu7ZepUInUuWbAx/EVXdV5Fsv+nwd4LfA6YN464+ww/lpyrOo1RxttAZKBY018OX2HdZ+5durrRjNbT9IUaxjyppnZuhVtl8JvDOHpnVvSlEq6AH8YlY0sQ7aidxn/kviNX8xm/4LrPMsrmdtxFU55XE9mbQ7A8xutaWZrSFoeON3MNir1tXLVWMzsvqxNXUPfKmZ2j9yTYoyZzSi2pf234eqLjjelpN/jqrSf4t/R3sAkM9tRUkdDn5kd3ml/l/OWVxFFn3+p2p5/zop925nZ6d22pe3vI/vOzeysDmNcGnjazFrUhjXv6U9Yq1PEoWbWUsMiqWE2TP1dZ55Dq9xm5/I2aDGwX2Zmm7b7TKnNVNxedmP2273JMhWkpHOAj1rJaFrR1w3AjsDp+G/gY/jKoUUdW/OaVRqlVcPwXupnXtwmU3zXl+NOB/8ttastx8rM0Sqdigu2hqRnccPtY1m7WhcK+G/StxVLtPFULMklXWJm7yBTU2TbcrrqtyWtghu1JtJ8Q5SF4WI0C/MrcKH5bHbMU7iBsxtt7QEZ7yP9gFLfDyUVRRNmdp9cR/72tOmv5qqjnAPxH/7l6Zip6XOXOQNYz8yez7b9HjeAgxseX0W1rj1nd7xO8grAg7ju+LNpX0/6TUkr4iuJjfD74kpgHzN7sKJ5vtReAP/Mk4HNSn0ujguRibh+GmixaXwVFzh02Qb+Hc0ws4slLSRp0aqHSDrHE2kMm5vZn0u7V8BnwvPgOu4WoQN8UNKLZvab1M/P0metYgw++50HeLWkV5cffmZ2clJlFmqoqknb1ZJ+ihtJcxtQrs58ycxMUvHbrXJzrmtLwszukjQ2PRhPlNSitkoP2kvN7E/p/eKStjWzs7M2h+IPj1tpTK4Mn4y9p2KMZG3K1/7nuPrrqPT+o2nbJ0vtasmxKuZogY8vG9+CqzfAvWCuxQX/QWb2q7S97oX6Ca7nX0bSt3E92izfbEkL4EvapSUtQbN74PIV46uj3z4bNzD+kc5fygm4wNs++wwnAu+X9EfSl1tF+eFBPXtAnR9Q4Yr4KRo3568lHWPNxtUqQ59lfayFL2cXKz3Ex9EsTJYGbpV0XWnc5c8nM/tw1XjNrK5BruBE3KuiiFP4SNq2eUXfTT/gpDv9XkWf5+H36XRK37nc0L4VsIKaXRvH0WrYRdKngN2AJYHVcKF9NBVZakscD0zI+jkBd1e8JRtTldB5P3COpP/hdp6nzOyzpTZIOgzXmZf7+0up3SZ4vet78d/TSpJ2Lj0YikCig7JtRvOD9DRJvwAWT9fk48CxpWHVtSW9kB5CUyV9D59gVN3/B+SrKTN7Jq2Mz87avI82RmlLhvce2KA0S79U7qpZpqMc68ScLvD/h7vMPQqz/PJ/DrwJv7EKgV/rQpnZbyRNxn8sAra1Zg+NTwP74sJ9Mg2B/xwNz4ictwG7SLoHF1BVni4vmlmVz3KZ1czsA9n7b6VlLLi6pBfelP7mKamH8gMCf+i+qZiVpx/6NfisuKCboW9NYGtgcZpnPTPwh0nBgXU+HP6gvQefEZ5hZs+UG/Qwcx9vZidm70+StG/NcTyI62TLLGDtfch78aQCX7lsiLv3YmZ3SloGZqkwqhBuX8h5s5mt3aZ9OVblk7hQuwo4SNKSZbUh9bxvwIOE3mVmd6TzrAH8jsaqjm7qnNTmB5I2x3+La+K2sj+X2pxceXArH8VXJ3vi13wlPNakTFVgallmdjVK11m9J16WtJqZ/SMdtyoVHn015Fhb5nSBP7EQ9onHgDXM7ClJ+bKw1oWSdARwqplVCW+sR/dA6nm6HJFmBRfRfrYN8G9JbzOzK9NYNwL+ndrO8iqQew1MKH5AbT5Hrz+gNaj4ARWnpPlavkzjQViwF25A/g/+Y74QyL2H/gD8QdJbzOyaDmO6Qm4zWL1QX9DqbYKZrS5pQ3wpvb+kW4FTzOzXWbO6M/cnJH0kjRtgJzzGoQVJR9JYuYzBi/lUzcB+lR6i51LyGrPePKkA/mNmLxWrJ7lNqxjD29Pn+ld5qPhDIucaSWub2a1tzjM59avs77vTq8VThHreN+AGyln3qpn9PalgG4OVvll1oKXgvtTmc7iNqeoeLdqsDnwXWJts5WhmTWNPasoFcc+WTivCG+ReOD/Dr8FeND+kwQ3zU+UR4fl3nauR2q7eS319CV+d341f/5XxoK7y51wSl4W/y7bNW+d+mtMF/l/lfsiFXvMDwF+S+uGZrF2tC4XrQr+eZhln4cL/hnIjc/fA19F64/yy1O4+NbuKjcd9w3Nej3/Bm9G89N2s1O4zuKvnYukzPIV7ZsxCmXEUWEXtjaNdf0CJ6bh3jdHsO51zIvA3ScXSdltcXZD3+wIu8PenM1MkfRZX7+TX9eNp3LXVF+YuaNdJ+g5uwD0Z94AqqDtz/zhu/P0Rfh2uTtuqyO+VmXhU5VUV7V7C3QH3pyGcy0JzoqSuwgm4QtLXgAXTA3oPXD0IrjZ6wSrcDNVwCSw4GRf6j1CxGrXeY1XqCDpwoXk8jdX4h2kVmrlNZwF8NViesY4DLpT0FHAK8PvSZBD8Xj0A/y43xWVAld98rd8RLuC/ga8kRbOtqKAI3OpEp9X7LMzskvTQKpxUbm+zgroRX5U8ndotDjws6THgU1aKzM2Zo7105NOa9+OqE/CZ13JtdIrz0/1CFW2XxB8eO+Kz5dVL+w/A7QVr4/rYLXEf4Q9WtOvo6SL3mFnHzF6q+ZnHAZjZcxX7CgPh5dbGUyFt+0L2dtYPqBCsqc0ncTfSS/FrtjF+059Qcd718O9AuJfIlLS9J9uCpNPxiOIP4fraD6dx7ZP2TyWpL7LPN91aQ9DH4brTHfEHw1nAafmNLuli3F0wn7nvaq2G99pI2ietArtt+weuBmtbgFrSlTSE03tIwsnMDii1G4Or1d6FX/8LzaxK9dZt7Hfh/uBNdgVL3laSNjOzS9XGs8RaPUq6et+kdvPjQnLW/QMc1eX3OT8eYPh/FfvWwW0HHwAetCylh1K6kPyekfRXM3t7qY9av6N+IekaPNVFvnr/gZm9Jb3v9dofDZxlZhem9+8CtgBOA44wsze19uLM0TN8M7Pix4Mvh+7BPT2AjhdqNVV7IBS8Go+Km4hb18t8EA+bn2Jmu8ptB8dVtKvj6TINfwI/RgWSPmJmv1bJnVANz47cjbBWFKSV3EIl/YDWWciXgDdactWUu59djS8/kTTOzJ5LD8d706vor9DpFraF9+PeNcUMe6e8fcarzWw7SduYe2/8Flf/FHRSX+RMw3XMB3VQEVXN3Get+iR92cy+V1LTzKJipgruB39EadsuFdtuwWfAnVgwzeiUhO6Bkv6KPwRyDjSzb5LsK5LGSvqNJaO16ucyut/MOs1EN8Yf/lWeJS3G3bJgb0cS7IfTHJHejYVoVSEVPAY8gk/+lintezE9IO+URxr/s6IN1PwdJU3AF2n1sNssa1NHjVS1es8fmD1de9wFOQ/AvEjSd8zs82qTvLFgjhT46ULvSEOfeio++ynrpnu6UHKD4/uBf+BPw4OtwuAH/NvM/idpZppNPkb1DVjH02VZ4HZJ11PteVIcU+VOWBZEdaMgy1T9gB7EDYUFM4AHsve/xVcGhW63oNDxrlqoEiQdbGa5j/ofJVX5phc6xmeSyuwR/MdU0El9kbNquu5tM5CaJ8aatcKQe13tgQcSQUNl0KLSKyMPrf8QvvzPheaiVOv7X8bVHZfRXt1RVzhNkPRVM/uu3LPkdGBK1ufLkl6QtJh19j+/PT1g/0hFRHGxsrAuniWSTjOz7SVNp/pBuU4v7VLbvM1YPAiwSf0o6TP4zH487sr7KWu1R+yL3+t74zakTWkWrAV1f0en4yrF46hOhwI11EjmNb3XLVbv+GRgBzyQcda1xycw95Q+d5Wq7SlJX8FVW6S+nk4P/47umXOkSkfuEvZXPET6rrTt7gr9ZtF+laoLVbFtd1z313apndodhUfP7ognpPoXnuxo11K7LwKr44bA7+Kzyt9aZvCVtHHVOcp6V0kbWUkfXN4mN2LuT7a8xx9aL5aOq/wBmdlPs5XEG3D7wh9S223w4Jnd6RF5wNS7zezu9H4V4DxLWS6zdp/EV2ivx9UtiwDfMLNfpP0t6gvgOCvdpMpy6VgpA6ncVfIbuKfVWbhK5yDcL/53hfoo66trAJTckLwK/h3nAUgz8KjjmaXj6wQbbYA/dBbHhdNiwGFm9rdSX8KjPKfjAuV8M/tRqc1peHBQW/9zSbk9I2syy35SK2BN0nJm9rC6BOTVbZfa5m1m4vmNytf0UNwwP7XTOFPbha051qPYfh7+0H+Mer+jrhll1UGNlAT8Z3Fb1B/w6OvP4quGaWa2Tamvqiy3LWOQB9cdQENNdiWenuJZXEV9V9vxzqECv9DPvhXP1ngK/sOvNCzVvVBp+xK4kM6XX5WRkqn9RGCcmd3UZv/mNOtXW7wIkkpog/T2OsuCxrp8hpZtdej0A5LbHdpiJa8FVQSclbdJ2gLPW3N32jQRF8C5uqbtWPMffx0k/Q1Xu51jpdTHaVZ9Be46ugVu8L0FD99/pKKvvl33Uh/dgo3K7ecBdrBGwFN+/nnx1ARXkQzmlnl51XnA1Bhvfl98Op0v76t8XxxmZl/p47Z1aQT3/aXq96YKB4l8UtdpIpD2b4+nsDgZ+F6N7+RA/OFwFm1ydMlz47wdX3Vciq/UDjWzNSX9ATesXoPfh0vghuJ98geXGnEq36M5uG8crvvvmG+/F+ZIgV+Qluzb4qqdzfAv6ixL+cd7vVBphrkPnuVyKj4ruqakkxsLLGGNiMX5cD3t58oz1uyYcTTr+PIbYnvcY+Ny/KHw9jS236f9b8EfbPviy8L8M7zPPH/2j81sX7Uxklqrd0GtH1An1AhCu4wOOeqz9vPjdhGoMJqnz7lCGstjcuPbfsDbzWyl1GZrfLa7Ms01AcaV+vqbmb1JFaHlKoWYS3oUn/WUx1MEQG2PqwwLxuHpHcpujciT9x0JvAb/4Y4Fnq8Y3yaUgo2Anc3D7fNZ3zn4rLxl1pceXO2w/J5N7SvddTUEW0V+XdvR5kFZ5UDQtZ1ag/veBzQF96meg0TbiUDWZmHcWWEL3HMoN2A32RnksR5lzDJNQ5uV2vfM7NrSrH8snklzgpWipCVtg8u599Jsa5uBr2quLrUfj6dtLnu7lT3/WpgjdfgFaVn2G+A3cuPhdriQKApO1A3oKdgHn2lfa2abpgfGrJmLpB3xmc3zku7EA4F+hSdaaonslPRpXF3wb/zGmaXfzprtjweGPZaOGY8v7X6f9s+HqzbmoVmP/xyNjHqFO2itAKyKH9BvVIqOlTQpja0QrkCTbrXXILT1aRi31pUbzX+ZzvV9/HuaCnxF7mq7B17cJneB/DFuY5lunWciHTOQqjlK+hE8k+TC6fMVD+NeA6DAjcDlHCyvrmjXKdjoVzRmfZ/EJyrz4cEzU4sO0v05BtjOzE6lA+rsZljoubvaKjLaXnu5Ln0PYFV5wZGCRfEVSLndahXtyvryOsF9dRwksO6pyP+Lq73mT2Npq/Nup1Eotbk+jXkMsHdJmP83a/eypHvKwj7tqxWnkvEbfJKyNZ5mZGc8xUV3bBhZ2+aUF14Fq067ohDBVFIGQJoLm9yMe5KAZ0X8Dz7LbtffnXjO/k7nnF56P6a8LW1fuUMfF2X/f7XG57yJrDgIbhi+qdTmDlzYrYIL/ZWrxgDsVeN8HbNc4kJngfT/EvgDcvWKfi7Dk411O19VBtIl07576a3YzThgbPZ+LO2zH95QXN9s29VV17/dtvy7T+d6ms7ZPP9S43pMprVoyfTyvVP3RUXRkGzfYviD/Xf5fVNc/17bFWMt7o/0foGK3811+dja3NO/x1fLN+IPvy/iM+Ri/xbpXjy03XdcMbbX4avAjxWv0v5Jafz3ptc0UvElUhZOGoVbZtKhYAmueTgr3dOP4vauFau+74r78Io6n2eOnuF3o1iuAh9SKlKQY63L1Qflia3OBv4s6Wl8plfwkiWDh5ndmJ7IZ3UYwj/o7n53gaQLafiD74D79pd5Ic2Eq5Zp47N22+HGw07UiY593Dq76RX8T9LilryZ0ux5JzM7Kmszic5ZLv9tySBmZk9LusPMWvKd48vU8+TlCXOdadNS21zd1rTikrueftHMJtb4TDkX4UVkimjVBdO2qkLRdXOwdAo2qjXry/iz3DmgnFgsT3XQKZfR0h36noUahn7RPCsvB2g9ixsHd0rHLYPfr4tIWsRS2cCinTy6/aniM0paVNKbrNk4fSJdgvuoTgVyXOrzKHzl3ympHviKdjszu6XmNTmAingcGitucDfmPczsr+mYt6XPs46ZtUSJd+FE6kWHF/fQw5LejcuwFeucYI7W4XdD0nvM7I8agtFK7j2zGHCBpaAoSQ/S7C/8+fx9WfBIeiPpZqVDtKGkD+A5XYrApZaHiLywxqn4rGTWMs3MvpLrQat0ohV9fT4dn/+ATjKzH2dt3oH/aMuRkmVX1qlm9obStimW6XjlAVV7W3Ohjrz9MzSSahV2jFmGcmvUcb0IF7zl4KAmg2Gbc9xvZhNK24qgPcOzfJ5dcVzV52vZlravjM+85sPVPosBP7OU0iNr1zbYSF6x7XkaD+AF8UlDO3tFHT3y8fj3uB8ekLQ3ntJgd3n0+Rcr+ig6OjP7bG2xkmE9qZEOx1V+j+Gz99us1W42Bc+Qaun9GHylVNbrVwb3ldpUOkhI+jKuwjzAzH7b6XOk9r8ys4/W2DadRjzOukrxOJYl0ZN0lbWmFG/a1sP5WlIct7k/t8a9GFfCV9Pj8DTpXSdwc7XAh1nGkEPN7Etd2o3Bl0BVya6KNgd06qMseORZHa+kVUDV9o7I+ircu2YZtCRdYWYbZwKzRVim81UZbTv+gCT9GjeyNmU7tCwaN7W7CVg3+8GOxa/ja7M2l+FunpVZLtXGNTVrV/jz32Bmkzq1bYekBywZf9P7o3D9er6y+oeVorTlXhZ7WfJ6kbQ+8FNLUZCltvtYvUjbhfGkeS+n92NxFWK31eCQULO7Lrib4SFm9qKkJ3GXwKooo6rve0/g11Ydn5K3m4Y7UlxsZm+UtCm+8tut1K5KYN1kZuuoOWFb1eCe6rQ/f8jLc9IfjieNO5rm32N5EtM0aUrfz3QrJZeTdJ2ZbSiPzN0UV8XcXLr3f4Q7N/wOn1jsQKMGcaEpKJ9vHvw3VD5f36PDy8zVKh2YtSzu6Cub2v1P0jRJE6xNtfpCoKs6O2AVM61NVkRJM6g2flXO5Oi8TNsma9fWcCv3GFjazM5PAqwQYu+VNMaac2ysa6WUBW24EF9OH50+z+64q2zOgZ06yAT61rh/fjtD2cWS3mXJC6tMBwEhWgXaxsDrsgfVyVTnC9oXOF1SodpbDv/RVrEz9SJtL6GLmiipoE6w9snMinZ1aj2saWbtchndVxbqXXgVrpK6EVdXXFhcwxL/NbMnJY1J99ZlcmNrmbsl7Y1nuQU35Bbuu3nCtuVoqFernB+qmPWdm9k/Jf0JD6x7DxUpoOX1bovAvueyPl7C3YrL3CBXAR+bxvovfFKT84b0tzxZfCseNLdMD+erlddJ7vjxKVojgLt/z9ajQWdOfOFeEefgScreX7yy/e9Pfy+lXrX6O3FPjK1Iq6A25/02nuxrOTzh15JUG6Wm1PgMW+MqgtfhxsvJwHt7vA6XUyqll7a/mtZyicfievdufY7BBc7v8VnLp8mMnFm7lYF3pv8XosIQiRtX/4G70b6mYv8M/If6IhXGLZLhlRoGWfxHvnJpfL9r8xnnTdf99VSUn8NnWn/EZ27nZK/LqSirR+YI0G4b7qFzFa4O3J2sHF6p3XG4i+dm6XUirlbI21yG5yg6GHhtr/dexTmF1ww+BS9P+B08AVje5mLcu+xIfEZ6BNUG7GVSP4Uh8rfAMkP5jVQcc3/6+1p81XsKnmur23HfHcK5JlJRzrLmsT2fr0t/VwOH4cbkDxSvOsfO9SodAHWPIixKglWqFqw16lX4DO3jeDKvU3Ed+N9L7brqV/Pz1/5AbZAnXTqQVj/1VdP+lkRj2bFl//Tb8MRj99A+l3/dcc3Kcmlmq8nD1Y+2iqWo3A99JzwE3XAB9jvrbLjsZSxFrMJiuAtuMSPbEBdILTln1CUzqnqPtO1FTbQmfi12wh8Ax5rZZdn+Kr1u1bZX4QJgB1yne6qZHZI+223Uy7eT97duGtcW+APlzcCfzezLaf/C+INZuFF6MVwVVGdlXHW+yt+I2kcAC9jfzJZM9/I+1mZlWNHnRvgD+Hl5auz18KRjRZRwx9+qNQe9LU5W3SxrU7bjrUBFGcRSm1WoVx1vqlXYmOrwShH4HdMSDEfgJt3kr3FvjGnAfpZ8ZSUtYK3h2FXb6hhaT8Zv2mfS+yWAH1pzhsuOtWol3WVmVX7hLftUI+Q9teuaHEo1s1xm7ZfGPRD2xYXRq/EqPj/FhccqZnawPE3CcuapkMt9VBpk69oLsn4qPTGslBm1dMxSuIrlfqtIRZtUa6fQUFEsh0fRTi61G4uv7HbFDXCnpc/0vJntmNrciHuW5LUeft/ufpL0etzbaQczmy/bfg716r3ujauunsBXF2eb2X+V8v6Y2WptjlsL+IKZfaq0fQHcz74yJXbWrp3A72pXkzS/dS/Ekvd5E26MXQf3pDoe1wJsnPZf1uFws+ZAzaupqG5mzWk0KssgVgjyaWks5b7K9+wh+OSlytuvM/1caozWiwq/4Xwb7gVxU7tXxbFL4UFaN+Al096PP3EnAffUOS+ZaglXQ+Tv319x3JRu23CB2uk6HI2rmVTa/i08cjHfNqHqVdHnlXhY+E34DOVA3COgZVzFeNO1qrqu78E9h27CA46WSdsXAu7D9bw/w709wH32r6/o5yhcJ75rel2Ae8uU2y2LC9StqVAjpDbTcbXVtOyYP5banIvbA8CF98O4iudWYN82/XZTEx2Oq0t+AWxY2ndH9v87gPtx9dEVuK/3pqX2r0nfy82pzR7lz4s/TO7HBcpPilfFuL5Fm5iQdJ510rW/GU9TsCyu6nsQj0YvH3M6rmr6B/4guQifTYN7wRWvB0vvPz8EOTADVwXmrwfSPbdqxW/0m3i+rlnbhnDOrsfhMS/z12jX8fdd+pz/w+NZ2vr1V73maqOtGmkJxpeWfuNorpR0D50LCpe5Bn/yb2vNJfFukHR0Wj6vgBtj3kjDeDQOF16UzndF6b3RmvJ0jKQlzOzp9NmWpNWo3q1W7RfwWdldahRYWBd/cJXr+/6JhsFsAVxlcQc+E8upk8b3CtXLcrkd8CNrLXT9gqSPA983V71NSduflvu9l9mYLgZZtaa0OFLSrJQWGXUyo65iZjen/3fFVRsfk0d6XoVHCJfZgMbS/I3KIo8TNwNft2rPnQ3lxVquwu+dbkUxTsIfSp/BH5Av0krXeq9pFv8BK+XkLzCz2+TpC35OI1fRjbhe/sNtztspJXYeKXssHQrQ1zRUHo6vqn6LX6sdcSP0HbgBepPUbkYy4H4EL+Y+Fn9AV523WyGkttXNsjZ3U6862BGqUR3PzNpep27M1QKfemkJwAOqmlQVXVizECZlzOwwud//LrgXzQ9pCPzncC8ArPcCxj/Ea7UWAmk7Gql8CzrWqjUPTd8pLfsLwX2LpSyWpc9RLiqyHm6QLVMnje9++LJ9Oq7L/5OZtdQPMLOPVfRf7LtE0n/Tj68Q5OOpDn2/A1+RFN/pSqRUsxndUloU1PHEyD1i3pHaYmYz5Jldm5D0K9w+MpVsCQ/8MtMPTwXWUiknu5ndaB5EtSJuCF0rfbar8QfAAyRhIHfv+0461/vw1eOKcpvW/pZ58iRhW5lvJ2vT1ZMNn6melP6/Qx4Utp8lF9QK2qbEtt4Kzv8B9z2/mPapirew5uIfx0i61swOShOSgh3wdNefMLNHJE3AJwdNtFP30Rx4Vae6Wd3qYK+nXnW8QuVbOwnkrOPayLW5CjUibvNts1LcSvqpme1Zo5/K5GQFlunckhDcyVJ2ww59Lk4No05quzb+5Qq4xLq47HU4p/DZyyrpZp8AvMoqdOGl46oSXZWTQ43DZ+LXypM+rWipRrA8LmE8fg2/bI0EcbVcVCV9GP8xrod7pnwQnwUX32Ntg2zZhpC+r2nWSGa1kZldlet/1SYzajrvRbja4YR0XZ9JAvQGaw02uo02kce96IdT+/nwB/xbgbek1zNmtrbcB3xRXJVSRLKOw113/21ZKmhl+XbMbBW1L495KY1rm0f2FjEVt+MG5uJJ9RtceCq1u7HUX9uU2JJ+0uFaNP1GVMNQKa8s9SMaD/UP4qqhN9c5vqK/OoFX/6B7dbOdq7Zba3WwWtXxVCMJZNtjXyECv1aKW3lSsRNxnddxeDKm/ayRfbNXg99frLnwR9XYuhp1UrsJVJDPtOQVcw6g4ZN9Bf6jbTLESfp5OtdmZvaaNBu4yMw2yNrkKrAxuJBdylJZOaVIQFUEFmV9XAXsaGYPpPdT8QfWIsCJNoSAEbnx7x00Hnp5UrTa309Sfa1DI4hlR9yuUHiZFIFudQzqy+BJ8pbDbQXF/bIpnjflB6X2HSOPeyF952/BI7Xfgj94p5tXYrsTWKP8YEmrpNstK92p6rJ+VeUjO3qy1X1gFfeNKhwqsnMVgnAjfBZdJInbDs8X87msbVdDZVrZHoFfJ8N/d5/DV6XrW6PEYFXW03+Z2WKl/uoEXp2D/wY6BtV1W12lNqfi3l2V1fGydtNpJIF8Q/rNfMvM2sWPzGKuVumokeJ2hdJsYVGal+EFH0834f/hM9Fd8QfARdAq0GtQJ8/JAtYmOKtEoVMHD9Sp0qmfgOt+t0/vP5rG//5SX2+y7rrwXAU2M53/jGzb+nJPno9L+iU0BzalzzhfIewTV6btT6lUjUr1Ip2PwN0Jq7JxlgV6xxoDZvYluSdPkdLiaGtOrfDfpPoo3zvF8Xtn/z+G+8qX21yGuyyWWRq4Na14qqqcFZ/hrbSu/H6Z9h2Df/czcF/9q4HDLdl4Gs1bZ2zmwYjl7Z3y7eTHdvwNWGvVuXbsigvfI/HJRFVfJwNI2gU3RP83vT+aRkbcgn2Ar0l6icZve9bqML25m/a2uiuz/6uynq5ecUwddV/X6maqXzS9W3W8ghfNo6hJK9Tb5e69XZmrBT5uoJlMa4rblalOalbc7VvhM9Bpyn4B6qEsW6IwGOXh+mX9XR2jTl2d+mpm9oHs/bfUMM7mdNWFW3f96dG498uqNKdHhsZnXKLUZ642G1/aV0c/fCPwdXk64bNw4d+S1lcdDLIl9VE+5k9JehH3Ftkf99x5Jz7rbXGtLJ2vtqovcWCn/lKfbfX86f8JeArfO/EZ6oPAM6VubpX0MWs2IiL3Lb+91LZWWb82s9+qnP8L4d40E8xst9TnmmZ2bmpym6R7cYeKXEVWFe+xPD4BKX4Ti6Rts7Aahkr1EIFqZndJGmtuezgxrcTLbfZI/x4t6QKqCyGdnV6dOBBXO16e+p2q6tKFHV1QM7olgWzLK0WlMy8+G/oQWbFzM/tpqd2JuHfNKrhubiy+xF0/7a9dlq2HsX0WN74+Q2bUsTblGkvHlnNwXIMXTymWphsBP7BSQI8auvD1cb3pLF14ugbtvnQzs0+U+vq5mX2mzfh+g1+/Y0vbPw1sYmY7lbZ31A9n7ZbEowd3xAXK6qX904DNrWSQtVIwUsV4x+Kukr8pVhqS1jWzaV2O60nVVwd10PNnbYTf129Nr9fhQvEaMztAHsxzJu6eV6Qp2ABfIb7PzP6Z9dU2307pnDdQMfs1s6+V2p2azvkx80pjC6ZxvSFr86p0npZcT9Zc4nBXXCgWq6WN8eLtZbXne8lSTGQPl2L/1bhhtxynckap3V/wh/1xuBH5YWAXaw5MnAd42cxMHg/yJjwX05TyZ1GX6maqLtjTUiwmbe9aHa/UfmNKSSA7tp+bBb6qi51/0cwqBXZSK7wBD8N/JgmWFSue2r2MoVuUZlejTmrXUaee2rwBN2Yuhs+UnsJv1BaBpTa6cHnmzjIT8CCosWbWlGZV0mrAg+aZHjfB9eK/TNdvGXyW8R9S3h78ITM/7tL6aKmvjvrhrN2G+ANrW+BWy4xkaX9Hg2w3JH3aGnV0v4f7k/8bX9Gsi/vW/7pOX6V+i9VFkQtm1i5K6gf1oOeXe+xshAv9rfH7YvFs/2b4g0G4V9YlFX28sUpYVbS7wcwmqTmJ39Vm9tY27XIh1hIBXDpmCWCl/PeWvrs3466LhYfN36xUjlIevLQBbiQG/81PNrP9sja1DLPqkvU0rcgPw1U4B+MxIzfiNr8TzOywrK9NaFPdLGvTNptpaVzdquMt2elzlbUG7RrNtS9cTXEFqWhJ2tZS5CLbtxGpMAjuxXI41UU/3oxXufoX7nb1MtUFCw7AZyWP4rr0R/AoyLzNOdQotpD6Kl774xGnC7RpOw5fXnbq7214pj1w9coqFW1WxWc5f8d9uOeraDMVXx6/GleH/AhPgJa32QwPCd8LNxQP9fs8DFdhXICryxZv0+77+Mxxl/S6AC8rN5RzTk1/34f/cJckBWFVtF0d9wC5FRdQd3e639r08cd0T1yG5+a5kIq8TrhQOAV3w7wbjwvZA38gdS0SU3Hey2iTb6fU7i+4IPwlnvPoc1XXA1cHLUgjiGk1UpGSUrvL0/26JB74NRm3ReRtrqkx/pvyz42vzssFUA4BthrifXBq9v8tuLpyAr4SXTptXwh/oObHTcZVWcX7NUgFSrJtC+Gr/OvT6xAqftt4JP8y2fvx+bXH5d392b13Dx2K+1R+zqFcnDnllX6kp6YfxbH4jPaeLjeN0o/mJtwQ1FIpBg9UejUwJd1YuwLfrmhXJ0rzLFyg/oIOEY5dPudH0t/PV70q2h+AC5a/p/fLA1dl+1+Dp4u4BReY83Q4d/GD/hKp+hVDSHSVjssjIV+k9CDFDaMdK4hlbd+Pxy4cjq8mhnoP3ZL+Hov7cUN7gd816ji1+wFtEtPh6oq2r6zd4bgqrmsysB4+66vwB8lV6d79ekWblXFBPi7dR4eTTaiydpvjk63H8Vn3vbgar9xuSvr7yeJa0Sqov4XPfDslKryJLDEh/gAp9zPkCFRSIrby/V2+F8r3fnkM5W24/GhJsNdmDB2r4+FG8Gl4pPnbO12vdq+52mhrXkjkLDWKnX8OWFbulniWtSZTmmlmJvcfP8LMjld7H9muRh3qRWmeTQejTk2DYOHxUmW4qjr2fbSp/5lUCZNwofQ5XOiOK2zX1ros/K+8mtjONDwgKqMSu2Elw5ukbfGo0sJOcR2eUnZC6bgiCVktg6xVqDQ68Ee5//O/gT2SPaAqYhTqRR2Dz6SPTXrgE/HkcM+mz1K4Nx5mZl/JD5KnF74itft82raaUqxAWaXWw2ck9fkI8BO5R8mX8dQCh5TaFLr1f5PVe67o68/yPD9vxr+LfaxabTmPpOVw21pV+mbwicvCwMz0PbaowPB8TlPS2IXr8r9aGtOQI1BLFBH0Y4D51IimL6LSc8rVzT5C5gRg7jH1gqTFrEseI7pUxzOzfZJdZxPcQ+9IedGgn5vZPXU+2Fytw69CjWLnO1hrEMsVNNQFb8dnJ1Ot1UOmq1EntTsKj6zdEU9r8K/U366ldm2NOple+/34DKzQHe8E3GuZsUxdksRl2wr/4SJL6ML4snkduedE8aUbJe8ba830uTY+877GzH4n9y7YwcwOpQ9IuhYXLu2w8vfYpp8Wg2wPY1gCnwm+nIyb46ykQ07trsLvm9/jqbb/iRffqXSJU4dMmKqOE2kx5Mm9sCbhnieF+mdNM9uqx8/4GlyAfJCGvev31jB8V3qmFVSM6314yu1n0/vF8Rn+2aV22wHfwF1295D7yn/fmr3N6n6G5XA9vsj0/JLWMndNbOf+WUwY2sVbCDjXzJZL7S7rNA7LXFPVWt3sClwA/ydrcxop2yjNzgq566bwQKoNaC5cdFblgP1674ir6L5mJceJdrziBH4n5F4DH8Lzjfw1zSQ3sVa3tpVpNeocZanebZu+J1IdpbkJXYw6qV1LEFd5WxshUbXti7i+eXN8ZvRx4LdmdmS78c8O5H7xBWNwQbaxVaQNHsY5Zhlku7TbzMwuLY1pFlaqkpSOqYo6/p4112ct2rbLhLkcbtReFV+RFCyKBxaVa/UWD+0v4f7XR6pUYrIO8hw45+I69ZZ8O+q9xOFU61L6ssfxLUFFqoA6wlzSMeauoVWCetaEoRdBXmO844HxVoqGlztxPGpmj2fb2mkRyl5Iky15DLY558J4MaQdcP3+mbjt4YF2x5SZq1U6vWKeN+MMGkEWT9Co+5q3uy99oVgXf3U1p+m9ktacLj8E3mUpwk7uWfQ73JslZ7ykVS3lvUkz6fHp/7pJ4orx/0CexOw5POnWNy3V/8zGLZpTETelX1DvMQl1yL1tZuIPwW2yMdWp7tSROsI+sTE+S68K1DFak9uBF5cpjPm7pjFvhwdGzULS4bgr4iXAd6yR0uIweXRs8SBuyq1foU6DYarUVDPfTlmg12BMxbZZ8kQp3YmkI6m+f/LZbWWqANwZ4PN4fqYfVpzPcCeBoqziZlaawcrTMxfnrC3Q07GdYg2OpFHFK2cFfNX/odTHWDwtdZ1aBNdK2iDdY1U8hjs1/A7PtGrABmkiUjlJaflMAzbD71ioIwnBA4A98dn4GFwwHWlmB1X017VuaptletW2LfCyZ0Wis4nAp83swqT22QRXrRydHTYDNxLfWeprT1y18XSHa9Ex/YJGICahG5KOw4VZMfP5KO4LXc702c9zrlLWf1ZtS9vrrrA+DpxiFeH2ynS5SRgsS3OQ0P2l9sNSqamHfDtpX93AqxPw2JKf4YJnL2AJM9sl7X+Pmf2xzuxWNVIFqEbtCUknWHP9iIVxz6d3lI7bDvdbnyHp67gL9MHWWve5bayBpFuslEMpO+5my9SKql+L4FZ8gnYvjUL3Zg332JPoHEPTEmBW1WpgXvjsYT6arfC5FfxzuJ5tlWzbqrjutCrX9y3QsJTjD4iy29aJeA7yTdLrWDzKt2p88+MeROtSkT+bNnnKK9odgs8ATsNT2LZY82l43+TXosr97rA622qOa0V8RVWUuzsDj4PodP5Kj5k+3hNVNQ3KbnVb4kLwUTJPKzyo7bqs3XqdXqU+98RXmLfgHjPTqfD4SG0XJHP96/Hz3dnm+x+LFzQpb6/robYwcGhqPxlfsSxc0W67bttINQ/S73P+4v8a39ONpfcH4/pzcLfKq0muyaV2N6W/b8MDtbahIhc9nhiv8jdC8oBrc83vKL2vW4tg5apXqc0YYPuh3u8DpdIB/mNmLyl5pKTlbv7E/BgewTnL28DM7paHql+E+6Dn1EnTuztu1NmbZIjB3aqKMeSZPt9rKTNk2vcda45wfEGeFKxcPajJqGlmX5f0DTyqclfgp8lwdLyl4BJqpF9IbA58pbRty4ptdTgRz1W+XXr/kbRt8/T+ZUmrWXN1p3apcIdFmkW+FlispMcfR6snxkO4YCun8JiBTxIKqtQOBUZzmtt9cSH+ZJdx1s3D0va8liRFaWNVvp1iX520A8/TrJJqx1fxqN1O29qmClC92hPFmL4h6TB5Lp71cYN6U5Rtorin3o0/IP4g6cCKdi+lWX3xG1mNRnqUOyVtZaVkbvL8Xnc3d9O5FoE8gPFr+IN2Ol4D97mqtuZegXviD5GeGTSBf4U6F+qY1ypcy8zs8aRfLrMUnjOk0NFuAFyTlnDgrqKTzZd3h7cZ0454gAu0/hC2IOXXT/wG967YGn+Q7Ix7GrVgZibpEdzLaCY+4/m9pKIu6U/w2faykr5NSr9QHC/pM/j1WVXNuVCKoh9DYbyZnZi9P0le6KPgS3iRl7vxH/bKJF35CLAmfh0Xp1mPPwPPxzIL80jmaZJ+Y6X6taV2veiIHwC6uelB/Tws7egl3w74pGI+PCHY93APtYXLjZIt6ou05q0pDKTtEhuOw+9HsmPel/49UG5YXQz3pgMvpr4LvjrMf0MzSL+N0gP7Otwr6DrAJL3fWnXb/5T0C9wT7zC5p02VTeKANI6V5GlENkpjAX/QnyuPji0mAZPwTJ1blz5fk3G2gl+mPo5Mx/4kO08VdZI2VjJoOvwxeKGOd+EC5UI8v3XxBG+bKreNrnbjqrYFZnZFulG+am0Shqk5NH3W/23eFyl987D3KyzV4sza7U2NuqTqnIp4MfwhUde42BVJF+NqkMLmsRO+5H5H1mZ+Old36iuS3mKpRnGHNj0bsNUhE2bafzz+Of9Ec1K9pomBesjD0mbstfPtpPa1PNTk+YyOpjVvzeS0f108jclBuL9/wQzgMvMMrrVTBUj6QJvZOnIDdIduWurnLoRPpqab2Z1yd8/XWyPt9axaCXgityLW4Np8Qpj2fwh3BwZXz/3WWm0N91B936ya9k+15hxEHVN2p/6qPmc5BqiFgZrhm9n/cB16O5/VdSVVLaWqAi4Kgb4ynlzq4rT8m8eScSyxHHBLWgVUJQzLb4TyTVF+X3irPCzp3fiyd0VaWRqvm9tkWE3Lwa1L7V4wsxMljVdmrDQ3MD2LC+Vi2bkAsIikRdo9wLrwcTw17Y/SZ7uaRsbRQoicApyWqZ5GminyBHedimwXhs2mmVs71D0TJrhO935csFaVcSyoleWyHUmgv0nN+XbOtzbBacU9I+ll3Of/n1adwGummVV5qRT9FKui31p7L6viASRcNfp0+n9x/NrkK5nL00oh94g7yMyeNK8NMBbPTVRWu1aN7QVJj6W+7sRXHLnjw09wldA1SfBWqmPMg+HWMrMv5NvVGlSXV6hbAFdpLtl8iJagoa4am78vPfjG4DU8TmUIDNoMfyN8ibwy/rArrOBdn4xt+mvr9SNpHjOb2W4VYI2Iy5dpWOQXpJHWWXi+jVmqpCSs/4rbCo7El8ffMrNzKJFmWG9Pb/9qpQRr8vJtk3A98hqSlgdON7ONSu3egy+ll8eNrSvjRcYrPRSGQ3p47pBe/8OXrKcN8eFS95yn46qND+Gz0Q/jn6/svTIWuNBquNepRibMrO2i+D34rzb78yyXxar04PIscrjI9d5HmtktaXV3Df6wWhJPSPi7UvsD8fvhLDqk/U6/ie/SmmBw1azN0bg3zXnp/ZbAO3NBKunPuP2rCEz8MB5Dk1c5u6yOWq3bvS8PBrwNV0m1CFZrdimtFUBXMYYrzext6f978ftdFU1b5JNqFF5qiw3R2js3vvAf9pZ4Tdalitcw+ptKG68fMg8C/Ic0Oz/n3nihlIPSazopD05p7CqNvSovyLR0naak95sCx/Q4niNp9m5perU5ZnV8RvzyCF+r4nMVnhvz4hGkVW3PARar0efpdMmBg6sBpuAG//vw2W7bpGaz4Z65Jft/X1wNCB79PaWi/T0Vr5YEXtTIP0TJKyptu2EIbb6NryDfThvvqNSu472Pr3x3TN/LzuVXavOZ9Lt6Pn224nUP7hKdny/31pqE29+G7H2G2yi+iE/8lixedY4dKJUO8KyZnd/H/jp5/eRP643KBw4FSSfjOUueSe+XAH5orf63n8RTMj+f2h2Gz9jySNuXzMyUPDVUqlCV8V8ze1LSGEljzOyy1F8v5EVMvkWHQg/yiOXt8Vn+y3jOl5GkbZHtCl4EpqfZZkuIvBp5kRale8WrY/DEd5elYzfBVY1vTe9/bGb7qk2uJavvpVOXPJf65iTnAfNgxZbGZlbXcFwn/9ATcn/4X+Of9SN4+oecyyTtSMM75YO0qlqKFM55zEzZOwq63PvmevpTJN1m7Wsl/BY4n3o2rtx7qwg43L7UBkmXWGvMQMs26hVeqmTQBP5lcrfGM2n+Id7Y/pCOXKH2Xj8joStbx7KkWeaGr6pQdtHszvgyrcvF0+SeCosn1dTHqbZtPCNpEXw5/Zuk+2zrqVKFNQfZ7GttvBbk4f/z4sJmO0tRxyPMMenB+Q18Br9I+r+Kju51uPtkXRYuhD2AmV1eEjxFMq5e+hwOzySV4UP4BOUTMGsSs2DVAepSCyLxYtI73yl3J/wnvsLO2Ql/AJyV3v8lbcv5NB71Wqh0xgDPyyPPzczGWX0vqY73vhqu0p9Uheuqme1tJRtXOm41PAHfjpYFXnUblzwaeGFg6ZIufxylyl+pv168tJrPlZYIA4G65NoYQn9tvX4kvYAHPwk34BVeDk3Rcz2ebxqut3w6vV8ST+9cTv72eXz5WfyAtgVOMrMfl9ptno/dSukXUpuFcQ+PMbjedDF8ydrRf7zDZ+jkCbWWmVW5Cs5VVBjtWrZJOgvPZppnWZxkZtvOtoFmyN0sf4KrcH5sZiel7f+HpwYpGyYPwAMJ18YzOm6JJ0j7YKldOf/QYnj+oWtH4DMshj84Cv32Fbhht8X9tdO9r85RwmbN3lbL4avRD+GZTL8LnGlm09Pv8FkzO7507r3wYkM/Tu/3wdVoy+MPxELgP4cn3CtX7vtY1eeveNi2MFACfyRQI+fO46XtK3c6zoZWLvFjuK/+79Om7fAoyF9VtF2P5qx7U0r7u6ZfKLVfCv8h3W/J9W4odBH48+N50SfS7M54UFX74ZCM0TdZwyvlm+nc9+FqsxbXtzoGyNSuqyEvzeS+RfYd4WX9ni4d11dHg25IepulEpr5GKw1I+t0PCJ8ipmtKy/Nd5yVqpOVjlkCeMZKQkfdffrnwycbr8VXzrfi9+5LpX7OwG1XeWqOdc0s99PP248rna+7H7v0AzP7YloZ7IR7yZ2WXn/IZ9+SbsZtCOVxzo9HF5fTq+xlNRIcyvMTFSyA20huLD9sK6mj6J/bX/RYQKRGf8J/hE/gusan8ACob7ZpvzLudQC+PF50GJ9lbTwsfy9KRTbIDDhVr1LbjukX8MyKr0v/L4cH4PwR9zXet8cx54VPZmb/NxWowINcTsX19l8oXiN0T9xEqkSGu1v+HXfF+yQ+46s6pqMBkh4MeT2Ms6+OBjXO1zWFQdp2Xfo7GVc9iGbD7zeBtdL/8+MVt57CPXveWeprWrp2G6bvYH1g/ex+vwsX4nvjLrInp22vLfUztWKcVds+jcca3EujclS9ilGpUApu87gCX5UV++4utZ3eoZ+WffgEbtH0/9dx1XOL0bniuMXIqqV1eg2KDr9TAZGhsC+u59zA0kxQngbg55I+Z5kvsDLXTVy1syIesFI2xHRFntHyX7iuedY2a7gt5n7NZZqMOtY9/cIqZnZzar4r8Gcz+5jcjfAq4Md1x231C1OsaGZb1O13mJg1kpu9H//ck4HJkvZoc0w3A2RXQ54aUdjtBlU2xvbb0aAS9ZiRFS/8sTiu+56M35fXZft3wFU44OpF8Oyva+AC++KsbSef/iOBz1hrttd34h45uX783/kKJa2OqmotfBF/WHSsM92G4re1PC6gD0+rm9OoyGIqaVlrre28bJu+v2Fmp0t6Gx5h/AM8I+eb2rQveIFGBuCODITAt5Qy17qkOu6BXnLufBafufwttbtTHsQ0FP5Ewxi8IB6Ycge+1MV6NOaYtU+/QMN7BfzhdGw6Zoakqpw7/eBqSa83s+kj1H+OkjH6BfzzHZXtawmyS3Q0QFpmyFNzJsw8WO0teFqF3+H3RNXDOaffjgbtmA83WM9D88ToOdwjpgkzKx6KR0u6gNZaEC9Zmn7iwusU89w8tyVDcM4f00P2LFp9+lcoC/u07+KSagN8lXBy0uULX1FU6eH/QSPepQW1jwBWepF++z/HJ3kr4m6cj8ljMM4yz4H1feBPkr5Aqj6Hr16+R7UxvlaOHzV7bo3BV0G1cusMhMAvkOcGOQR/6l+A6yD3NbNfdzywlV5y7nRL2FYbazXOrocvT1tQc57+v1prJaJy+oUvWZZ+AXcr3AsXauuRcpvIo4mHVOKwBm8DdpGHjv+HYRi4a/Bj3B/7OTzQ6gYAudfTw22O2RdP2LU3PnvdjAqBkh4GB+Jqg+LhaLhR71W42+NOuKHvT3gJxFvanLOY3eXRmlWuhsPCPBDwCkknWU37kqR1yPTukl5tjbw1/1EqBoLPwr+YHdqU9IzGNfxSPiR8RTpGqcRj6dwLZOf9Mb7qvNrcnjAufabn2gz9q/jk4m80P2CKgKpOK+WWqGEzexAX4D9I9oid0vZfSnocdxMtvHZuBg5os2qrm+Mnf1jMBO5LY+hOHb3PK+VF0ufhhSBOxtUsPQdAUKHTbLcPf5p/DdfFbo7PYlrSzQ7jM1XpV4/CVxq7ptcFwM9KbQ6iTbplPBf6Mrjq6WzcS6PYtykeeTkS38/KVa8RvB9WwGv/jsm2LYcXvBhOv3dRQ8+O67Z3we0/ew3nnH28JmvgMQIX4QViLqUiEA04AY+vOBnPeHoicEK2/03pnn8SV1UU27fCH3B1x/N13J40Mds2EVdrfjO93xNXp92bXr/FV9ZN3212/HV49PiulAKqaoynbOsakt69Td8L4erF1bN7Mf/tvRrYqOK4twOr1TnHQHnpKBUtkHQscIaZXSBpmpVq1dbop0iH0LKL1nQIbV03hzD+XLc6Bp95L2Vm/1dqdwtucLVsDNOtlA5BXdIvpDbbWZayud224dBhCQ3U854Y5vnz1dCVVqoj2qveXe7+u7m1yayZZm7vxmeCE3HhdYKVEpll7d9Na66fvnsupXN1TIqWtbvVzNbu43nb+vSnFdOXaawMngd+YBUeLXI3yY1we8Q2eIbWcvGWq83sreVjK/o6yMy+mb0fA/zKsjKUhfdV0rt/F599f83M3pS1GY9nYJ1Is1dQOWCyaF/krSra3Z+2n5v6vqnUfhK+amjrIVUwUCodXFd4O67S2SN9ET3nJDGzKiNWOxbEf8zHAiTdbp4zpxdy3epMXB1QlUGwa57+pNLZjUYpv1/La4OWf0R18pkPl9rG5n6j1qpln5b0TsuqltG73v1uPNlXSyZMebT063Dj7resYRhvN76jcUG3Ka56+yDNxtF+0zEpWsY1kta2Uk3XMnJ33gOoSHqWtTmACp9+UrI5M/upGtXQsEblriWtYQwX8Hpc0G+U+rqT5oR1BZdJ2g33OmubBwiYIOmrZvbd9JA+nYYuvqCO3v0PeA6si+lQ30HSe/Go3CJv1QR8lVRM1CaWhX0a9w3yCPWuDNQMH2b5Aj9nXgBiIdzY9MgInu9a3A3tX+n9Ingpwa4zjGGc8wo8BW5Tnn7SQ8bM3ivPcf8Wa6RfWBjPDlikXS7ymW9PcwKpcbg76IYjNf7ZSZ3VUHpIF3r3deiid08CrAUz+1YyeBerw/zHV9gryrPRYgZZ/F0ED+x511A+bzdUPyna/8MF5iN0sLeoXtKzrj796eG5TbFqSjP5c83Thf8Zvy+nAtfiaYxnpfqu+Iz3VGw2a42nEF6DYjr+wD3fStk406z7n7jefX18MnldrjVQRcH3NuOahttmLjazN0raFNjJUs1eSXeZ2avbHNt2X85AzfCVRaipOT9I1wi1YbCAZZkQzexf6UFTG7XJp5L1WXbl+2Zlw1K3dE6/ULfKU1/pZmweAbquhsy9Sy4ALkgzvZ3wGfxBVWoFS95gqsiEaWZVRrhOFG6FL8izOj5Jc9rgftPJgJpzAh7YNJ3qSmkFS5rZwdn7QyRtW2rzb/PU3TOTwfWxivOdjXuQfQD/js6hYQi+G39grI5fnyckPW5t3C6tizdbcoYoOAL4BW4UvkLSetbsIbU9HsfyAzN7Jj2I8msHXiilpTpWBd3yVl0v6VOFtiAb7ydo/o22ZaAEPj7TLZgVocbICvzn85tEUjEL6IXCKv9+3MujmC3thBupmrBG6uVOkYQnAn+Th/iDp184Pms7je75zPtKhXpld0mbl9Qr/TpX8RBdjOaqZRtSkW++Qu/+ExrqsHLb1+EpE5ZM75/AC2G388TpxLlyf/fv4/eq0b6ew7DpJgwz7reKtNwV1El61s2nHzM7Vh5xezZ+/T9tZlenfZ+GWff7m3G1zmeTyvZmM9s57d/MzC5Vc4Ws/BzF91kuVfk0riL6IZmHVFoNXmfNeXMeptXLax/ga5L+g3v5VK7m6J63al/gLEkfprnK1ny4I0pXBk6lkyP31/1VxQy5n+fYAC/q8VDatBywQ9kIVrOvljzYbbbthrsN/ptGnu2qJWvH9AupzWwL7a+jXunjuTbutL94aKa2ud79lBp696uB/a05E+Z3hqvGSw+dBawiN0y/SKvPz+OeSrvJ00msaWbnltodhefHKevBzyy1m4EHPhYryDFkKq0KFdZEMp9+NTsqiMaqYkrq4PDs2PnxSV1htH0z8Jgld2ZJB5rZgaqukGW5ETXde9tZl0Ij6lLRrhdUnbfq1xXqtE3JqmyZ2aW1zzHgAn9ePJ/Ka2bDefKyfUOaMcuDOt5tKYukvLbpeeXxS7oT18+3LGnVo0eM3Mj9OVq9NoaUPK0Tks4EPmeN/DYr44Woy5kT+33eZWms/q6zUoWnIejdWzy/qrbVHNsCeBbW3Oj5c+tzAZTsfKfi3/XHzOx18riLa8o66DpCs4dztk0L3M4ekp3wW5J+hAv41XE9/tXFy7LsspK+Yx4QVXdcXQuNSLqUhr2sqaJdSTVUNfauwXOSrrJSUaK0PQ/sK/rr+tAZKJWOhhGhNkw2oOGS9UZJs1zOeuRzuO64SBs8kerAq06RhL16xIx4aH+v6pU+n3t7XF1yOX5NjpT0JTMrEtQNRe9+tzxtRZ4Js8pQWIdf4naTwlawU+p3uyH2143VzGwHSUXw0L9VMnglYfOEmZV11Xmbtczs9nZCz8xuTA+zheiQFtjqRcffgxtXpyR7Szu2IBU+r0mdYuGdxldWDeXUDZ6bUN4gD4g8gOrAvo4M1Ay/tIzvLUJt6OesrG9qWZm0HvubH1grva0s8i2PFj0RdyGsiiTs5XyH4rlURiy0vxf1Sr+Re0ZsXszqk9734qHMxrM+a2XCrDu+fq0Wap7vaty2dZWZrSfP8f47K3llVc3KS/uPSSqhyyp2m5ltpta0wAUzKKUFlnvibGfNxX9OMbP/qzuTTt/1JlRPdqpWt7W8eUYSSfeb2YTStrvwAkc9r7IHaoZf0ssuTWtVnZFgEjXrm7ZDjYIMAO+1LOipzTL1F3iEZEcPCtXziBnx0P7S99JRvTICjCmd40mqw9lrkwT7kB7oFUyR9GZL+eMlvQn3GBkpDsA9klZK+umN8GjgMlPlAWmn0zz7PTP93S393bTi2IKr8RX2B83sSHn++Q/QiJbNGW+txX+KPEZ1Z9Jr4SvcWqtbq2HAlvRmfPX1Gtx4OhZ4PlfzSdoOuMA8D9XX8YDJgwubWTsjchpnVfGZB/CcTT0zEDP89KUciidTOhhfEi+N/7A/ZmYXjOC5Twf2NrfeD7WPWfnVVcq1Xn6ftnWNJFSrR8wOwD9sBDxi6lKhXnk7nuPn952OG+Y5v48vhYvrsCNu1+m5tKJ6z4RZp8/bcPtPoZ+dgBcU+R8jlGdIHiz1Zvw7uLaNLai2Dl/SW2mNMv2lpBvxGJWn5H79p+Bpv98AvMay/O6SJgPvs0bU6cp4krKOs/vSOKaY2Rt7aD8vnpCt0ONfDvwit8FJugG/Z07HJ0Ufw1MjfC1r0zEat821nIWZ7ZraFQbs1+L3REtgX7fPNCgz/J/iurvF8JnvlmZ2raS18B/6iAl8/MHSrb5pN9Tm/6r3UC+ScGOaPWJOxlcEzZ37jPs7wPJmtqWktXGD8PHltn1gfzzldJN6hUbBl75jZl9KM6yN8Gt5dJuVTh16jcitw+xKFw2ApPfhuXP+lN4vLmnb8jUphFCN/ipVmrhtYmx2T+4AHGNmZwBnSJpa6mp/4Ep5UCG4EN6t4nx1yi7W5ed4dG+RSfWjadsn80Zmdpekscl+cGJSi+V0jMatey1pRNrfn17zpRfUTchoc0DCppF+kRVBwDMj5vumjPC5N6569djHjVX/V71P2+6peJWLM5xJlpgMd7tsSWqFuyJuT0oyh08S2hZ2GOa1ml56P2YEz5UXZZlRej2OR2y+o8c+x+IC+mTcbfAQSkU6hjjWJfBVyHrFawTv16kV26ZUbFsRj8Z9DDcenoHXMyi3uw2aE45l+24G5kn/3w78v3xfRful8WI1WwNLV+w/AC+08ihuw3oE+H22f5cer0VLYsXyNtw+Mx/+APse7lhRbnMurmb9B+7KOn+bvpfFY2HOT+/XBj5R0W67OtuqXoMyw8/12OWgpxHVaVl/DI7rSnqOpNNL/5Pet+Rut3rBM0vR7BGzAZ4f5ZzUR7ECWdrMTpP01bR9pjx53EhwgaQLaVavjIiHkHUoypK8UF6He368rl27ij57isitg6SDcR36P2jcq321oZSosl9UyYkTcT174S30kbRt81K7m/FgwSqV5u/w6NUn8N/lXwEkvZpqHfVbaahXwAVpzgdppGjYNa1Ojyt2WqNOb1Xk+rN4ZPkvrOHy+rKk1cwLAiEvclS+9z+KX7M9cWG/Em6HyKkTjQtwEn4N90/v/457CJVX00PObzUoAr8ngdlP6hh1umE1k7Xlxl2VMlpWGHfrpF8AjxReivQDSZ9nRAJ/rL/qleGM42U8yrhnIa0eInJrsj3uKvlS15b94QZJhwM/w7/zvagO2x9vZrnu+SRJ+xZvMqG6KG1Ummb2bUmX4MGIF1maquICdK/8ZMlbbAP8IQywj7zW7lezZnVSNICnYhhPs/3qUTw19LHJXnAVXrXs0sxbZyLQZKMws/vksQrLWYULqepH40KXyZUa+a1WkPST7LhxNEfktqdfS8F4tV0W3oAbR6fgwn5XPOpyJM7Vk+onbR9Hm7q3af96+M3/bPr7d2CdPo+77+qVUfquT8aF4yGkesB96PMMYJnZ+BkWxh0cbkiv7wALV7S7GJ/Vj02vjwCXZPsrVZkMQaWZ+ruJ5roFY3Hjet6miP7dHc+UOQU4saKvv7Tbhtds/gHuQfQ0bos5E591L19x3HvwfEz3pPdvoFRfFn9Ida2xgBuFlyp+q7jh/Ips/7p4rqP7yPL44ylXlqh1HWfXjTSoL+CG9PembNvVI3SuKVX/t3m/GzULOeMrwdfi6o15Z/P1G5tu9Bad7pz2wlWHxYPqOdoUa++xz0m4j/qFeMKwc8oCpc/X+uKabSeksTyOz6TPpqJYDf4AGZP+XwNPxtfzPZQE/pLZ+yXLAr/UfiJtJia4XWGCNX+WW9P/U7Lt8+FqpC/iD96HinZZm8m4M0h+XPlBdGm6By7p9B1Sc3JFsnsM5TUoKp3R5AV50qep8hKLD9Moqt5vrM3/Ve+/RI1CzkmfvRUNt7p3ySOFu7qA9QMbhnpldmO9R+TW4WTgMLpnpRw25inDX5C0mLXJ1yPpMDP7Ch74U8fT7C/A21Og1CX4qmEHPFdML3wXj0m4DFf3/T9KUbPKgsHM7N7ytowv4B4//0h9rYLXx1gYv94FC+Ir4MXS6yFaPdlmmtmzUkeHrDrRwphHH29MIw3LHdbsAnqamW2PX4cW26PVcNEdCD/80ST5Cz+KzxY+h984P7NkCOrzuYpKXEXARpFeoaoS1wXA+82sYyEWSefhRWKaBI71ryB80AFJV5jZxrPxfKfhqoQ/0xxQtXfaPx2fif7NavjAF3Ei8nQAC5rZ91QzP3xFX8vhenyl8z+SthcpGi6jOZJ2HO7x0pIrS42I9SK/1YvZvmPwFe0MXKVT5Nh/OmtzHl5G8ev4g2w/3Fi7N76C2X0In++zwG+sOZp4JzM7qvj8ZvZwkiktWI1axDHDH3m2NbMjcKFZ5EnfB8+z3Vest0pc3Qo5F6xYZ+YQjBiTJX0XVwOMSGqLEn+iNX1xzgV44fuFM0cIK/5aqzOCJL0Fn9F/Im3r5T4tOilm6udUbPs0jRQN+XV5Djc+V7E+jVXrOmrObzUBd528E1enPQg8Uzr+JFzN9itc1fkf3GvpQjy4Mx97XceNT5nZrPGaRxN/ikYcwNGSrsTtC9fbEAz5McMfYVQdCTvFeoj4GwmS18SVtM7cTy61Oww3xl00e0cYAKhDLpoRPOeCuI77jg5t/mBm29To6//hOvCrzOyw5Nq4b8XEot3xtWfvkvayGu6v7YLB8jHJdTSvxXX4b8WF+lN45tADUpuFcW+3LXDBb1lfedrmrtG4qd1NwLqWhHJSp95kKT24pK2z8ayDxy5cRSMz6KPdPnvM8EcIebbBDwGrqDnkfhyzJ4dPN2aa2ee7N+NavOjCGDoXbwhGAOuci6bvSHoP7qUyH37vvgGvQfverM1YatqhzOwvuB6/eH83veUZymfv5cpr5dn7L+S1mtumQkh0zW+V9t0s6RnciPosHvC1IR7gBf57eB5fDSxCh5ge6x6NC746OE1ex9hwb6MLsj7OJcUepO/gjfhD8Pu4HaLryikE/shxNW6gXZrm5E4zKJXQGyXqFnL+IZ4yYHqnH0gwMsiL9BxAQ4hdgQvgkSqCciAu1C4HMLOp8roLs6hj3C2QtAY+w59Icy6duiuUXhKsHUWNVAh0DgYjPTSKYuj/xWfR1+BlHaenNlsAh+MqpvW62MLqOm58BX/AfQafWF1EFjiWzrs0jVn+m/E4oovT+LoSKp0RJi37ioCQNXBD0fkVs47ZPa57KjabtVbFuhDPPTSiHiJBNZLOwAVUoWr7KL7sb5dhcbjn+5uZvSlXOyol/yq162jczdpNA46mtYBOrYpvqpFgTdI85kFKtVJJJzXZG/CiJS35reSBZ1fjaqh2D4W/ArtbjbKVbRw3jjKzu+pcg6yfO/GVxhn4yvt6y+ol1yFm+CNPv9zS+orVr136MJ4e4Hx6zMwX9IXVzCwP1f+WWhOL9ZObJX0IGCsvb7g31UVouhl3C2aa2c+HMZ46Cdauwz2H6qRCAF/FtKWOqtPM3l5v+N2jcQvUvZzoCfhD9gPA64HXSbqG7oVfZhECf+SRmb0gryx/ZHJLmzJqg+kt/QI0kq/lmfmC2ce/Jb3NzK6EWUKhnA+qn+yFR5XmXieHlBuZ2cl1jLvAHyXtgSda66Q6bMfYYgaPF2bJM2QW8qsw5H4RV1XmFeFaMlHaCBbUqaKOXSRxPBXlRAvM7LtZn2vgap1P4RPKx62G+26odEaYJNz3AH6EZ767RdJ0S4WVR2E8PeXWD0YXSevimRgXS5uexrM+TuvzeRbAjYSvxvXUxych2679LCFmZm2FWF3VYYfz7I8H/j2Bu0uuZ2YmT7B2spltJOlBXJ8OHn8yFlczLYCrUw9PfV1pZm+TF1bPBd+IOiLIc/NsBlzeRU32N0s58rv0tyoNG8NbcYP238xs627Hxgx/5NkX93k/Kwn7VXEXs9Gip9z6fTC6BcMgCfZ15cnAMLPnuhwyVE7GDZR/BbbEfcb37dD+QLoYd9P2uqrDSqxegrWxuJdMfv8ukv7OyopqZm9Lf9tmSh0h6kTjgq9Ovk+bcqKSzsJVOs/iRtqrcK3BrXUHEgJ/hEnLxyuy9726pfWbXtIvgPsOH417C4xUWuSghLy60bOWCs0Ugl4esTrWzH7c51OuXaw6JR2P68U7USXEWu4f1aga1Q1L5R1L2/6evX3YzA6q218a11g8/3w+ibm//RG9o0Y0bl27SLdyoifiwVkd06F0IgT+CCHpx2a2r6pzb8/yCBgFek0VPVyjWzA0Po4bIsscA1wP/LjP55slgJPHS7f2dYVYrapRw6SnymLpoXkA7jlTeJ8ZHszUT06iZjQudI+5MLNzwG1vtNbIPcRqRF+HDn+EkLS+mU2WJ0NqYXYbjoaKvBTbYwzd6BYMgU52npGwAamRhwmaczFV6rclLYQbd9+VNl2IC50XS+1quUoOc+xL9nI/SroLT/424gGQqhGNK+kjZvZrNWrWNlH2iFOXGrmdiBn+CFH4GZvZFfLarJjZ46M7qiGxc/qbV+gxqgtLBH1E0rJWCpeXV3HqO1a/yE7ZuPuWTsZd6rtKDpkhTD4eYISK+FRQJxq3CMKqa1voWCO3EzHDHyHka+ID8NJnwo1MM3EjS0/6xmDwkPQxXE3yBRoJwdbH66b+zEo5j2bjuE6l2bh7r5nt26H9O3Dd893472BlYFczGzXHhWSjWBOPIxix2BI1R+MeZF0y0/bQ77l4Urd34vfEv/GqWl1XTSHwRwhJn8PdyXYzs3vStlVx/eUFZvaj0RxfXfphdAuGhryk3X406ureDBxqZiNS57fmmGapkyTNgwuajq688lTERY73283sP53ajzSSDqja3ikoaojnqRWNK8+IebmZ3ZkmisfjwVX3ATub2ZRS+4VwFdH0dMxywOutRoLDEPgjRPK/37xsUU/qnYtslLNl1kXScbjRLQ/tf9nM+ml0C+YS6sZuSNrMzC6V1yhuwcyGU+f3FYWkm4E3mtl/kyH8C7ht5I3AAZZF9cqTGN5kWY3cXggd/sgxb5X7lJk9nmbNcwsblJaKl8rzowSzgeTbvhetcRCj7eUFzZ5eZePuxnhpv/dU9GEMr7D7kJiDPedmZivmrYFfJoPyxfJka7Mwz8k1TdKEobiRhsAfOToVJ+i5cMEoMuJGt6AjZ+NL/D8ywiUO61DXuGspZzyuu26Ktq0K0JpN/Cr9/cEonb8d/0tqmafx9BHfzvYtWNF+OeAWeU2LPHFd1wdWCPyRI58J5bTzd59TyfOTzDK6je6QBooXzewnoz2IYXAGrfEEv8eNjbOV3HNudp+7C9/EkyqOxYub3wKQXLrvrmg/ZFtDCPwRou5MaE4mRSOuC6zOHGR0GzCOSEbGi5g9JQ77gqS18IpRi5X0+OMY5QlPChT7LrB2Pharmd+n35jZufIUyotaVjcXD7DboaL9kB9YIfCDtpgXunhv8iiaE4q2DCKvxw3lm9EcFTqn5zJaE9dHL06zHn8GnuFxNDkRd5n+EbApvmLtKVq336To5ndKKkfQHgyUvXTq1shtIbx0go5I+jaeqfFUmvWFc/QM85WCpNuBdWwIBavnBCS9xcxqVWOaXUiabGbrl1xM/2o95LgfoXHViqBVzRq5VcQMP+jGW9PfPFhsbphhvlKYhs+SHxvlcQyVKZI+i6t3cvXJx0dvSLyY3BvvlLQnHsS0zCiOp6B2BK3Vq5HbQgj8oCPdEjoFI86ywO2SrqeiHN9cwK+A24H/wycNHwZuG9URedrnhfBI5oNxtc7OnQ6YTfxT0i/wCNrDUsDamIp2dWvkthAqnaCSXhM6BSPDKyD53hQze2OmrpgXuNBGqZ5CckQ41My+1LXxbKZuBK2GUSM3ZvhBO3pN6BSMAHOLYO9AEVD0jKTXAY/gQWSzHTWKna8vSTYHzXaTium6PILWvIB6SxF1q1kjt4oQ+EElZvaL9O9Rc2mWz1cEai7HNx+e5qKWR8YcwjGSlgC+gScRWwT3Ox8NimLnU4A/SDqdZkeEUUv30EsErerXyG0hBH7QjavldUlPBc4s+QkHI4yVyvFJ2hYvLThXYGbHpX+vYM5Jqb0k8CTueGCktBCMQrqHEnUjaA+ktbzkxDonCIEfdMTMVpe0Ie4Gtr+kW4FTzOzXozy0gcTMzpa032iPoxvtbD8Fo2QDWiaN62Yagn7WkEZhPGXqqmfq1shtIQR+0BUzuw64TtJ38PzeJwMh8GcDpSjVMbjf9ZwgnLoxJ9p+qoqdF4z6Ne1mr1HvNXJb+5iD7BbBHIikccD78Bn+anipw9OKvCTByCLpxOztTOBe4Fgzm1v98keNdqmc5xS6RdBK2h44BHd1XRDYPB16IXBwnZQnIfCDjiT9/dm4kJ+jIiaDOR9Ja+BFf5Y1s9dJWgd4r5kdMgpjmTIn16GoE0GrGjVyOxEqnaAbq85J7muDgqROnixmZgfPtsEMj2Pxesi/ADCzmyT9Fp+pzm7eMQrn7IkaEbR1auS2JQR+0I3VJX2R1gIckVphZHm+YtvCwCeApfAI0bmBhczsupKBsVPR8xHDei92PrvpGEGr5hq569kQauSGwA+6cTpwNHAcUfhktmFmPyz+l7QosA+e1fEU4IftjpsDeULSaqSZqKQPUhFMFACeFXUMsCceQbsSXtu2YH9gO+tSI7cTocMPOlJkFhztcQwikpYEPo/nnzkZOGJui4NIFdKOwZPwPQ3cA3zYzO4b1YHNoaQI2glmdsdI9F+VmCcIcv4oaQ9Jy0lasniN9qBe6Uj6Pl4AYwaeT+XAuU3YA5jZ3Wb2TmA8sBawCfC2UR3UHEqKoJ0KXJDev0HSOX09R8zwg04kL50yNlrVgQYFSf/Ds2POpNkwVy4WPkeS3Hk/C6wA/AG4OL3/IjDNzLYZxeHNkUiajEf/Xl54ExVJ5/p1jtDhBx0xs9EqOD3QmNncvvr+Fa7CuQavcPVl3Ld8WzObOorjmpMZcgRtXeb2myoYISR9Oft/u9K+78z+EQVzGaua2S4pCd9OuF/51iHsW5F0nqRVKEXQSjqSmhG0dQmBH7Rjx+z/r5b2bTE7BxLMlRRpkUk+5feY2YxRHM+czEl4tOy9wOtwVd5vgWdx76y+EQI/aIfa/F/1PgjKrCvpufSaAaxT/C/pudEe3JyEmZ0GvBEPpHo3npn2FFwl9tl+nit0+EE7rM3/Ve+DoAkzGzvaY5jLGFYEbV1C4AftWDfNxAQsmM3KRFaMOgiC4dGPCNra5wq3zCAIgtFD0l+B3YcTQVv7XCHwgyAIBoMw2gZBEAwIIfCDIAgGhBD4QRAEA0II/CAIggEhBH4QBMGAEAI/CIJgQPj/nTEM+5dsgdMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df=pd.DataFrame(characteristic_dict,index=[0])\n",
+    "df.T.plot(kind=\"bar\",legend=False,title=\"characteristic Importance\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0c1f23a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "From above graph, We can clearly infer that job level, overtime and PerformanceRating are the main reasons for employee \n",
+    "attrition. So company should try to improve these areas so as to reduce attrition."
+   ]
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Minor Project Submissions/SOLUTION.md b/Minor Project Submissions/SOLUTION.md
index 978c4b2..39db0b9 100644
--- a/Minor Project Submissions/SOLUTION.md	
+++ b/Minor Project Submissions/SOLUTION.md	
@@ -1,2 +1,11 @@
 # SOLUTION
 
+1.
+Quantitative variables=Data with numerical variables are considered quantitative data (e.g. how many; how much; or how often)
+Example-In honey production dataset numcol, yieldpercol, totalprod, stocks, priceperlb, prodvalue are quantitative variables.
+
+Qualitative variables=Qualitative data are data about categorical variables (e.g. what type). 
+They may be represented by a name, symbol, or a number code.
+Example-In honey production dataset, state is the qualitative variables.
+
+3.There is no state named as "US" in the dataset
diff --git a/Minor Project Submissions/Saloni Choudhary_Honeyproduction.ipynb b/Minor Project Submissions/Saloni Choudhary_Honeyproduction.ipynb
new file mode 100644
index 0000000..6cb9b50
--- /dev/null
+++ b/Minor Project Submissions/Saloni Choudhary_Honeyproduction.ipynb	
@@ -0,0 +1,2151 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "bab015b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np \n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats\n",
+    "from scipy.stats import pearsonr\n",
+    "import seaborn as sns\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "30976dd5",
+   "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>state</th>\n",
+       "      <th>numcol</th>\n",
+       "      <th>yieldpercol</th>\n",
+       "      <th>totalprod</th>\n",
+       "      <th>stocks</th>\n",
+       "      <th>priceperlb</th>\n",
+       "      <th>prodvalue</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>16000.0</td>\n",
+       "      <td>71</td>\n",
+       "      <td>1136000.0</td>\n",
+       "      <td>159000.0</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>818000.0</td>\n",
+       "      <td>1998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AZ</td>\n",
+       "      <td>55000.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>3300000.0</td>\n",
+       "      <td>1485000.0</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>2112000.0</td>\n",
+       "      <td>1998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AR</td>\n",
+       "      <td>53000.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>3445000.0</td>\n",
+       "      <td>1688000.0</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>2033000.0</td>\n",
+       "      <td>1998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CA</td>\n",
+       "      <td>450000.0</td>\n",
+       "      <td>83</td>\n",
+       "      <td>37350000.0</td>\n",
+       "      <td>12326000.0</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>23157000.0</td>\n",
+       "      <td>1998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CO</td>\n",
+       "      <td>27000.0</td>\n",
+       "      <td>72</td>\n",
+       "      <td>1944000.0</td>\n",
+       "      <td>1594000.0</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1361000.0</td>\n",
+       "      <td>1998</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  state    numcol  yieldpercol   totalprod      stocks  priceperlb  \\\n",
+       "0    AL   16000.0           71   1136000.0    159000.0        0.72   \n",
+       "1    AZ   55000.0           60   3300000.0   1485000.0        0.64   \n",
+       "2    AR   53000.0           65   3445000.0   1688000.0        0.59   \n",
+       "3    CA  450000.0           83  37350000.0  12326000.0        0.62   \n",
+       "4    CO   27000.0           72   1944000.0   1594000.0        0.70   \n",
+       "\n",
+       "    prodvalue  year  \n",
+       "0    818000.0  1998  \n",
+       "1   2112000.0  1998  \n",
+       "2   2033000.0  1998  \n",
+       "3  23157000.0  1998  \n",
+       "4   1361000.0  1998  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('honeyproduction.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9fbbbf8e",
+   "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>state</th>\n",
+       "      <th>numcol</th>\n",
+       "      <th>yieldpercol</th>\n",
+       "      <th>totalprod</th>\n",
+       "      <th>stocks</th>\n",
+       "      <th>priceperlb</th>\n",
+       "      <th>prodvalue</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>621</th>\n",
+       "      <td>VA</td>\n",
+       "      <td>4000.0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>164000.0</td>\n",
+       "      <td>23000.0</td>\n",
+       "      <td>3.77</td>\n",
+       "      <td>618000.0</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>622</th>\n",
+       "      <td>WA</td>\n",
+       "      <td>62000.0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2542000.0</td>\n",
+       "      <td>1017000.0</td>\n",
+       "      <td>2.38</td>\n",
+       "      <td>6050000.0</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>623</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>6000.0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>288000.0</td>\n",
+       "      <td>95000.0</td>\n",
+       "      <td>2.91</td>\n",
+       "      <td>838000.0</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>624</th>\n",
+       "      <td>WI</td>\n",
+       "      <td>60000.0</td>\n",
+       "      <td>69</td>\n",
+       "      <td>4140000.0</td>\n",
+       "      <td>1863000.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>8487000.0</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>WY</td>\n",
+       "      <td>50000.0</td>\n",
+       "      <td>51</td>\n",
+       "      <td>2550000.0</td>\n",
+       "      <td>459000.0</td>\n",
+       "      <td>1.87</td>\n",
+       "      <td>4769000.0</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    state   numcol  yieldpercol  totalprod     stocks  priceperlb  prodvalue  \\\n",
+       "621    VA   4000.0           41   164000.0    23000.0        3.77   618000.0   \n",
+       "622    WA  62000.0           41  2542000.0  1017000.0        2.38  6050000.0   \n",
+       "623    WV   6000.0           48   288000.0    95000.0        2.91   838000.0   \n",
+       "624    WI  60000.0           69  4140000.0  1863000.0        2.05  8487000.0   \n",
+       "625    WY  50000.0           51  2550000.0   459000.0        1.87  4769000.0   \n",
+       "\n",
+       "     year  \n",
+       "621  2012  \n",
+       "622  2012  \n",
+       "623  2012  \n",
+       "624  2012  \n",
+       "625  2012  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b17be995",
+   "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>numcol</th>\n",
+       "      <th>yieldpercol</th>\n",
+       "      <th>totalprod</th>\n",
+       "      <th>stocks</th>\n",
+       "      <th>priceperlb</th>\n",
+       "      <th>prodvalue</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>626.000000</td>\n",
+       "      <td>626.000000</td>\n",
+       "      <td>6.260000e+02</td>\n",
+       "      <td>6.260000e+02</td>\n",
+       "      <td>626.000000</td>\n",
+       "      <td>6.260000e+02</td>\n",
+       "      <td>626.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>60284.345048</td>\n",
+       "      <td>62.009585</td>\n",
+       "      <td>4.169086e+06</td>\n",
+       "      <td>1.318859e+06</td>\n",
+       "      <td>1.409569</td>\n",
+       "      <td>4.715741e+06</td>\n",
+       "      <td>2004.864217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>91077.087231</td>\n",
+       "      <td>19.458754</td>\n",
+       "      <td>6.883847e+06</td>\n",
+       "      <td>2.272964e+06</td>\n",
+       "      <td>0.638599</td>\n",
+       "      <td>7.976110e+06</td>\n",
+       "      <td>4.317306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>8.400000e+04</td>\n",
+       "      <td>8.000000e+03</td>\n",
+       "      <td>0.490000</td>\n",
+       "      <td>1.620000e+05</td>\n",
+       "      <td>1998.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>9000.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>4.750000e+05</td>\n",
+       "      <td>1.430000e+05</td>\n",
+       "      <td>0.932500</td>\n",
+       "      <td>7.592500e+05</td>\n",
+       "      <td>2001.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>26000.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>1.533000e+06</td>\n",
+       "      <td>4.395000e+05</td>\n",
+       "      <td>1.360000</td>\n",
+       "      <td>1.841500e+06</td>\n",
+       "      <td>2005.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>63750.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>4.175250e+06</td>\n",
+       "      <td>1.489500e+06</td>\n",
+       "      <td>1.680000</td>\n",
+       "      <td>4.703250e+06</td>\n",
+       "      <td>2009.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>510000.000000</td>\n",
+       "      <td>136.000000</td>\n",
+       "      <td>4.641000e+07</td>\n",
+       "      <td>1.380000e+07</td>\n",
+       "      <td>4.150000</td>\n",
+       "      <td>6.961500e+07</td>\n",
+       "      <td>2012.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              numcol  yieldpercol     totalprod        stocks  priceperlb  \\\n",
+       "count     626.000000   626.000000  6.260000e+02  6.260000e+02  626.000000   \n",
+       "mean    60284.345048    62.009585  4.169086e+06  1.318859e+06    1.409569   \n",
+       "std     91077.087231    19.458754  6.883847e+06  2.272964e+06    0.638599   \n",
+       "min      2000.000000    19.000000  8.400000e+04  8.000000e+03    0.490000   \n",
+       "25%      9000.000000    48.000000  4.750000e+05  1.430000e+05    0.932500   \n",
+       "50%     26000.000000    60.000000  1.533000e+06  4.395000e+05    1.360000   \n",
+       "75%     63750.000000    74.000000  4.175250e+06  1.489500e+06    1.680000   \n",
+       "max    510000.000000   136.000000  4.641000e+07  1.380000e+07    4.150000   \n",
+       "\n",
+       "          prodvalue         year  \n",
+       "count  6.260000e+02   626.000000  \n",
+       "mean   4.715741e+06  2004.864217  \n",
+       "std    7.976110e+06     4.317306  \n",
+       "min    1.620000e+05  1998.000000  \n",
+       "25%    7.592500e+05  2001.000000  \n",
+       "50%    1.841500e+06  2005.000000  \n",
+       "75%    4.703250e+06  2009.000000  \n",
+       "max    6.961500e+07  2012.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "30ea68c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state          0\n",
+       "numcol         0\n",
+       "yieldpercol    0\n",
+       "totalprod      0\n",
+       "stocks         0\n",
+       "priceperlb     0\n",
+       "prodvalue      0\n",
+       "year           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1f8a2786",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f3c9669d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(626, 8)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76bb0890",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state           object\n",
+       "numcol         float64\n",
+       "yieldpercol      int64\n",
+       "totalprod      float64\n",
+       "stocks         float64\n",
+       "priceperlb     float64\n",
+       "prodvalue      float64\n",
+       "year             int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1.To explore quantitative variables and qualitative variables in the data set.\n",
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5541278a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# From above we can clearly see that state is the only qualitative data , year is a categorical data so we will convert it \n",
+    "# to numerical data and the rest is all quantitative data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "15e9813d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      AL\n",
+       "1      AZ\n",
+       "2      AR\n",
+       "3      CA\n",
+       "4      CO\n",
+       "       ..\n",
+       "621    VA\n",
+       "622    WA\n",
+       "623    WV\n",
+       "624    WI\n",
+       "625    WY\n",
+       "Name: state, Length: 626, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"state\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5c8ca9de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       16000.0\n",
+       "1       55000.0\n",
+       "2       53000.0\n",
+       "3      450000.0\n",
+       "4       27000.0\n",
+       "         ...   \n",
+       "621      4000.0\n",
+       "622     62000.0\n",
+       "623      6000.0\n",
+       "624     60000.0\n",
+       "625     50000.0\n",
+       "Name: numcol, Length: 626, dtype: float64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['numcol']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a25f6636",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.year = data.year.astype('category')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ed5fc9e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 626 entries, 0 to 625\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column       Non-Null Count  Dtype   \n",
+      "---  ------       --------------  -----   \n",
+      " 0   state        626 non-null    object  \n",
+      " 1   numcol       626 non-null    float64 \n",
+      " 2   yieldpercol  626 non-null    int64   \n",
+      " 3   totalprod    626 non-null    float64 \n",
+      " 4   stocks       626 non-null    float64 \n",
+      " 5   priceperlb   626 non-null    float64 \n",
+      " 6   prodvalue    626 non-null    float64 \n",
+      " 7   year         626 non-null    category\n",
+      "dtypes: category(1), float64(5), int64(1), object(1)\n",
+      "memory usage: 35.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1fd8fdbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 42 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2.To find the relationship between numerical variables using pair plots and corelation plots.\n",
+    "sns.pairplot(data);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b692caeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numcol         0.953594\n",
+       "yieldpercol    0.396252\n",
+       "totalprod      1.000000\n",
+       "stocks         0.878830\n",
+       "priceperlb    -0.264499\n",
+       "prodvalue      0.907236\n",
+       "Name: totalprod, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correlation_matrix=data.corr()\n",
+    "correlation_matrix['totalprod']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ed7ee04c",
+   "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>numcol</th>\n",
+       "      <th>yieldpercol</th>\n",
+       "      <th>totalprod</th>\n",
+       "      <th>stocks</th>\n",
+       "      <th>priceperlb</th>\n",
+       "      <th>prodvalue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>numcol</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>-0.23</td>\n",
+       "      <td>0.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yieldpercol</th>\n",
+       "      <td>0.24</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>-0.36</td>\n",
+       "      <td>0.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>totalprod</th>\n",
+       "      <td>0.95</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>-0.26</td>\n",
+       "      <td>0.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>stocks</th>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>-0.31</td>\n",
+       "      <td>0.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>priceperlb</th>\n",
+       "      <td>-0.23</td>\n",
+       "      <td>-0.36</td>\n",
+       "      <td>-0.26</td>\n",
+       "      <td>-0.31</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>-0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prodvalue</th>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>0.73</td>\n",
+       "      <td>-0.09</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             numcol  yieldpercol  totalprod  stocks  priceperlb  prodvalue\n",
+       "numcol         1.00         0.24       0.95    0.83       -0.23       0.91\n",
+       "yieldpercol    0.24         1.00       0.40    0.37       -0.36       0.28\n",
+       "totalprod      0.95         0.40       1.00    0.88       -0.26       0.91\n",
+       "stocks         0.83         0.37       0.88    1.00       -0.31       0.73\n",
+       "priceperlb    -0.23        -0.36      -0.26   -0.31        1.00      -0.09\n",
+       "prodvalue      0.91         0.28       0.91    0.73       -0.09       1.00"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correlation=data.corr().round(2)\n",
+    "correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "192f7ab6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Relation between numerical variables')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,5))\n",
+    "plot=sns.heatmap(correlation.round(2),annot=True)\n",
+    "plot.set_title(\"Relation between numerical variables\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2e788b00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#       OBSERVATIONS\n",
+    "# From above we can clearly infer that \n",
+    "# 1)Number of colonies has a high positive correlation with total production, stocks and production value.\n",
+    "# 2)Price per lb has a high negative correlation with number of colonies, stock, yield per colony and total production \n",
+    "#   value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a8e9e9c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#        CONCLUSION\n",
+    "# Increase in number of colonies will increase production value which leads to an increase in stocks and total production."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2a9a1206",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 3. Explore the overall trend of honey production in the US over the years.\n",
+    "\"US\" in data[\"state\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "49fd7afe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='totalprod', ylabel='prodvalue'>"
+      ]
+     },
+     "execution_count": 35,
+     "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": [
+    "# 4. To observe the pattern of total honey production ans value of production every year.\n",
+    "sns.regplot(x=data[\"totalprod\"], y=data[\"prodvalue\"],color=\"orange\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfa02d0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OBSERVATIONS:Most of the values of total production and production value lies in th range of 0 to 1 as most of the dots\n",
+    "# are accumulated in that region.\n",
+    "\n",
+    "# CONCLUSION: Above plot shows that total production decreases with increase in production value ie they are inversely \n",
+    "#related."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "16d5b339",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
+       " [Text(0, 0, '1998'),\n",
+       "  Text(1, 0, '1999'),\n",
+       "  Text(2, 0, '2000'),\n",
+       "  Text(3, 0, '2001'),\n",
+       "  Text(4, 0, '2002'),\n",
+       "  Text(5, 0, '2003'),\n",
+       "  Text(6, 0, '2004'),\n",
+       "  Text(7, 0, '2005'),\n",
+       "  Text(8, 0, '2006'),\n",
+       "  Text(9, 0, '2007'),\n",
+       "  Text(10, 0, '2008'),\n",
+       "  Text(11, 0, '2009'),\n",
+       "  Text(12, 0, '2010'),\n",
+       "  Text(13, 0, '2011'),\n",
+       "  Text(14, 0, '2012')])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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cTfcX6fPARCKmorqe55eXUFxSQUZqIu8b24dZw7RsWWKj/EgdxSXlAEwZlE9Gij5eI01/YfGl5dvLuO6Pb1FeXR+87bFF2zl3VE/uvmqyes9J1C3YdBDnvOsaPowODSGK7xyubeCGh5e2Kl7N/rtuH7946d0YZCWnupbzX2dpAUdUqICJ7/x11S4OHm5/a7mn3tpBdWAlmEg0eO2jvN008jOSGdM3J8YZnRpUwMR31ga2qWhPVW0D20uPRCkbEdh28AglZdUAzBpeSEKC5mGjQQVMfCczteOp26w0Te9K9Mzb2HL4UPNf0aICJr5z8fg+J7x/0sA8+uWlRykbEZi3/uhmvLM1/xU1+poqvpObnkxKolHX6I67zwy+cdHpMchKYsU5x4od5cE9uOaMKGTigLyonU7R0NjEwk1e+6ihhZn68hRFKmDiKzX1jdz8+LJg8UpLTqCmvil4f6IZA/IzYpWeRFllTT23PLacN1sM4f3qlfXMGVHI3VdNJjstOeI5rCqp4FBtA6Ddl6NNQ4jiK9/72xre3ukt4hjfP5dl/3se8742ly+fPxKAhibHA/M2xzJFiaIvP72qVfFqNm/DAb76THFUcmhefQgaPow2FTDxjaeX7uDJJTsAyMtI5u6rJpOZmsyAggyunzOE/Azv2/YTi7dTeoJl9tI9bNpfxStr97Z7/7/W7GHrgcMRz6N56DIxwZgxVO2jokkFTHzh7Z0VfOuFtwFvnuu3H59E/xZDhRkpSVx35hAAqusbeWj+lpjkKdGzbFtZWB7TFZU19azYUQ7A5IF5URmylKNUwCTuVRyp5+bHl1Hb4M11feE9Izl75PFDNZ+YOZiswBL7hxZs5VDN8Z06pPtITer446uusanDx3TFok0HaWzy5mNnD9fwYbSpgElca2pyfOnplewo9U4SPee0Im49d3ibj83NSObqGYMAqKxp4PHF26OWp0TfnBFFpHRQxH76z3U8vnhbsMiEW8v5N/U/jD4VMIlrd7+2kf+s2wdA//x0fvOxiSfscnD97CHBb+a/n7eFmnq1lOquCjJTuHHO0BM+pqK6nm8+/zYfvGs+KwNDfeHU3P8wOy2JCf1zw/76cmIqYBK35m3Yzy9fWQ9ASlIC91w1hbyMlBM+pyg7lY9NGwDAgapanllWEvE8JXa+dP5I3j+u9YntuenJ3HbecD49ZwiJgS87q3dWcNnd8/mf54rDtsCnpOwIWwKLRGYN60FSoj5Oo01/cYlLu8qrue2plcHtKb5/yRjGhfgN94Y5Q4MfXPe9von6CM+DSOwkJFhw3hPg5x8Zz+JvvIcvnnca//v+0fzztjnBlYHOeY2e5/7iNR5b1PVhxTdbdJ+fo+XzMaECJnGntqGRmx9fHvymfPnU/nx8+sCQnz+gIINLJ/YFoKSsmr+t2hWRPCU+zN/kFZKCzBQ+PLl/q73gRvbK5skbZvC7KybRKycV8IYV//eFt7n0rjdZsf3kVynOa1XANP8VCypgEnd++OI7rArMV4zuk8P3Lx3b6de45ZxhNHcSuvu1TTRFaBJfYmt7iy7wM4f2aHN+1My4ZEJf/vPlc7jxrKEkBR7z9s5KLrt7AV9/tpiDVbWditvY5IKFc0BBOoN6ZHbxN5GToQImceX5FSU8umgbADlpSdx79ZST2l15eM9s3ju6NwAb91Xx8glOeBX/ai4iALOG9zjhY7NSk/jGRafzz9vmMHPo0cf+aekOzv3l6zzaiWHFNbsqKD/inaah4cPYUQGTuLFuTyW3/3l18Odff2wiA3ucfF/DW+YOC16/+7WNOKejsO5mQaCJLsCZw0IbxhvRK5snbjiDO44ZVvxWYFhxeQjDiq2GD4dr+DBWVMAkLlTW1HPzY8uDjXk/N3c47zm9V5dec3z/vODcRHFJRZs988S/nHMsDByB9ctLZ1AnvuyYGR8IDCvedMyw4ofuXsDXnl11wmHF5v6HCQazQiycEn4qYBJzzjm++syq4JLk2cML+WKgOW9XfXbu0ZOe73p1Y1heU+LDu3sPcaDKW+gzc1iPk9o+JSs1idsvOp1/fWEOs4YdHVZ8emkJc3/xGo8u3HrcsOLh2oZgi6rx/fPIzVD7qFhRAZOYu/+Nzby0xpuj6pubxm8/PjG4DL6rzhhSwJRB+QAs2lzKsm2lYXldib35G1sMH3Yw/9WR4T2zefzTZ3DnlZPonZMGeN1cvvWXNVxy55ss21bGpv1V3P7nYs76v1epD2znc3qf7C7Fla5RAZOYWrjpID/71zoAkhONu66aTI+s1LC9vpnx2ZZzYa9uCttrS2wtbLmAIwzDeGbGxeP78p8vn81NZx8dVlyzq5IP37OAC379Bk8u2cHBFidCP7OshNdb7MYs0aUCJjGzt7KGW59cQfMIzbcvHs2kgflhjzP3tJ6M6u19U/7Pun28s7sy7DEkuhoam1i82TuaHt4zi16Bo6ZwyExN4vb3ecOKLY/s2lqh2NDo+Mozq6hr0MnysaACJjFR39jEZx9fzoHARPllk/oFG/GGm3cUdnQu7J7XdBTmd8U7j+6C3HLuKpyG98zmsevP4IvnnXg+dv+hWh2FxYgKmMTET/6xjqWBifDTemXzo8vGntQkfKguGteHwYFVai8W74rKRocSOQs2hnf4sD1mxuDCjlc37q6ojlgO0j4VMIm6F4t38WBgw8ns1CTuvWYKGSlJHTyraxITjM+c7c2FNTm47w0dhflZ8/lfCUark5IjoX9+elgeI+EXcgEzs0Fmdl7gerqZafmNdNrGfYf42rPFwZ9//tEJDCmMThueyyb3C64we3ZZCXsqaqISV8Krpr4xePQ+tl9uxJexTx6Yz4ieWe3e3yc3jbPUjSMmQipgZnYD8CxwX+Cm/sALEcpJuqmq2gZuenQZR+q8PbpuOmsoF47tHbX4qUmJ3HCWt39UfaPjgXmboxZbwmfZtrLgoolonERsZvzq8onkpB0/SpCRksivPzZRW6nESKh/9c8CZwKVAM65DUDPSCUl3Y9zjv95rphN+725pzOGFPDV954W9TyumD6A/MA39icWbw/b3lASPfNbzX9Fdviw2bj+ufzjtjlcP3sIw3tmMbQok2tmDOLvn5/DjAgPYUr7Qp14qHXO1TVPsptZEqDGchKyP87fyovFuwHomZ3KHVdOism31oyUJK47cwi/fGU91fWNPDR/C1+6IPqFVE5e8/xXSmIC0wYXRC1u//wMvnXxaL4VtYjSkVA/QV43s28A6WZ2PvAM8LfIpSV+55zjSF0D9Y1NLN1ayo//8Q4ASQnG3VdNpmd2+M7b6axPzBwc3ATxoQVbOVRTH7NcpHMqa+opLikHYNLAPNJTOr9TgXQfoR6B/Q9wPbAauAn4B/D7SCUl/tXU5Hh88Tb+OH8rmw8cJikBEhMSaAicBHr7RaczNYrfmtuSm5HM1TMGce/rm6isaeDxxduDKxQlvi3eXBo88f1MdYE/5YV0BOaca3LOPeCc+6hz7iOB6xpClON8569r+NZf1rA5cJ5VQxPUBibczx5ZxHVnDo5hdkddP3sIqUne2//387ZQU98Y44wkFLGY/5L4FeoqxC1mtvnYS6STE39ZXVIR3IyyLU2uKaInK3dGUXYqH5s2AIADVbU8s6wkxhlJKBYG5r8yUxKZMCAvtslIzIU6BzYVmBa4zAF+BzwWqaTEn15YufOE97+58SDlR+Jn1V/L7eXve30T9Y3qZxfP9h+q5d29hwCYPqSAZC1dP+WFOoR4sMVlp3PuN8C5kU1N/Kasg+LknLfzbbzon5/BJRP7AlBSVs3fVu2KcUZyIgtadJ/X/JdA6EOIk1tcpprZZwB14pBWRvY68VsiKzUprF3Dw+GWc4bRPKp592ubaGqj47jEhwUt9v+aqfkvIfQhxF+2uPwEmAJcHqmkxJ8+PLk/6cntL2v+2LQBpJ3g/lgY3jOb9472uoFs3FfFy2v3xjgjac+Czd4RWEFmCqf3zolxNhIPQh1CnNvicr5z7gbn3LuRTk78pSg7lTuumERyG7spzxrWg6/E6QnDt7Tc8PK1jWiBbfzZUXqEHaVex/eZQ3uQEKYdu8XfTngemJl96UT3O+d+Fd50xO/OG92L6+cM4d7XvUWq0wbnc92ZQzh/dK+47Rc3vn8ec0YUMm/DAYpLKnhz4wHmqDlrXGm1fH64hg/F09EnSnYHF5HjbC89Erx+91VTeN+4PnFbvJq13PDyrlc3xjATacv8TUfnv6LRwFf84YRHYM6570UrEek+iksqAG+biaLs1BhnE5ozhhQwZVA+y7aVsWhzKcu2lTFlUH6s0xK8tmQLAysQ++amBTcmFQl1FWKamX3WzO42swebLyE8b6uZrTazlWa2tOvpSrwrO1xHSZk3VzGuX26MswmdmfHZFnNh97ymo7B4sX5vFQeqvFM0Zg0vjJuT4SX2Qh3XeRToDbwXeB1vP7BDIT53rnNuonNu6knkJz6zemdF8Pr4/v4pYABzT+vJqN7eyPi/39nHO7srY5yRQOv5rzM1/yUthFrAhjvnvgUcds49DLwfGBe5tMSvWhawcf3zYpfISfCOwo7Ohd3z2qYYZiPNWp7ArPkvaSnUAtbcPqHczMYCucDgEJ7ngJfNbJmZ3djWA8zsRjNbamZL9+/fH2I6Eq+at7oAfw0hNrtoXJ/gHMuLxbvYGmhKLLHR0NjE4s2lAAwryoy7E+EltkItYPebWT7wLeCvwFrgZyE870zn3GTgfcBnzeysYx/gnLvfOTfVOTe1qEhLl/3u7Z3esFu/vHQKMlNinE3nJSZYcGuVJgf3vaGjsFhavbOCQ7UNgNpHyfFCLWB/dM6VOeded84Ndc71dM7d19GTnHO7Av/dBzwPTO9CrhLnDlTVsrPcW8Dht/mvli6b3I/egW/6zy4rYU9FTYwzOnUt0PJ5OYFQC9gWM7vfzN5jIS4BMrNMM8tuvg5cALx9knmKD7Se//JvAUtNSuSGs4YCUN/oeGCedg6KleYFHGYwY2hsN0KV+BNqATsN+DfwWWCrmd1pZrM7eE4v4E0zWwUsAf7unPvXyacq8W51SYsC5sP5r5aumD6A/IxkAJ5YvJ3Sw/GzDcypoqa+kaXbygAY2zeXvAz/DUlLZIXaC7HaOfe0c+5DwEQgB285/Ymes9k5NyFwGeOc+1HX05V41uoIzOcFLCMlievOHAJAdX0jD83fEuOMTj3Lt5VRF9jNW+2jpC0h9/cxs7PN7G5gOZCGutHLMZqPwAYWZHSLb8ufmDmYrFSvWc1DC7ZyqCZ+9jI7Fcxvuf+X5r+kDSdsJdXMzLYAK4Gnga8657S2WFrZV1nDnkpvsYOf579ays1I5uoZg7j39U1U1jQw+QevkJOWzAVjenPLOcMYUKCWRpE0P7D/V3KiMW2w5r/keCEVMGCCc05tCaRd3Wn4sKWs1KP7l9U3Og4eruPJJdv55+rdPP2ZmR1u4iknp7KmPnhO4aSB+aSnxNc+chIfOtpO5Q68k5Hb7D/mnPt8ZNISv2nVQqqbFLC9lTX85t8b2ryvvLqeb//lbZ66cWaUszo1LNlcSvPm2Bo+lPZ0NAe2FFiGN+c1GdgQuEwEGiOamfhKyxWIY7pJAfvLyp00NLW/ueWizaWUlB1p9345ea3mv7SAQ9rR0XYqDwOY2bV4TXnrAz/fC7wc8ezEF5xzFAeOwIYUZpKbnhzjjMJjX2Vtx485VEv/fM2FhduCwPxXRkoiEwbkxTYZiVuhrkLsS+sNLLMCt4mwt7KW/Ye8D/ux3eToC2BQB/tOJRj0z0+PUjanjv2Hanl3r7fZxfQhBSTH+WaoEjuhvjN+Cqwws4fM7CG8pfQ/jlhW4ivdcf4L4JKJ/cg8weKB80f3ome2msuG28LNR9tHaf5LTiTUE5n/CJyB18/wz8DM5uFFkdUtO9B3kyX0ALnpyfz6YxNJTjx+AVNyovHdD4yJQVbd34IW+3/pBGY5kc4cm08H5gBnAdMik474UfP8lxmM6ZsT42zC64IxvfnnbXO4esZAxvbLIS/QXqq+0bFse1mMs+uemhdw5Gckc3rv7vV+kvAK9UTmn+IVrccDN33ezGY5526PWGbiC8654ArEIYWZZKd1jwUcLQ3vmc0PP+jt37pmVwXv/92bANz5341cNLYPCQna4j5cdpQeYUept6PBzGE99LeVEwr1COwi4Hzn3IPOuQeBC/F2ZZZT3O6KGg4GGt12p/mv9ozpm8t5p/cEYN2eQ7y8dm+MM+petPuydEZnhhDzWlzv/p9UEpLilh3o++fFLpEouvXcEcHrd/x3A861f66YdE5z+yjQBpbSsVAL2I85ugrxYbyTm7UKUVi9szx43c+bWHbGhAF5nD3S2z18za5K/rtuX4wz6h6cc8ENLPvmpjG4g9MYRDosYGaWADQBM/BWIDavQnwqwrmJDzQfgZnB6D6nzoT7599z9Cjsd//dqKOwMFi/t4oDVd75hDOHFbbZvk6kpQ4LmHOuCficc263c+6vzrm/OOf2RCE3iXPOOd4OrEAcXpRFZmqovaH9b8qg/GCLo1U7ynljw4EOniEdWaD2UdJJoQ4hvmJmXzGzAWZW0HyJaGYS90rKqik74u2R1Z3O/wrV51vMhf3uP5oL6yrNf0lnhVrArgNuwduFeWmLi5zCumsHjlCdMbQH04d43+OWbStj4aaDHTxD2tPQ2MTiQAeOYUWZ9MpRhxPpWKgFbDRwF7AKb2PLOwC1ITjFtV6BeOoVMIDbWs2Ftb31inRs9c4KDtU2AFo+L6ELtYA9DJwO/A6veJ0euE1OYc3zXwkGo/ucmgVs1rAeTB6YB3jbqyzZUhrbhHxqwaaWw4ea/5LQhFrATnPOfdo592rgciNwWiQTk/jmnAvumDuyV/Ypu2OumbVakXiHjsJOSvMCDjOYMVQFTEITagFbYWYzmn8wszOA+ZFJSfxge+kRKmu8IZ9xp+D8V0tnjywKngM3b8MBlqtHYqfU1DeydKv3NxvbN5e8jJQYZyR+EWoBOwNYYGZbzWwrsBA428xWm1lxxLKTuKX5r6PMrNWKxDv+o6Owzli+rYzahibAG5IVCVWoJ+5cGNEsxHfebrEC8VQ/AgN4z+k9Gd0nh7W7K3n13f0Ul5Qz/hRprdVVLee/Zmn5vHRCqPuBbTvRJdJJSvxpPgJLSjBOP4U6cLTHmwsbHvz5jv9ujGE2/tK8fUpyojFtcH6MsxE/0V7d0mlNTUc7cIzslU1a8qm5gONYF4zuzWm9sgF4Ze1e1u6qjHFG8e9QTX3wy9CkgflkpJw63Vyk61TApNO2HjwcPGdHw4dHJSQYnzv36FHYna9qLqwjizeX0tjkdTDR/Jd0lgqYdFrLDhyn+gKOY100rg9DizIB+Ofbe1i/91CMM4pv81v1P9T8l3SOCph02uoWKxBPlS1UQpWYYNwaOApzztu1WdrX3H4rIyWRCVr0Ip2kAiadVhw4AktONE7rnR3jbOLPB8b3ZVBgL6sXi3exaX9VjDOKTweqalm3xztCnT6kgJQkfRxJ5+gdI53S1ORYEyhgp/XOJjVJCziOlZSYwGfnekdhTQ7uelVHYW1p1T5K/Q/lJKiASadsPnCYw3WNAIzrlxfbZOLYZZP60T8/HYC/rNzFtoOHY5xR/Fmw8ej810wt4JCToAImnbJ6Z3nwuua/2pecmMAt53hHYY1Njrtf3RTjjOJP8xFYfkbyKbWbt4SPCph0SqsWUlpCf0IfntKPPrnevlbPLS+hpOxIjDOKHztKj7C91Pt7zBzWg4QEi3FG4kcqYNIpzSsQUxITGNlLCzhOJDUpkZvPGQZAQ5Pjntd0FNZsQYvl89r/S06WCpiErLHJsSbQXeL0PtlaNRaCy6cOoGd2KgDPLC1hd0V1jDOKD/M3tuh/qPkvOUn6BJKQbdpfRXV9YAGH5r9CkpacyE1ne0dhdY1N3Pf65hhnFHvOueD8V5/cNIYUZsY4I/ErFTAJWcv5r/FagRiyK6cPpDDL2+PqySXb2VdZE+OMYmvDvioOVNUC3vChmea/5OSogEnIVgd2YAYYqwUcIUtPSeSGOUMBqG1o4v43Tu2jsPkbW7aP0vChnDwVMAlZcw/E1KQERvTKinE2/nL1jEHkZyQD8Pji7cEjkFNR6/kvLeCQk6cCJiFpaGwKLuAY3TeH5ES9dTojMzWJTweOwqrrG/n9vC0xzig2GhqbWLzZK2BDizLpHTjNQORk6FNIQrJhX1Vw2/fxGj48KZ+YOYicNG+/q0cXbqXscF2MM4q+t3dVBrfiUfso6SoVMAlJyw70mv86OdlpyVw3ewgAh+saeXD+qXcUpvkvCScVMAlJyz3Axmvbi5P2qVlDyEr1jsIemr+Viur6GGcUXc0nMJvBjKEqYNI1KmASkuYtVNKTExlWpPN2TlZuRjLXzhoMwKHaBh6avzWm+URTTX0jS7eWATCmbw55GSkxzkj8TgVMOlTX0MQ7u70FHGP65pCkBRxdct3sIWSkeNvQPDh/C4dqTo2jsOXby4LzqJr/knDQJ5F0aP3eQ9QFPng0/9V1BZkpXDNzEAAV1fU8snBbjDOKjgUtl88PVwGTrlMBkw693Wr+SwUsHG6YM5S0ZO+f3x/e3MLhwMq87mx+YP4rOdGYNjg/xtlId6ACJh0qVgELu8KsVK6c7h2FlR6u4/HF3fso7FBNfbAV2aQB+WSkJMU4I+kOVMCkQ81L6DNTEhlSqA4c4XLT2UODHf3vf2ML1YGdrrujJVtKaWxyAMzS8nkJExUwOaHahkbW7WlewJFLojYeDJteOWl8fNoAAA5U1fLkku0xzihyWraPOlPzXxImES9gZpZoZivM7MVIx5LwW7+nivpG75uztlAJv8+cPYzkRO9LwX1vbKKmvnsehTWf/5WenMgEnUcoYRKNI7DbgHeiEEcioHhnefC65r/Cr29eOh+Z4h2F7a2s5ZmlO2KcUfgdqKpl3Z5DAEwfUqCNUCVsIvpOMrP+wPuB30cyjkROyxZS47SEPiJuOWcYSYGh2Xte2xQ8ZcHvauobeXrpDm5+bFnwthlDC2KYkXQ3kV4K9Bvga0B2ew8wsxuBGwEGDhwY4XSks5pXjmWlJjG4hzpwRMKAggwum9SPZ5aVsKuihjk/+y89c9I47/ReXDNzEAWZ/utYsf3gEa7+w2K2lx5pdfuf3trBR6YMoCg7NUaZSXcSsSMwM7sY2OecW3aixznn7nfOTXXOTS0qKopUOnISauobWb/XG/oZ2y+HBC3giJhJA/OC1/ceqmX1zgp+/e/1XPy7eWw/eKT9J8Yh5xyfeWzZccULYOvBI3z5mVUxyEq6o0gOIZ4JXGJmW4GngHPN7LEIxpMwW7fnEA2Bpc9q4Bs5h2sb+Mk/17V5366KGr7x/OooZ9Q1S7aUsjbQeqwtb6zfz6b9VVHMSLqriA0hOuduB24HMLNzgK84566OVDwJv9Ul5cHrmv+KnL+v3s2hmvY7cby58QDFO8oZPyAvekl1QnVdI+/sqWTNrkrW7qrg9Xf3d/icdbsPMaxI5xRK1+h0eGlXsRZwRMWONobajnXJXfMpyExheFEWw3pmMbzFpW9uGmahD+8erm3ghZU7WbKllMQE49xRPXnvmN4h7bJdfqSONbsqWbOrIvDfSjbvryJwoB6yrDR99EjXReVd5Jx7DXgtGrEkfJr3AMtOS2JQj4wYZ9N99cpJC+lxpYfrWHK4lCVbS1vdnpGSyLCiowWt+fqgHhnHFaWN+w5xzR+WsLuiJnjbn5fvZFy/XB6+bnpwwYhzjj2VNazZWdmqYO0sr+4wz/TkRGoaGnHtFLXCrBStRpSw0NcgaVN1XSMb9nnzFOP753bqG750zsXj+/DDv6+lpr7t5fO9c1KZMriATfuq2Hzg8HHL7I/UNbJ6Z0WrTUcBkhKMwYWZDA8UtKFFmfzy5XdbFa9mq3dW8MkHlzBreA/WBo6sSg/XdZh7QWYKY/rmMLpvDmP65jKmbw6De2Tyx/lb+OHf2z7983/fP5rUpMQOX1ukIypg0qa1uyuDvevG9cuLbTLdXF5GCj/84Di++swqjj1oyU1P5sFrpzO6bw4AjU2OHaVH2Livio37q7z/7qti074qDh3T0b6hyQXvZ03HebRVBFvql5ceKFRHi1WfdoYvPz1nKD2yUrjr1U1efLy95L5w3kjOH92r42REQqACJm3SAo7o+siU/gzIT+eBeVtYtq2UlKQEzju9FzedNYyBLYZvEwNHVYMLMzmPo4XAOce+Q7XBghW87K9i/6HaTuWSYDC0KCtQqLxiNbpPDvmdPB/tskn9+eDEfuw/VEtCgtEjM0VH8hJWKmDSptU7jy6DVgup6DhjaA/OGHpyndrNjF45afTKSTuuWW7FkXo27j/Eowu38cLKXSd8nR9dNpbLJvUL23YnZkbPEOf4RDpLBUzatDrQAzEvI5n++emxTUa6JDcjmSmDChhYkMnfV+8ONmc+Vr+8dD4+baB2HBDfUFdNOc7h2obgvMW4flrA0V0UZafyhfNGtnlfgsG3Lh6t4iW+oiMwOc7a3ZXB83o0/9W93HLOMIqyU7n3tU1sPnAYgIkD8vjS+SM5a6RauYm/qIDJcVp2oNf8V/diZlw+dQAfndKf/VW1JCckdHpxhki8UAGT47RcSj1OPRC7JTOjZ7YWV4i/aQ5MjlMcWELfIzOFvrn6kBOR+KQCJq1U1TYE50bGagGHiMQxFTBpZc3OimAPO81/iUg8UwGTVlrNf2kFoojEMRUwaaW41QrEvNglIiLSARUwaaX5CKwwK5VeOakxzkZEpH0qYBJUWVPPlsACDm2hIiLxTgVMgt7W/JeI+IgKmASpA4eI+IkKmAQV6whMRHxEBUyCmocQe+Wkag8nEYl7KmACeJsebjt4BIBx/fJim4yISAhUwARofQKz5r9ExA9UwASA4sAOzKD5LxHxBxUwAVovoR+rAiYiPqACJsDRFlJ9c9MoylYHDhGJf77c0LLiSD1/XbWTLQeOUJidwqUT+9EvLz3WaflW6eE6SsqqARin+S8R8QnfFbB/r93LbU+t4HBdY/C2X768nq9feBo3njUshpn5lzrQi4gf+WoIcfP+Km55Ynmr4gXQ2OT48T/W8cravTHKzN9atZBSB3oR8QlfFbBHFm6jrqGp3fsfmLc5itl0H8Ul5cHrOgITEb/wVQFbuaO8S/dL25p7IPbPT6cgMyXG2YiIhMZXBSwt+cTppiX56teJCweqatlVUQPo6EtE/MVXn/gXjul9wvsvGN0rSpl0H60WcGgFooj4iK8K2EenDmBEz6x2769taMI5F8WM/K/VFirqgSgiPuKrApaZmsQTN8zg/eP7kJhwdLfg5ut/K97NQwu2xig7fyou0RJ6EfEn350HVpSdyl1XTuZAVS07y6rpkZXC5v2H+dRDb9HY5PjBi2sZXJjJ3NN6xjpVX1gd6IE4sCCD3Izk2CYjItIJvjoCa6kwK5UJA/Lon5/BWSOL+PbFowFocnDrEytYv/dQjDOMf/sqa9hbWQto/ktE/Me3BexYn5w1mGtmDAKgqraB6x9+i9LDdTHOKr612kJFw4ci4jPdpoABfPsDo5k9vBCAHaXVfObRZSc88flU12r+S0dgIuIz3aqAJScmcNeVkxlamAnAkq2lfPP51VqZ2I7V2kJFRHysWxUwgNyMZP5w7TRy070FCc8sK1GLqTY454IFbEhhJjlpWsAhIv7S7QoYeB/I91w1maTA8vqf/HMd/1aj31b2Vtay/1BgAYeOvkTEh7plAQOYNbyQ7106BgDn4LanVvDO7soYZxU/WjbwHa/5LxHxoW5bwACuOmMQnzpzMACH6xr59MNLg0cdpzrNf4mI33XrAgbwzYtO5+yRRQDsLK/mpkeXUlPf2MGzur/mAmYGY/rmxDgbEZHO6/YFLCkxgTuunBTsobh8ezm3//nUXpnonAv2QBxamEm2FnCIiA91+wIGkJOWzB8+OY38QKuk51fs5O7XNsU4q6OccyzafJBHF23jb6t2cbi2IaLxdlXUcDBwkvd47cAsIj7lu16IJ2tgjwzuvXoKV/9hMfWNjp+/9C7DijK5cGyfmOa1aX8Vn318Oev2HG19lZWaxLcvHs3l0wZEJObqFgs4NP8lIn51ShyBNTtjaA9+9MFxwZ+/+KdVvN1iMUO0VdbUc9UDi1sVL/BaYX3tuWL+805klv63aiGlFYgi4lOnVAEDuHzaAG48aygA1fWN3PDIUvZV1sQkl+eWlbDnBLHvfHVj2GOWHq7jzQ0HAEgwGN1HCzhExJ9OmSHElr5+4Sg276/i3+/sY3dFDTc8spQ/3TSTtOTEqOYxf+PBE96/Yns5F/12HgMK0umXl0H//HT65afTLy+d/vnp5KYnY2YnfI1mh2rq+e5f1/KXlSU0t4dMSkhg1Y5yZgX6R4qI+IlFajWemaUBbwCpeIXyWefcd070nKlTp7qlS5dGJJ9jVdU28JF7FgSH7y4e34c7rpgUckHoiobGJv79zj6+8efVlB45+Y75WalJ9MtrXdSar/fLT6coKxUzo6GxiY/fv4il28qOe42kBOOpG2cwdXBBV34lEZGIMLNlzrmpbd4XwQJmQKZzrsrMkoE3gducc4vae040CxhASdkRPnjXfA5UeUXki+eN5LbzRkQs3sGqWp56awePL9rGroqOhy0zUhJJTUqg7Ej9ScVLTUqgX146qckJvLO7/f3RZg7twZM3zjipGCIikXSiAhaxIUTnVcaqwI/JgUtcnXzVPz+D+66ZwhX3L6ausYlf/3s9w3pmcvH4vmGNs3JHOY8s3MqLq3ZT19h6e5eUpIQ2t3xJMLj/mqnMHlHI4doGdpVXU1JWTUl5NTvLqtlZXk1J2RF2llWzr53uIrUNTWw+cLjD/BZuPkhlTb0a+oqIr0R0DszMEoFlwHDgLufc4kjGOxlTBhXws4+M44t/WgXAl59exYD8DCYMyOvS69bUN/L34t08snArq0par3RMTUrgkgl9+cTMweRnJvOFp1a2Gt4rzErle5eMYfYIb24qMzWJEb2yGdEru81YtQ2N7C6voaSsmp3lXlErCRS8nWXV7Cqv7vCbQ219E6R16VcWEYmqiA0htgpilgc8D9zqnHv7mPtuBG4EGDhw4JRt27ZFPJ+2/Pylddz1qndyc8/sVP7yuTPpk5ve6dfZWV7N44u28dRbO47bEbp/fjrXzBjE5VMHkJ+Z0uq+Nbsq2LC3iryMZGYNKyQlKXwLRB94YzM/+sc77d4/sCCD1796TlTm/0REOiMmc2BtJPEd4LBz7hftPSbac2AtNTU5bn58GS+t8c69Gtsvh6dvmklGSscHqc45Fm46yMMLt/LK2r00HfMnnTOikE/OHMzcUT1JTIh+kag4Us/cX7zW7oKRH3xwLNfMGBTlrEREOharRRxFQL1zrtzM0oGXgZ85515s7zmxLGAAR+oa+Mg9C1kb2HblwjG9uXxaf7YcOEKPzBTOG92LrNSjBa2qtoE/Ly/hkYXb2LivqtVrZacm8eEp/blm5iCGFWVF9fdoS3FJOZ9+eGmr+TIDbjx7KP9z4SgdfYlIXIpVARsPPAwk4p0w/bRz7vsnek6sCxjA7opqLrlzfpvbrmSlJvGdD4xm0sB8Hl24leeW76TqmL6FI3tlcc3MwVw2qV+rYhcPauobeWnNHt7ZfYic9CQuHteXgT0yYp2WiEi74mIIMRTxUMAAXn93H5/841shPz4xwbhgdC8+MXMwM4YW6GhGRCRMYrKM3s9Wh9gfsUdmCldMH8iVZwykb17nF3yIiMjJUwFrw6LNpR0+5pcfncDFE/qQmhTd9lMiIuJRAWtDQgcrBRMTjMsm9evwcSIiEjmnXDf6UJwzsuiE9589skjFS0QkxlTA2vDRqf3pn9/2nFZSgvG5c4dHOSMRETmWClgbstOSefKGGUwbnN/q9n556TzwyalMHpjfzjNFRCRaNAfWjgEFGTzzmVm8u+cQm/ZXUZCZwrTBBTHppCEiIsdTAevAab2zOa132010RUQkdjSEKCIivqQCJiIivqQCJiIivqQCJiIivqQCJiIivqQCJiIivhRX26mY2X5gWyefVggciEA6iqM4iqM4ihP7OIOcc23294urAnYyzGxpe3vFKI7iKI7iKE73jaMhRBER8SUVMBER8aXuUMDuVxzFURzFUZxTL47v58BEROTU1B2OwERE5BSkAiYiIr6kAiYiIr6kAiYiIr6kAhZDZvZeM7vHzP5qZn8JXL8wivG/HYHXfK+ZXW9mg4+5/bowxjAzu9zMPhq4/h4z+52Z3WJmEX1Pm9l/I/Cahcf8fHXg97nRzMK2BbiZXWZmBYHrRWb2iJmtNrM/mVn/MMb5lZmdGa7XO0GcAjP7tpl9OvA++KaZvWhmPzez/DDHmmtmdwb+nT5nZj81s+HhjBGI060+EyL9eeCrVYiBf3yfA3YBfwC+AcwE3gF+7JwrC2OsucCHgQFAA7AB+L1zbmOYXv83wEjgEaAkcHN/4BPABufcbeGI00EO251zA8P4ej8GZgPLgQ8Av3HO3RG4b7lzbnKY4twN9ARSgEogFfgbcBGwN1x/OzMrPvYmvP9n7wI458aHKU7wb2Nm/wvMAZ4ALgZKnHNfDFOctc650YHrfwIWAc8A5wFXOefOD1Oc5pZwRcCfgCedcyvC8drHxPkHsBrIAU4PXH8aOB+Y4Jy7NExxfgr0Av4DfBDYAqwHbsH73HkmTHF+Qzf6TIjG54HfCli3ecOa2Xrn3Mg2bjdgvXNuRFdjBF6vsr27gHTnXFI44gRirQYmOecazCwP70P4XefcF81shXNuUrjiOOfGmVkysAfo45yrM7MkYIVzblyY4vwVr0D+EKjG+5vNw/tHiXOus30724sT/NuY2XJgjnPucOD3Wx7G3+dd59xpgevLnHNTWty30jk3MUxxVjjnJpnZCODjgUsi8CReMVsfpjgrnXMTA/9mSpxz/Y69L0xxVjf/Pwi8x153zp0ZOMqb55wbG6Y43eozIRqfB34bQuzrnPs6XiEZ4Zy71Tk3zzn3bWBQGOO83zn3KefcY3j/+GY55x4AzgW+E6YYNWY2vY3bpwE1YYoBUI73t8o55pIN7A5jHIAk51wDgHOuHO9bV46ZPYN3tBQuzTHqgbecc3WBnxuAxnAFcc5dAjyHd/LlBOfcVqDeObctXMUrIN3MJpnZFCDROXc4EL+eMP4+wGtm9n0zSw9c/yAERxsqwhjHATjnNjjnfuCcGwNcDqQB/whjnIRAERkAZDUPU5lZD8L7fmtqHnoF+uIVYwIjPmEb4qX7fSZE/PMgbN++o6T5DZtN4A3rnNsaqTesc66UY96wYZyTuBa4x8yyOTpcMADvG/+1YYoB3nDEIGBvG/c9EcY4AJvM7Gzn3OsAzrlG4Hoz+yHecGy47DGzLOdclXMuOD9gZr2BujDGwTn3vJm9DPzAzD5NeN9nzXYDvwpcLzWzPs653YH3dUMY43wO+CaBIVDgi2Z2GG/49Zowxjnu34hzrhgoBm4PY5yfAOsC168Dfm9mDhgNfC+McX4MrDCzd4FRwM3gzSMCq8IY51q612dCxD8P/DaEeAXwm8CPt+C9kYJvWOdcWNqUmNnHgP/D+4c+CrjZOff3wBv2t865K8MRJxCrN9AP7x99iXNuT7heO9oC3+xxzlW3cV8/59zOCMfPBDKdc/si9PoTgJnOuXsj8fptxEsEUp1zRyLw2rl435APRuC1s5xzVeF+3XZiJeJ9jjUEhvcmAjudc2EdXQgcgQ0FNgaOJiKmu3wmROPzwFcFDLrXGzZwNDcd783q8BanLHFh/p8SrTjRjKU4itMd45wg/ijn3LqOH3lqxfFdAQMws6m0WB0YqT94JOOY2QXA3XirG5u/ifQHhgO3OOde9lOcaMZSHMXpjnE6yCGsK4a7SxxfFTAzOxv4Jd4k5BRgPpAP1APXOOd2+CWOmb0DvC+wMKDl7UOAfzjnTu9qjGjGiWYsxVGcbhrnd+3dBXzSOZejOK35bRHHb4ALnHP7A2+eXzlvOev5eOeFXeCjOEkcnahtaSeQHIbXj3acaMZSHMXpjnE+BXwZqG3jvisU53h+K2CJzrn9gevbCSydd869Yt5JgH6K8yDwlpk9BTQf0Q3AW7b/hzDFiGacaMZSHMXpjnHeAt52zi049g4z+67iHM9vQ4gP4k2g/ge4FG/xxpfMLAPvhM9RPoszGriEFiuOgL8659aG4/WjHSeasRRHcbpbnMDCsZpIrDrtrnH8VsCSgRvwls2vAh50zjUGlmv2dOHrjBCVOCIi0gXOOV1icAFygZ/inYh5MHB5J3Bbnt/idMffSXEUR3HiO46vWkmZWZZ5rXDeNrMKM9tvZovM7FofxnkaKAPOcc71cM71AObirXwMS3PQKMeJZizFUZxTKU6Z4rTNb0OIfwGeB/6N11stE3gK+F+8eapv+CWOtWis2pn74jVONGMpjuIojuKA/5r5DnbOPeScK3HO/Qq4xDm3AW+55od8FmebmX3NzHo132Bmvczs6xxd6eSnONGMpTiKoziK47sCdtjMZgOY2QeAUgDnXBOEtSt0NOJ8DOgBvG5mZWZWCrwGFOAd9YVLtOJEM5biKI7iKI6/FnEA44EleGPPbwIjA7cXAZ/3YZxReJsJZh1z+4Vh/rtFJU53/J0UR3EUJ37jhC3RWF+AT/kpDvB5vG73LwBbgUtb3Lc8jPlGJU53/J0UR3EUJ77jhCXReLgA2/0UB2836azA9cHAUuC2wM8rwphvVOJ0x99JcRRHceI7jq9aSZlZcXt3Ab3auS9e4yS6wJ5JztuU8xzgWTMbRHjn86IVJ5qxFEdxFEdxfLeIoxfwCbytqY+9hHNjvmjE2WNmE5t/CPyPvhgoBMaFKUY040QzluIojuIojr+GEPEaZ85u574n/BQHbz+h3u3cd2YYf5eoxOmOv5PiKI7ixHccX53ILCIi0sxvQ4giIiKACpiIiPiUCpiIiPiSCpiIj5lZYqxzEIkVFTCRKDGzH5jZbS1+/pGZfd7Mvmpmb5lZsZl9r8X9L5jZMjNbY2Y3tri9yrztfhYDM6P8a4jEDRUwkej5A/BJADNLAD4O7AVGANOBicAUMzsr8PjrnHNTgKnA582sR+D2TOBt59wZzrk3o5i/SFzxVScOET9zXjeCg2Y2Ce9k+RXANOCCwHWALLyC9gZe0boscPuAwO0HgUbguWjmLhKPVMBEouv3wLVAb+BB4D3AT5xz97V8UKDtznnATOfcETN7DUgL3F3jnGuMUr4icUtDiCLR9TxwId6R10uBy3VmlgVgZv3MrCeQC5QFitcoYEasEhaJVzoCE4ki51ydmb0KlAeOol42s9OBhWYGUAVcDfwL+EygsfS7wKJY5SwSr9RKSiSKAos3lgMfdc5tiHU+In6mIUSRKDGz0cBG4D8qXiJdpyMwERHxJR2BiYiIL6mAiYiIL6mAiYiIL6mAiYiIL6mAiYiIL6mAiYiIL/0/iipKULtGlkEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,5))\n",
+    "sns.pointplot(x='year',y='prodvalue',data=data,ci=None)\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "43eb5fc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#            OBSERVATIONS\n",
+    "# Above graph shows that maximum production value was obtained in the year 2012.\n",
+    "# We can also refer that in 2003 production value increased from 1998 but then decreased till 2007.\n",
+    "# But after that it increased till 2012 and in 2012, highest value of production was noted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cb793b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Conclusion: Production value had increased from 1998 to 2012."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "dd477721",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
+       " [Text(0, 0, '1998'),\n",
+       "  Text(1, 0, '1999'),\n",
+       "  Text(2, 0, '2000'),\n",
+       "  Text(3, 0, '2001'),\n",
+       "  Text(4, 0, '2002'),\n",
+       "  Text(5, 0, '2003'),\n",
+       "  Text(6, 0, '2004'),\n",
+       "  Text(7, 0, '2005'),\n",
+       "  Text(8, 0, '2006'),\n",
+       "  Text(9, 0, '2007'),\n",
+       "  Text(10, 0, '2008'),\n",
+       "  Text(11, 0, '2009'),\n",
+       "  Text(12, 0, '2010'),\n",
+       "  Text(13, 0, '2011'),\n",
+       "  Text(14, 0, '2012')])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 5. Variation in number of colonies over the year\n",
+    "plt.figure(figsize=(7,5))\n",
+    "sns.pointplot(x='year',y='numcol',data=data,color='blue',ci=None)\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed57499d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#             OBSERVATIONS\n",
+    "# 1) Maximum number of colonies were obtained in 2010.\n",
+    "# 2) Minimum number of colonies were obtained in 2008."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d5407bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Conclusion: Number of colonies had decreased from 1998 to 2012."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "d976a9c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
+       " [Text(0, 0, '1998'),\n",
+       "  Text(1, 0, '1999'),\n",
+       "  Text(2, 0, '2000'),\n",
+       "  Text(3, 0, '2001'),\n",
+       "  Text(4, 0, '2002'),\n",
+       "  Text(5, 0, '2003'),\n",
+       "  Text(6, 0, '2004'),\n",
+       "  Text(7, 0, '2005'),\n",
+       "  Text(8, 0, '2006'),\n",
+       "  Text(9, 0, '2007'),\n",
+       "  Text(10, 0, '2008'),\n",
+       "  Text(11, 0, '2009'),\n",
+       "  Text(12, 0, '2010'),\n",
+       "  Text(13, 0, '2011'),\n",
+       "  Text(14, 0, '2012')])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 6. Analyse the the variation of yield per colony over the years and the production trends at state level.\n",
+    "plt.figure(figsize=(7,5))\n",
+    "sns.pointplot(x='year',y='yieldpercol',data=data,ci=None)\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7ab52bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#        OBSERVATIONS\n",
+    "# 1)Maximum yield per colony was obtained in 1998 with a value of 70.\n",
+    "# 2)Minimum yield per colony was obtained in 2009 with a value lower than 54."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad2e9aa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CONCLUSION: Yield per colony had decreased from 1998 to 2012."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "54f0b7aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
+       " [Text(0, 0, '1998'),\n",
+       "  Text(1, 0, '1999'),\n",
+       "  Text(2, 0, '2000'),\n",
+       "  Text(3, 0, '2001'),\n",
+       "  Text(4, 0, '2002'),\n",
+       "  Text(5, 0, '2003'),\n",
+       "  Text(6, 0, '2004'),\n",
+       "  Text(7, 0, '2005'),\n",
+       "  Text(8, 0, '2006'),\n",
+       "  Text(9, 0, '2007'),\n",
+       "  Text(10, 0, '2008'),\n",
+       "  Text(11, 0, '2009'),\n",
+       "  Text(12, 0, '2010'),\n",
+       "  Text(13, 0, '2011'),\n",
+       "  Text(14, 0, '2012')])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,8))\n",
+    "sns.pointplot(x='year',y='totalprod',data=data,ci=None,hue='state')\n",
+    "plt.legend(bbox_to_anchor=(1,1))\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dee76b17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#     OBSERVATIONS\n",
+    "#  States with blue colour ie NY,ND,OH etc have maximum total production."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50180750",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CONCLUSION: States with blue colour had maximum total production in 2010 with a value greater than 4 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "fc46515f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1eaa91c2eb0>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1944 with 44 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(x='year', y='totalprod', data=data , col='state' , kind=\"point\" , height=3 , col_wrap=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f823d71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#    OBSERVATIONS\n",
+    "#  1) ND state has the maximum total production value.\n",
+    "#  2) CA,FL,MN,MT,SD,TX also produce large amount of total production value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48786d64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CONCLUSION: ND state has the maximum total production value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "c71bdcbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
+       "         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
+       "         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
+       "         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
+       "         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
+       "         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
+       "         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
+       "         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
+       "        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
+       "        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
+       "        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
+       "        143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
+       "        156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
+       "        169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
+       "        195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
+       "        208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n",
+       "        221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n",
+       "        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n",
+       "        247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
+       "        260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n",
+       "        273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n",
+       "        286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,\n",
+       "        299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n",
+       "        312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,\n",
+       "        325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,\n",
+       "        338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,\n",
+       "        351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,\n",
+       "        364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,\n",
+       "        377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,\n",
+       "        390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,\n",
+       "        403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,\n",
+       "        416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,\n",
+       "        429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,\n",
+       "        442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,\n",
+       "        455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,\n",
+       "        468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,\n",
+       "        481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493,\n",
+       "        494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506,\n",
+       "        507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519,\n",
+       "        520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532,\n",
+       "        533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545,\n",
+       "        546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558,\n",
+       "        559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571,\n",
+       "        572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584,\n",
+       "        585, 586, 587, 588]),\n",
+       " [Text(0, 0, '162000.0'),\n",
+       "  Text(1, 0, '173000.0'),\n",
+       "  Text(2, 0, '174000.0'),\n",
+       "  Text(3, 0, '179000.0'),\n",
+       "  Text(4, 0, '186000.0'),\n",
+       "  Text(5, 0, '210000.0'),\n",
+       "  Text(6, 0, '221000.0'),\n",
+       "  Text(7, 0, '235000.0'),\n",
+       "  Text(8, 0, '238000.0'),\n",
+       "  Text(9, 0, '249000.0'),\n",
+       "  Text(10, 0, '251000.0'),\n",
+       "  Text(11, 0, '253000.0'),\n",
+       "  Text(12, 0, '255000.0'),\n",
+       "  Text(13, 0, '256000.0'),\n",
+       "  Text(14, 0, '259000.0'),\n",
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+       "  Text(578, 0, '31020000.0'),\n",
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+       "  Text(580, 0, '35040000.0'),\n",
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+       "  Text(582, 0, '42579000.0'),\n",
+       "  Text(583, 0, '44702000.0'),\n",
+       "  Text(584, 0, '47817000.0'),\n",
+       "  Text(585, 0, '48960000.0'),\n",
+       "  Text(586, 0, '54542000.0'),\n",
+       "  Text(587, 0, '63590000.0'),\n",
+       "  Text(588, 0, '69615000.0')])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 7. Effect of declining production trend had on the value of production.\n",
+    "plt.figure(figsize=(7,5))\n",
+    "sns.pointplot(x='prodvalue',y='totalprod',data=data,ci=None)\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81e6becb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OBSERVATION: Total production value and production value are directly proportional to each other.\n",
+    "\n",
+    "# CONCLUSION: Total production value is decreasing with decrease in production value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "3b6824b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
+       " [Text(0, 0, '1998'),\n",
+       "  Text(1, 0, '1999'),\n",
+       "  Text(2, 0, '2000'),\n",
+       "  Text(3, 0, '2001'),\n",
+       "  Text(4, 0, '2002'),\n",
+       "  Text(5, 0, '2003'),\n",
+       "  Text(6, 0, '2004'),\n",
+       "  Text(7, 0, '2005'),\n",
+       "  Text(8, 0, '2006'),\n",
+       "  Text(9, 0, '2007'),\n",
+       "  Text(10, 0, '2008'),\n",
+       "  Text(11, 0, '2009'),\n",
+       "  Text(12, 0, '2010'),\n",
+       "  Text(13, 0, '2011'),\n",
+       "  Text(14, 0, '2012')])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,5))\n",
+    "sns.pointplot(x='year',y='prodvalue',data=data,ci=None)\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d03f0d56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#         OBSERVATIONS\n",
+    "# 1)Maximum total production value is greater than 7 in 2012 and 2010 followed by 2003 and 2008. \n",
+    "# 2)Minimum total production value is less than 3 in 1999 followed by 2001 and 2000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d8da3d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CONCLUSION: Production value has increased from 1998 to 2012."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "8c50173b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='prodvalue', ylabel='year'>"
+      ]
+     },
+     "execution_count": 65,
+     "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.regplot(x=data[\"prodvalue\"], y=data[\"numcol\"],color=\"blue\")\n",
+    "sns.regplot(x=data[\"prodvalue\"], y=data[\"yieldpercol\"],color=\"red\")\n",
+    "sns.regplot(x=data[\"prodvalue\"], y=data[\"totalprod\"],color=\"black\")\n",
+    "sns.regplot(x=data[\"prodvalue\"], y=data[\"priceperlb\"],color=\"purple\")\n",
+    "sns.regplot(x=data[\"prodvalue\"], y=data[\"stocks\"],color=\"green\")\n",
+    "sns.regplot(x=data[\"prodvalue\"], y=data[\"year\"],color=\"orange\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "992f49cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OBSERVATIONS: Above plot shows that prod value is decreasing as years are increased.\n",
+    "\n",
+    "# CONCLUSION: Total production is the cause for its declining followed by stocks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "e8c8f9f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='prodvalue', ylabel='totalprod'>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(kind=\"scatter\",x=\"prodvalue\",y=\"totalprod\",figsize=(8,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0873eb0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OBSERVATION: Total production value and production value are directly proportional to each other.\n",
+    "\n",
+    "# CONCLUSION: Total production value is decreasing with decrease in production value."
+   ]
+  }
+ ],
+ "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}