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Created July 5, 2022 06:11
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Travel Package Purchase Prediction: The "Visit with us" travel company dataset is used to analyze the customers' information and build a model to predict the potential customer who is going to purchase the newly introduced package.
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Travel_Package_Ahmad_Abdelaziz.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "f6b0bb26",
"metadata": {},
"source": [
"# Exploratory Data Analysis, Bagging & Boosting models building for identifying potential asset customer\n",
"*by Ahmad Abdelaziz*\n",
"\n",
"Background and Context\n",
"\n",
"a tourism company named \"Visit with us\". The Policy Maker of the company wants to enable and establish a viable business model to expand the customer base.\n",
"\n",
"One of the ways to expand the customer base is to introduce a new offering of packages.\n",
"\n",
"Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages.\n",
"\n",
"The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being.\n",
"\n",
"However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient.\n",
"\n",
"Data Dictionary\n",
"- `CustomerID`: Unique customer ID\n",
"- `ProdTaken`: Whether the customer has purchased a package or not (0: No, 1: Yes)\n",
"- `Age`: Age of customer\n",
"- `TypeofContact`: How customer was contacted (Company Invited or Self Inquiry)\n",
"- `CityTier`: City tier depends on the development of a city, population, facilities, and living standards. The categories are ordered i.e. Tier 1 > Tier 2 > Tier 3\n",
"- `Occupation`: Occupation of customer\n",
"- `Gender`: Gender of customer\n",
"- `NumberOfPersonVisiting`: Total number of persons planning to take the trip with the customer\n",
"- `PreferredPropertyStar`: Preferred hotel property rating by customer\n",
"- `MaritalStatus`: Marital status of customer\n",
"- `NumberOfTrips`: Average number of trips in a year by customer\n",
"- `Passport`: The customer has a passport or not (0: No, 1: Yes)\n",
"- `OwnCar`: Whether the customers own a car or not (0: No, 1: Yes)\n",
"- `NumberOfChildrenVisiting`: Total number of children with age less than 5 planning to take the trip with the customer\n",
"- `Designation`: Designation of the customer in the current organization\n",
"- `MonthlyIncome`: Gross monthly income of the customer\n",
"\n",
"### Contents:\n",
"\n",
"### A. Data Preprocessing\n",
"1. Column Data check\n",
"2. Check the column Data types and its applicability for analysis / transformation\n",
"3. Imputing/Dropping for missing values\n",
"\n",
"### B. EDA\n",
"* Univariate analysis\n",
"* Bivariate analysis\n",
"* EDA Observations\n",
"\n",
"### C. Bagging Model Building\n",
"* Model evaluation criterion\n",
"* Decision Tree model\n",
"* Random Forest model\n",
"* Bagging clasifier model\n",
"* Model Tuning\n",
"* Model performance comparison\n",
"* Conclusion\n",
"\n",
"### D. Boosting Model Building\n",
"* Adaboost classifier model\n",
" - Hyperparameter tuning\n",
"* Gradient boost classifier model\n",
" - Hyperparameter tuning\n",
"* XGboost classifier model\n",
" - Hyperparameter tuning\n",
"\n",
"### E. Bagging vs Boosting Model comparison\n",
"* All Models Comparison\n",
"* Final Remark\n",
"* Recommendation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "fd351fe4",
"metadata": {},
"outputs": [],
"source": [
"#Importing required libraries for Explarotary data analysis\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"sns.set(color_codes=True)\n",
"import matplotlib.pyplot as plt\n",
"pd.set_option(\"display.max_rows\", None)\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# Library to split data \n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Libraries to import decision tree classifier and different ensemble classifiers\n",
"from sklearn.ensemble import BaggingClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import tree\n",
"from sklearn.ensemble import StackingClassifier\n",
"\n",
"# Libtune to tune model, get different metric scores\n",
"from sklearn import metrics\n",
"from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, precision_score, recall_score,f1_score,roc_auc_score\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
"from xgboost import XGBClassifier"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "68372af6",
"metadata": {},
"outputs": [],
"source": [
"#Reading the dataset and inserting it in a Dataframe\n",
"data = pd.read_excel('Tourism.xlsx', sheet_name='Tourism')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cfc945c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4888, 20)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Checking the overall size of the dataset\n",
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0f570e21",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" 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>CustomerID</th>\n",
" <th>ProdTaken</th>\n",
" <th>Age</th>\n",
" <th>TypeofContact</th>\n",
" <th>CityTier</th>\n",
" <th>DurationOfPitch</th>\n",
" <th>Occupation</th>\n",
" <th>Gender</th>\n",
" <th>NumberOfPersonVisiting</th>\n",
" <th>NumberOfFollowups</th>\n",
" <th>ProductPitched</th>\n",
" <th>PreferredPropertyStar</th>\n",
" <th>MaritalStatus</th>\n",
" <th>NumberOfTrips</th>\n",
" <th>Passport</th>\n",
" <th>PitchSatisfactionScore</th>\n",
" <th>OwnCar</th>\n",
" <th>NumberOfChildrenVisiting</th>\n",
" <th>Designation</th>\n",
" <th>MonthlyIncome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>200000</td>\n",
" <td>1</td>\n",
" <td>41.0</td>\n",
" <td>Self Enquiry</td>\n",
" <td>3</td>\n",
" <td>6.0</td>\n",
" <td>Salaried</td>\n",
" <td>Female</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" <td>Deluxe</td>\n",
" <td>3.0</td>\n",
" <td>Single</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>Manager</td>\n",
" <td>20993.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>200001</td>\n",
" <td>0</td>\n",
" <td>49.0</td>\n",
" <td>Company Invited</td>\n",
" <td>1</td>\n",
" <td>14.0</td>\n",
" <td>Salaried</td>\n",
" <td>Male</td>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
" <td>Deluxe</td>\n",
" <td>4.0</td>\n",
" <td>Divorced</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>Manager</td>\n",
" <td>20130.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>200002</td>\n",
" <td>1</td>\n",
" <td>37.0</td>\n",
" <td>Self Enquiry</td>\n",
" <td>1</td>\n",
" <td>8.0</td>\n",
" <td>Free Lancer</td>\n",
" <td>Male</td>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
" <td>Basic</td>\n",
" <td>3.0</td>\n",
" <td>Single</td>\n",
" <td>7.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>Executive</td>\n",
" <td>17090.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>200003</td>\n",
" <td>0</td>\n",
" <td>33.0</td>\n",
" <td>Company Invited</td>\n",
" <td>1</td>\n",
" <td>9.0</td>\n",
" <td>Salaried</td>\n",
" <td>Female</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>Basic</td>\n",
" <td>3.0</td>\n",
" <td>Divorced</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>Executive</td>\n",
" <td>17909.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200004</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>Self Enquiry</td>\n",
" <td>1</td>\n",
" <td>8.0</td>\n",
" <td>Small Business</td>\n",
" <td>Male</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>Basic</td>\n",
" <td>4.0</td>\n",
" <td>Divorced</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>Executive</td>\n",
" <td>18468.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>200005</td>\n",
" <td>0</td>\n",
" <td>32.0</td>\n",
" <td>Company Invited</td>\n",
" <td>1</td>\n",
" <td>8.0</td>\n",
" <td>Salaried</td>\n",
" <td>Male</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" <td>Basic</td>\n",
" <td>3.0</td>\n",
" <td>Single</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>Executive</td>\n",
" <td>18068.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>200006</td>\n",
" <td>0</td>\n",
" <td>59.0</td>\n",
" <td>Self Enquiry</td>\n",
" <td>1</td>\n",
" <td>9.0</td>\n",
" <td>Small Business</td>\n",
" <td>Female</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" <td>Basic</td>\n",
" <td>5.0</td>\n",
" <td>Divorced</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>Executive</td>\n",
" <td>17670.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>200007</td>\n",
" <td>0</td>\n",
" <td>30.0</td>\n",
" <td>Self Enquiry</td>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>Salaried</td>\n",
" <td>Male</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" <td>Basic</td>\n",
" <td>3.0</td>\n",
" <td>Married</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>Executive</td>\n",
" <td>17693.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>200008</td>\n",
" <td>0</td>\n",
" <td>38.0</td>\n",
" <td>Company Invited</td>\n",
" <td>1</td>\n",
" <td>29.0</td>\n",
" <td>Salaried</td>\n",
" <td>Male</td>\n",
" <td>2</td>\n",
" <td>4.0</td>\n",
" <td>Standard</td>\n",
" <td>3.0</td>\n",
" <td>Unmarried</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>Senior Manager</td>\n",
" <td>24526.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>200009</td>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>Self Enquiry</td>\n",
" <td>1</td>\n",
" <td>33.0</td>\n",
" <td>Small Business</td>\n",
" <td>Male</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" <td>Deluxe</td>\n",
" <td>3.0</td>\n",
" <td>Divorced</td>\n",
" <td>7.0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>Manager</td>\n",
" <td>20237.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CustomerID ProdTaken Age TypeofContact CityTier DurationOfPitch \\\n",
"0 200000 1 41.0 Self Enquiry 3 6.0 \n",
"1 200001 0 49.0 Company Invited 1 14.0 \n",
"2 200002 1 37.0 Self Enquiry 1 8.0 \n",
"3 200003 0 33.0 Company Invited 1 9.0 \n",
"4 200004 0 NaN Self Enquiry 1 8.0 \n",
"5 200005 0 32.0 Company Invited 1 8.0 \n",
"6 200006 0 59.0 Self Enquiry 1 9.0 \n",
"7 200007 0 30.0 Self Enquiry 1 30.0 \n",
"8 200008 0 38.0 Company Invited 1 29.0 \n",
"9 200009 0 36.0 Self Enquiry 1 33.0 \n",
"\n",
" Occupation Gender NumberOfPersonVisiting NumberOfFollowups \\\n",
"0 Salaried Female 3 3.0 \n",
"1 Salaried Male 3 4.0 \n",
"2 Free Lancer Male 3 4.0 \n",
"3 Salaried Female 2 3.0 \n",
"4 Small Business Male 2 3.0 \n",
"5 Salaried Male 3 3.0 \n",
"6 Small Business Female 2 2.0 \n",
"7 Salaried Male 3 3.0 \n",
"8 Salaried Male 2 4.0 \n",
"9 Small Business Male 3 3.0 \n",
"\n",
" ProductPitched PreferredPropertyStar MaritalStatus NumberOfTrips \\\n",
"0 Deluxe 3.0 Single 1.0 \n",
"1 Deluxe 4.0 Divorced 2.0 \n",
"2 Basic 3.0 Single 7.0 \n",
"3 Basic 3.0 Divorced 2.0 \n",
"4 Basic 4.0 Divorced 1.0 \n",
"5 Basic 3.0 Single 1.0 \n",
"6 Basic 5.0 Divorced 5.0 \n",
"7 Basic 3.0 Married 2.0 \n",
"8 Standard 3.0 Unmarried 1.0 \n",
"9 Deluxe 3.0 Divorced 7.0 \n",
"\n",
" Passport PitchSatisfactionScore OwnCar NumberOfChildrenVisiting \\\n",
"0 1 2 1 0.0 \n",
"1 0 3 1 2.0 \n",
"2 1 3 0 0.0 \n",
"3 1 5 1 1.0 \n",
"4 0 5 1 0.0 \n",
"5 0 5 1 1.0 \n",
"6 1 2 1 1.0 \n",
"7 0 2 0 1.0 \n",
"8 0 3 0 0.0 \n",
"9 0 3 1 0.0 \n",
"\n",
" Designation MonthlyIncome \n",
"0 Manager 20993.0 \n",
"1 Manager 20130.0 \n",
"2 Executive 17090.0 \n",
"3 Executive 17909.0 \n",
"4 Executive 18468.0 \n",
"5 Executive 18068.0 \n",
"6 Executive 17670.0 \n",
"7 Executive 17693.0 \n",
"8 Senior Manager 24526.0 \n",
"9 Manager 20237.0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cbcdc2b9",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
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" <th>top</th>\n",
" <th>freq</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>CustomerID</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>202443.5</td>\n",
" <td>1411.188388</td>\n",
" <td>200000.0</td>\n",
" <td>201221.75</td>\n",
" <td>202443.5</td>\n",
" <td>203665.25</td>\n",
" <td>204887.0</td>\n",
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" <tr>\n",
" <th>ProdTaken</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.188216</td>\n",
" <td>0.390925</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>4662.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>37.622265</td>\n",
" <td>9.316387</td>\n",
" <td>18.0</td>\n",
" <td>31.0</td>\n",
" <td>36.0</td>\n",
" <td>44.0</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TypeofContact</th>\n",
" <td>4863</td>\n",
" <td>2</td>\n",
" <td>Self Enquiry</td>\n",
" <td>3444</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CityTier</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.654255</td>\n",
" <td>0.916583</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DurationOfPitch</th>\n",
" <td>4637.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.490835</td>\n",
" <td>8.519643</td>\n",
" <td>5.0</td>\n",
" <td>9.0</td>\n",
" <td>13.0</td>\n",
" <td>20.0</td>\n",
" <td>127.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Occupation</th>\n",
" <td>4888</td>\n",
" <td>4</td>\n",
" <td>Salaried</td>\n",
" <td>2368</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gender</th>\n",
" <td>4888</td>\n",
" <td>3</td>\n",
" <td>Male</td>\n",
" <td>2916</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfPersonVisiting</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.905074</td>\n",
" <td>0.724891</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfFollowups</th>\n",
" <td>4843.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.708445</td>\n",
" <td>1.002509</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ProductPitched</th>\n",
" <td>4888</td>\n",
" <td>5</td>\n",
" <td>Basic</td>\n",
" <td>1842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PreferredPropertyStar</th>\n",
" <td>4862.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.581037</td>\n",
" <td>0.798009</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MaritalStatus</th>\n",
" <td>4888</td>\n",
" <td>4</td>\n",
" <td>Married</td>\n",
" <td>2340</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfTrips</th>\n",
" <td>4748.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.236521</td>\n",
" <td>1.849019</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Passport</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.290917</td>\n",
" <td>0.454232</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PitchSatisfactionScore</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.078151</td>\n",
" <td>1.365792</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OwnCar</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.620295</td>\n",
" <td>0.485363</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfChildrenVisiting</th>\n",
" <td>4822.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.187267</td>\n",
" <td>0.857861</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Designation</th>\n",
" <td>4888</td>\n",
" <td>5</td>\n",
" <td>Executive</td>\n",
" <td>1842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MonthlyIncome</th>\n",
" <td>4655.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23619.853491</td>\n",
" <td>5380.698361</td>\n",
" <td>1000.0</td>\n",
" <td>20346.0</td>\n",
" <td>22347.0</td>\n",
" <td>25571.0</td>\n",
" <td>98678.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count unique top freq mean \\\n",
"CustomerID 4888.0 NaN NaN NaN 202443.5 \n",
"ProdTaken 4888.0 NaN NaN NaN 0.188216 \n",
"Age 4662.0 NaN NaN NaN 37.622265 \n",
"TypeofContact 4863 2 Self Enquiry 3444 NaN \n",
"CityTier 4888.0 NaN NaN NaN 1.654255 \n",
"DurationOfPitch 4637.0 NaN NaN NaN 15.490835 \n",
"Occupation 4888 4 Salaried 2368 NaN \n",
"Gender 4888 3 Male 2916 NaN \n",
"NumberOfPersonVisiting 4888.0 NaN NaN NaN 2.905074 \n",
"NumberOfFollowups 4843.0 NaN NaN NaN 3.708445 \n",
"ProductPitched 4888 5 Basic 1842 NaN \n",
"PreferredPropertyStar 4862.0 NaN NaN NaN 3.581037 \n",
"MaritalStatus 4888 4 Married 2340 NaN \n",
"NumberOfTrips 4748.0 NaN NaN NaN 3.236521 \n",
"Passport 4888.0 NaN NaN NaN 0.290917 \n",
"PitchSatisfactionScore 4888.0 NaN NaN NaN 3.078151 \n",
"OwnCar 4888.0 NaN NaN NaN 0.620295 \n",
"NumberOfChildrenVisiting 4822.0 NaN NaN NaN 1.187267 \n",
"Designation 4888 5 Executive 1842 NaN \n",
"MonthlyIncome 4655.0 NaN NaN NaN 23619.853491 \n",
"\n",
" std min 25% 50% \\\n",
"CustomerID 1411.188388 200000.0 201221.75 202443.5 \n",
"ProdTaken 0.390925 0.0 0.0 0.0 \n",
"Age 9.316387 18.0 31.0 36.0 \n",
"TypeofContact NaN NaN NaN NaN \n",
"CityTier 0.916583 1.0 1.0 1.0 \n",
"DurationOfPitch 8.519643 5.0 9.0 13.0 \n",
"Occupation NaN NaN NaN NaN \n",
"Gender NaN NaN NaN NaN \n",
"NumberOfPersonVisiting 0.724891 1.0 2.0 3.0 \n",
"NumberOfFollowups 1.002509 1.0 3.0 4.0 \n",
"ProductPitched NaN NaN NaN NaN \n",
"PreferredPropertyStar 0.798009 3.0 3.0 3.0 \n",
"MaritalStatus NaN NaN NaN NaN \n",
"NumberOfTrips 1.849019 1.0 2.0 3.0 \n",
"Passport 0.454232 0.0 0.0 0.0 \n",
"PitchSatisfactionScore 1.365792 1.0 2.0 3.0 \n",
"OwnCar 0.485363 0.0 0.0 1.0 \n",
"NumberOfChildrenVisiting 0.857861 0.0 1.0 1.0 \n",
"Designation NaN NaN NaN NaN \n",
"MonthlyIncome 5380.698361 1000.0 20346.0 22347.0 \n",
"\n",
" 75% max \n",
"CustomerID 203665.25 204887.0 \n",
"ProdTaken 0.0 1.0 \n",
"Age 44.0 61.0 \n",
"TypeofContact NaN NaN \n",
"CityTier 3.0 3.0 \n",
"DurationOfPitch 20.0 127.0 \n",
"Occupation NaN NaN \n",
"Gender NaN NaN \n",
"NumberOfPersonVisiting 3.0 5.0 \n",
"NumberOfFollowups 4.0 6.0 \n",
"ProductPitched NaN NaN \n",
"PreferredPropertyStar 4.0 5.0 \n",
"MaritalStatus NaN NaN \n",
"NumberOfTrips 4.0 22.0 \n",
"Passport 1.0 1.0 \n",
"PitchSatisfactionScore 4.0 5.0 \n",
"OwnCar 1.0 1.0 \n",
"NumberOfChildrenVisiting 2.0 3.0 \n",
"Designation NaN NaN \n",
"MonthlyIncome 25571.0 98678.0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Display basic descriptive statistics around the data\n",
"data.describe(include=\"all\").T"
]
},
{
"cell_type": "markdown",
"id": "68c0f73b",
"metadata": {},
"source": [
"## A. **Data Preprocessing**\n",
"#### 1. First, Check if all columns contain important data to be analysed and for the model that will be built later"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b4e16e03",
"metadata": {},
"outputs": [],
"source": [
"#Drop the ID column as it is a duplicate to the Dataframe index\n",
"data.drop('CustomerID',axis=1,inplace=True)"
]
},
{
"cell_type": "markdown",
"id": "7118ee42",
"metadata": {},
"source": [
"#### 2. Check the column Data types and its applicability for analysis / transformation"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2b2afbd3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 4888 entries, 0 to 4887\n",
"Data columns (total 19 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 ProdTaken 4888 non-null int64 \n",
" 1 Age 4662 non-null float64\n",
" 2 TypeofContact 4863 non-null object \n",
" 3 CityTier 4888 non-null int64 \n",
" 4 DurationOfPitch 4637 non-null float64\n",
" 5 Occupation 4888 non-null object \n",
" 6 Gender 4888 non-null object \n",
" 7 NumberOfPersonVisiting 4888 non-null int64 \n",
" 8 NumberOfFollowups 4843 non-null float64\n",
" 9 ProductPitched 4888 non-null object \n",
" 10 PreferredPropertyStar 4862 non-null float64\n",
" 11 MaritalStatus 4888 non-null object \n",
" 12 NumberOfTrips 4748 non-null float64\n",
" 13 Passport 4888 non-null int64 \n",
" 14 PitchSatisfactionScore 4888 non-null int64 \n",
" 15 OwnCar 4888 non-null int64 \n",
" 16 NumberOfChildrenVisiting 4822 non-null float64\n",
" 17 Designation 4888 non-null object \n",
" 18 MonthlyIncome 4655 non-null float64\n",
"dtypes: float64(7), int64(6), object(6)\n",
"memory usage: 725.7+ KB\n"
]
}
],
"source": [
"#using info method to check on available datapoints and their type\n",
"data.info()"
]
},
{
"cell_type": "markdown",
"id": "9bb56c7d",
"metadata": {},
"source": [
"#### 3. Imputing/Dropping for missing values"
]
},
{
"cell_type": "markdown",
"id": "91f9f5f2",
"metadata": {},
"source": [
"Imputing missing values in Age based on designation median age."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7dcd3c8a",
"metadata": {},
"outputs": [],
"source": [
"M_age = data[data['Designation']== 'Manager']['Age'].median()\n",
"E_age = data[data['Designation']== 'Executive']['Age'].median()\n",
"SM_age = data[data['Designation']== 'Senior Manager']['Age'].median()\n",
"A_age = data[data['Designation']== 'AVP']['Age'].median()\n",
"VP_age = data[data['Designation']== 'VP']['Age'].median()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c96d6ebc",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' for 'Age' based on Designation level\n",
"indx_a = list(data[data['Age'].isnull()].index)\n",
"for i in indx_a:\n",
" if data['Designation'].iloc[i] == 'Manager':\n",
" data['Age'].iloc[i] = M_age\n",
" if data['Designation'].iloc[i] == 'Executive':\n",
" data['Age'].iloc[i] = E_age\n",
" if data['Designation'].iloc[i] == 'Senior Manager':\n",
" data['Age'].iloc[i] = SM_age\n",
" if data['Designation'].iloc[i] == 'AVP':\n",
" data['Age'].iloc[i] = A_age\n",
" else:\n",
" data['Age'].iloc[i] = VP_age"
]
},
{
"cell_type": "markdown",
"id": "27cc7c1a",
"metadata": {},
"source": [
"Imputing the Type of Contact with mode value for missing values"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "12b4b483",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Self Enquiry\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#identifying the mode in Type of Contact\n",
"data['TypeofContact'].mode()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a57112f1",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' in type of contact with mode captured in the above cell\n",
"indxt = list(data[data['TypeofContact'].isnull()].index)\n",
"for i in indxt:\n",
" data['TypeofContact'].iloc[i] = 'Self Enquiry'"
]
},
{
"cell_type": "markdown",
"id": "517a335d",
"metadata": {},
"source": [
"Imputing the pitch duration with median value for missing values"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "441c71cd",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' instead of zero Pitch duration\n",
"indxd = list(data[data['DurationOfPitch'].isnull()].index)\n",
"for i in indxd:\n",
" data['DurationOfPitch'].iloc[i] = data['DurationOfPitch'].median()"
]
},
{
"cell_type": "markdown",
"id": "1abb048f",
"metadata": {},
"source": [
"It was noticed that some rows have Gender as 'Fe male' instead of 'Female', this will be imputed to have data consistency"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ce781b84",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Fe Male' instead of 'Female'\n",
"indx = list(data[data['Gender'] == 'Fe Male'].index)\n",
"for i in indx:\n",
" data['Gender'].iloc[i] = 'Female'"
]
},
{
"cell_type": "markdown",
"id": "ca30abc8",
"metadata": {},
"source": [
"It was noticed that some rows have null as 'NumberOfFollowups', this will be imputed by 0.0 for data type consistency"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "02e8fb04",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' instead of zero in number of followups\n",
"indxn = list(data[data['NumberOfFollowups'].isnull()].index)\n",
"for i in indxn:\n",
" data['NumberOfFollowups'].iloc[i] = 0.0"
]
},
{
"cell_type": "markdown",
"id": "41b10888",
"metadata": {},
"source": [
"Imputing prefered propety star with mode value for missing values"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "fa6513ef",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' in prefered property star rating\n",
"indxpp = list(data[data['PreferredPropertyStar'].isnull()].index)\n",
"for i in indxpp:\n",
" data['PreferredPropertyStar'].iloc[i] = data['PreferredPropertyStar'].mode()"
]
},
{
"cell_type": "markdown",
"id": "c5375da4",
"metadata": {},
"source": [
"Imputing of missing values for 'NumberOfTrips' based on number of visiting children as they are having the highest corelation with number of trips"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e4c1226c",
"metadata": {},
"outputs": [],
"source": [
"#creating several bands for number of trips based on number of children customer have.\n",
"ch_0 = data[data['NumberOfChildrenVisiting']== 0.0]['NumberOfTrips'].mode()\n",
"ch_1 = data[data['NumberOfChildrenVisiting']== 1.0]['NumberOfTrips'].mode()\n",
"ch_2 = data[data['NumberOfChildrenVisiting']== 2.0]['NumberOfTrips'].mode()\n",
"ch_3 = data[data['NumberOfChildrenVisiting']== 3.0]['NumberOfTrips'].mode()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "32fc4e82",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' for 'NumberOfTtips' based on number of children visiting\n",
"indx_ch = list(data[data['NumberOfTrips'].isnull()].index)\n",
"for i in indx_ch:\n",
" if data['NumberOfChildrenVisiting'].iloc[i] == 0.0:\n",
" data['NumberOfTrips'].iloc[i] = ch_0\n",
" if data['NumberOfChildrenVisiting'].iloc[i] == 1.0:\n",
" data['NumberOfTrips'].iloc[i] = ch_1\n",
" if data['NumberOfChildrenVisiting'].iloc[i] == 2.0:\n",
" data['NumberOfTrips'].iloc[i] = ch_2\n",
" else:\n",
" data['NumberOfTrips'].iloc[i] = ch_3"
]
},
{
"cell_type": "markdown",
"id": "9726b0c2",
"metadata": {},
"source": [
"Imputing of missing values for 'NumberOfChildrenVisiting' based on marital status"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a5fc3b13",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' for Visiting children\n",
"indxvc = list(data[data['NumberOfChildrenVisiting'].isnull()].index)\n",
"for i in indxvc:\n",
" if data['MaritalStatus'].iloc[i] == 'Single':\n",
" data['NumberOfChildrenVisiting'].iloc[i] = 0.0\n",
" else:\n",
" data['NumberOfChildrenVisiting'].iloc[i] = data['NumberOfChildrenVisiting'].mode()"
]
},
{
"cell_type": "markdown",
"id": "661218e5",
"metadata": {},
"source": [
"Imputing Monthly income based on designation median salary"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "96984e2a",
"metadata": {},
"outputs": [],
"source": [
"#creating several salary bands according to Designation type.\n",
"M_sal = data[data['Designation']== 'Manager']['MonthlyIncome'].median()\n",
"E_sal = data[data['Designation']== 'Executive']['MonthlyIncome'].median()\n",
"SM_sal = data[data['Designation']== 'Senior Manager']['MonthlyIncome'].median()\n",
"A_sal = data[data['Designation']== 'AVP']['MonthlyIncome'].median()\n",
"VP_sal = data[data['Designation']== 'VP']['MonthlyIncome'].median()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ba37982c",
"metadata": {},
"outputs": [],
"source": [
"#Identifying and imputing rows having 'Nan' for 'MonthlyIncome' based on Designation level\n",
"indx_s = list(data[data['MonthlyIncome'].isnull()].index)\n",
"for i in indx_s:\n",
" if data['Designation'].iloc[i] == 'Manager':\n",
" data['MonthlyIncome'].iloc[i] = M_sal\n",
" if data['Designation'].iloc[i] == 'Executive':\n",
" data['MonthlyIncome'].iloc[i] = E_sal\n",
" if data['Designation'].iloc[i] == 'Senior Manager':\n",
" data['MonthlyIncome'].iloc[i] = SM_sal\n",
" if data['Designation'].iloc[i] == 'AVP':\n",
" data['MonthlyIncome'].iloc[i] = A_sal\n",
" else:\n",
" data['MonthlyIncome'].iloc[i] = VP_sal"
]
},
{
"cell_type": "markdown",
"id": "bce3970a",
"metadata": {},
"source": [
"## B. **EDA**"
]
},
{
"cell_type": "markdown",
"id": "82294f53",
"metadata": {},
"source": [
"#### Univariate analysis"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a623c935",
"metadata": {},
"outputs": [],
"source": [
"# function to create labeled barplots\n",
"\n",
"\n",
"def labeled_barplot(data, feature, perc=False, n=None):\n",
" \"\"\"\n",
" Barplot with percentage at the top\n",
"\n",
" data: dataframe\n",
" feature: dataframe column\n",
" perc: whether to display percentages instead of count (default is False)\n",
" n: displays the top n category levels (default is None, i.e., display all levels)\n",
" \"\"\"\n",
"\n",
" total = len(data[feature]) # length of the column\n",
" count = data[feature].nunique()\n",
" if n is None:\n",
" plt.figure(figsize=(count + 2, 6))\n",
" else:\n",
" plt.figure(figsize=(n + 2, 6))\n",
"\n",
" plt.xticks(rotation=90, fontsize=15)\n",
" ax = sns.countplot(\n",
" data=data,\n",
" x=feature,\n",
" palette=\"Paired\",\n",
" order=data[feature].value_counts().index[:n].sort_values(),\n",
" )\n",
"\n",
" for p in ax.patches:\n",
" if perc == True:\n",
" label = \"{:.1f}%\".format(\n",
" 100 * p.get_height() / total\n",
" ) # percentage of each class of the category\n",
" else:\n",
" label = p.get_height() # count of each level of the category\n",
"\n",
" x = p.get_x() + p.get_width() / 2 # width of the plot\n",
" y = p.get_height() # height of the plot\n",
"\n",
" ax.annotate(\n",
" label,\n",
" (x, y),\n",
" ha=\"center\",\n",
" va=\"center\",\n",
" size=12,\n",
" xytext=(0, 5),\n",
" textcoords=\"offset points\",\n",
" ) # annotate the percentage\n",
"\n",
" plt.show() # show the plot"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "0c602b4f",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>unique</th>\n",
" <th>top</th>\n",
" <th>freq</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ProdTaken</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.188216</td>\n",
" <td>0.390925</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>38.148322</td>\n",
" <td>9.406965</td>\n",
" <td>18.0</td>\n",
" <td>31.0</td>\n",
" <td>37.0</td>\n",
" <td>45.0</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TypeofContact</th>\n",
" <td>4888</td>\n",
" <td>2</td>\n",
" <td>Self Enquiry</td>\n",
" <td>3469</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CityTier</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.654255</td>\n",
" <td>0.916583</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DurationOfPitch</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>15.36293</td>\n",
" <td>8.316166</td>\n",
" <td>5.0</td>\n",
" <td>9.0</td>\n",
" <td>13.0</td>\n",
" <td>19.0</td>\n",
" <td>127.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Occupation</th>\n",
" <td>4888</td>\n",
" <td>4</td>\n",
" <td>Salaried</td>\n",
" <td>2368</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gender</th>\n",
" <td>4888</td>\n",
" <td>2</td>\n",
" <td>Male</td>\n",
" <td>2916</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfPersonVisiting</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.905074</td>\n",
" <td>0.724891</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfFollowups</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.674304</td>\n",
" <td>1.058886</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ProductPitched</th>\n",
" <td>4888</td>\n",
" <td>5</td>\n",
" <td>Basic</td>\n",
" <td>1842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PreferredPropertyStar</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.577946</td>\n",
" <td>0.797005</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MaritalStatus</th>\n",
" <td>4888</td>\n",
" <td>4</td>\n",
" <td>Married</td>\n",
" <td>2340</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfTrips</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.229746</td>\n",
" <td>1.822769</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Passport</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.290917</td>\n",
" <td>0.454232</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PitchSatisfactionScore</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.078151</td>\n",
" <td>1.365792</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OwnCar</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.620295</td>\n",
" <td>0.485363</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NumberOfChildrenVisiting</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.180646</td>\n",
" <td>0.855595</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Designation</th>\n",
" <td>4888</td>\n",
" <td>5</td>\n",
" <td>Executive</td>\n",
" <td>1842</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MonthlyIncome</th>\n",
" <td>4888.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24162.27189</td>\n",
" <td>5783.666925</td>\n",
" <td>1000.0</td>\n",
" <td>20485.0</td>\n",
" <td>22655.0</td>\n",
" <td>26728.0</td>\n",
" <td>98678.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count unique top freq mean \\\n",
"ProdTaken 4888.0 NaN NaN NaN 0.188216 \n",
"Age 4888.0 NaN NaN NaN 38.148322 \n",
"TypeofContact 4888 2 Self Enquiry 3469 NaN \n",
"CityTier 4888.0 NaN NaN NaN 1.654255 \n",
"DurationOfPitch 4888.0 NaN NaN NaN 15.36293 \n",
"Occupation 4888 4 Salaried 2368 NaN \n",
"Gender 4888 2 Male 2916 NaN \n",
"NumberOfPersonVisiting 4888.0 NaN NaN NaN 2.905074 \n",
"NumberOfFollowups 4888.0 NaN NaN NaN 3.674304 \n",
"ProductPitched 4888 5 Basic 1842 NaN \n",
"PreferredPropertyStar 4888.0 NaN NaN NaN 3.577946 \n",
"MaritalStatus 4888 4 Married 2340 NaN \n",
"NumberOfTrips 4888.0 NaN NaN NaN 3.229746 \n",
"Passport 4888.0 NaN NaN NaN 0.290917 \n",
"PitchSatisfactionScore 4888.0 NaN NaN NaN 3.078151 \n",
"OwnCar 4888.0 NaN NaN NaN 0.620295 \n",
"NumberOfChildrenVisiting 4888.0 NaN NaN NaN 1.180646 \n",
"Designation 4888 5 Executive 1842 NaN \n",
"MonthlyIncome 4888.0 NaN NaN NaN 24162.27189 \n",
"\n",
" std min 25% 50% 75% \\\n",
"ProdTaken 0.390925 0.0 0.0 0.0 0.0 \n",
"Age 9.406965 18.0 31.0 37.0 45.0 \n",
"TypeofContact NaN NaN NaN NaN NaN \n",
"CityTier 0.916583 1.0 1.0 1.0 3.0 \n",
"DurationOfPitch 8.316166 5.0 9.0 13.0 19.0 \n",
"Occupation NaN NaN NaN NaN NaN \n",
"Gender NaN NaN NaN NaN NaN \n",
"NumberOfPersonVisiting 0.724891 1.0 2.0 3.0 3.0 \n",
"NumberOfFollowups 1.058886 0.0 3.0 4.0 4.0 \n",
"ProductPitched NaN NaN NaN NaN NaN \n",
"PreferredPropertyStar 0.797005 3.0 3.0 3.0 4.0 \n",
"MaritalStatus NaN NaN NaN NaN NaN \n",
"NumberOfTrips 1.822769 1.0 2.0 3.0 4.0 \n",
"Passport 0.454232 0.0 0.0 0.0 1.0 \n",
"PitchSatisfactionScore 1.365792 1.0 2.0 3.0 4.0 \n",
"OwnCar 0.485363 0.0 0.0 1.0 1.0 \n",
"NumberOfChildrenVisiting 0.855595 0.0 1.0 1.0 2.0 \n",
"Designation NaN NaN NaN NaN NaN \n",
"MonthlyIncome 5783.666925 1000.0 20485.0 22655.0 26728.0 \n",
"\n",
" max \n",
"ProdTaken 1.0 \n",
"Age 61.0 \n",
"TypeofContact NaN \n",
"CityTier 3.0 \n",
"DurationOfPitch 127.0 \n",
"Occupation NaN \n",
"Gender NaN \n",
"NumberOfPersonVisiting 5.0 \n",
"NumberOfFollowups 6.0 \n",
"ProductPitched NaN \n",
"PreferredPropertyStar 5.0 \n",
"MaritalStatus NaN \n",
"NumberOfTrips 22.0 \n",
"Passport 1.0 \n",
"PitchSatisfactionScore 5.0 \n",
"OwnCar 1.0 \n",
"NumberOfChildrenVisiting 3.0 \n",
"Designation NaN \n",
"MonthlyIncome 98678.0 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Display basic descriptive statistics after pre-processing\n",
"data.describe(include=\"all\").T"
]
},
{
"cell_type": "markdown",
"id": "a23fbf82",
"metadata": {},
"source": [
"From the above table, we can notice the following\n",
"- Less than 25% have taken the `PodTaken`\n",
"- 75% of customer base are 45 of `Age` and below.\n",
"- Self inquiry is the prefered `TypeofContact`\n",
"- 75% of customers listend to a `DurationOfPitch` 19 minutes.\n",
"- 75% of customers have `NumberOfPersonVisiting` up to 3 persons.\n",
"- 75% of customer had 4 `NumberOfFollowups`\n",
"- 75% of customers had `NumberOfTrips` from 1 to 4\n",
"- Executive `Designation` is the most freqeunt in the customer data base\n",
"- Customer database have a mean `MonthlyIncome` of 24,162"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "6ac6009d",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 288x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'ProdTaken', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "28bbf80a",
"metadata": {},
"source": [
"18.8% of customer database have taken the company products. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "0fce4e2c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 3466\n",
"1 1422\n",
"Name: Passport, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Passport'].value_counts()"
]
},
{
"cell_type": "markdown",
"id": "86fbea7a",
"metadata": {},
"source": [
"Majority of customers don't have passports."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "3fcc187c",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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2kMLs3TkoxlXslkRJAwAg1ljuBAAAMBAlDQAAwECUNAAAAANR0gAAAAxka0nbsGGDPB6PPB6P1qxZI0l69dVX5fF4VFBQoOeee06WNXBngLa2NpWVlSk/P18VFRXq6emxMyYAAIBxbCtpH3zwgfbt26eGhgZ5vV59+umneumll/TSSy9p+/bteuutt/Txxx/r/fffHzC2pqZGpaWl8vv9ysrKUl1dnV0xAQAAjGRbSXO73aqsrNSwYcPkdDo1duxYORwOvf322xoxYoROnTql7u5ujRo1KmpcOBxWS0uL8vLyJEnFxcXy+/12xQQAADCSbSXtpptu0vjx4yVJR44c0d69ezVx4kQ5nU7V19fr7rvvltvt1i233BI1rqurSy6XSykp517h5na71dHRYVdMAAAAI9n+4MAXX3yh+fPna/ny5brxxhslSbNnz9ZHH32ka6+9Vhs2bIi637IsORyOqHPnHwMAACQ6W0va/v37NW/ePC1dulQzZ85Ue3u79u/fL0lKSUmRx+PR559/HjUmLS1NwWBQkUhEkhQIBJSRkWFnTAAAAOPYVtLa29u1cOFC1dbWyuPxSJKCwaCWLVumU6dOybIsvfPOO7rtttuixjmdTmVnZ8vn80mSvF6vcnNz7YoJAABgJNv27ty8ebN6e3u1evXq/nNz5szRww8/rDlz5ig5OVnZ2dl68MEHJUlVVVWaPHmypkyZourqalVWVmrTpk3KzMzU2rVr7YoJAABgJId1oReVXeXYYH3wjKvYrUAgeNHrbnfqJa9j8DDXscNcxw5zHVvMd+xczlwnJTmUnu66+PXBDgUAAIArR0kDAAAwECUNAADAQJQ0AAAAA1HSAAAADGTbKzji6U//6ufxjpAwzoZ64x0BAIAhKSFLWmdnt/r6Eu7NIgAAYAhhuRMAAMBAlDQAAAADUdIAAAAMlJC/SbvUFgsYfG536lfeczbUq66ToRikAQAgMSRkSdv/6nz1drN3p0n+n4f3SKKkAQBwuVjuBAAAMBAlDQAAwECUNAAAAANR0gAAAAxka0nbsGGDPB6PPB6P1qxZE3Vt27ZtKi8vv+C4trY2lZWVKT8/XxUVFerp6bEzJgAAgHFsK2kffPCB9u3bp4aGBnm9Xn366af65S9/KUn69a9/rRdffPGiY2tqalRaWiq/36+srCzV1dXZFRMAAMBItpU0t9utyspKDRs2TE6nU2PHjlVbW5tCoZBWrlypRYsWXXBcOBxWS0uL8vLyJEnFxcXy+/12xQQAADCSbe9Ju+mmm/r/feTIEe3du1evvfaafvrTn6qkpETXX3/9Bcd1dXXJ5XIpJeVcNLfbrY6ODrtiAgAAGMn2Bwe++OILzZ8/X8uXL9fvfvc7tbe3q6Sk5KL3W5Ylh8MRde78YwAAgERna0nbv3+/5s2bp6VLl2rmzJnas2ePvvjiCxUVFWnFihX65JNPtHjx4qgxaWlpCgaDikQikqRAIKCMjAw7YwIAABjHtuXO9vZ2LVy4UOvWrVNOTo4k6dlnn+2//tFHH2nDhg16/vnno8Y5nU5lZ2fL5/OpsLBQXq9Xubm5dsUEAAAwkm3fpG3evFm9vb1avXq1ioqKVFRUpNdee+2i91dVVamxsVGSVF1drfr6ehUUFKi1tXXAt20AAACJzmFZlhXvEIONDdbN8/88vEeBQDDeMa5qbncqcxgjzHXsMNexxXzHzuXMdVKSQ+nprotfH+xQAAAAuHKUNAAAAANR0gAAAAxESQMAADAQJQ0AAMBAtr0nLZ5uK/2neEfAec6GeuMdAQCAq0pClrTOzm719SXcm0WMxOPcAADYg+VOAAAAA1HSAAAADERJAwAAMFBC/ibtUlssYPC53anxjnBZwqFenTgZincMAAAuS0KWtPdff1Bn2LsT55ny0NuSKGkAgKsDy50AAAAGoqQBAAAYiJIGAABgIEoaAACAgWx9cGDDhg3au3evJGnixIlavny5nnjiCe3fv1/Dhw+XJP3oRz/SPffcEzWura1Ny5YtU2dnp8aMGaPa2lqNHDnSzqgAAABGsa2kffDBB9q3b58aGhrkcDj00EMP6Ze//KU++eQTbdu2TRkZGRcdW1NTo9LSUnk8Hm3cuFF1dXVatmyZXVEBAACMY9typ9vtVmVlpYYNGyan06mxY8eqra1NbW1tevLJJ1VYWKj169err68valw4HFZLS4vy8vIkScXFxfL7/XbFBAAAMJJtJe2mm27S+PHjJUlHjhzR3r179Rd/8Re688479cwzz6i+vl6tra3asWNH1Liuri65XC6lpJz7ks/tdqujo8OumAAAAEay/cGBL774QvPnz9fy5cv17W9/Wxs3blRGRoaGDx+u8vJyvffee1H3W5Ylh8MRde78YwAAgERna0nbv3+/5s2bp6VLl2rmzJn6/PPP9c477/Rftyyr/xuz/5SWlqZgMKhIJCJJCgQCl/z9GgAAQCKyraS1t7dr4cKFqq2tlcfjkXSulD3zzDM6efKkwuGw3njjjQFPdjqdTmVnZ8vn80mSvF6vcnNz7YoJAABgJNue7ty8ebN6e3u1evXq/nNz5szRww8/rPvvv19nz57V1KlTNW3aNElSVVWVJk+erClTpqi6ulqVlZXatGmTMjMztXbtWrtiAgAAGMlhWZYV7xCDjQ3WcSFTHnpbgUAw3jH+aG536lWd/2rCXMcOcx1bzHfsXM5cJyU5lJ7uuvj1wQ4FAACAK0dJAwAAMBAlDQAAwECUNAAAAAPZusF6vNw1Z0u8I8BA4VBvvCMAAHDZErKkdXZ2q68v4R5aNRJPCgEAYA+WOwEAAAxESQMAADAQJQ0AAMBACfmbtEu9vReDz+1OjXeEIeNy5zoc6tWJkyGb0wAA7JSQJa2x/gF9ybZQGMKmzd8riZIGAFczljsBAAAMREkDAAAwECUNAADAQJQ0AAAAA9la0jZs2CCPxyOPx6M1a9ZIkj7++GPNnj1bHo9Hjz/+uEKhgT9ubmtrU1lZmfLz81VRUaGenh47YwIAABjHtpL2wQcfaN++fWpoaJDX69Wnn36qhoYGPfroo1q1apXefvttSdKOHTsGjK2pqVFpaan8fr+ysrJUV1dnV0wAAAAj2VbS3G63KisrNWzYMDmdTo0dO1a/+93vNH78eN1yyy2SpBUrVuiee+6JGhcOh9XS0qK8vDxJUnFxsfx+v10xAQAAjGRbSbvppps0fvx4SdKRI0e0d+9eDRs2TCNGjNCSJUtUVFSkf/iHf9CoUaOixnV1dcnlcikl5dwr3Nxutzo6OuyKCQAAYCTbHxz44osvNH/+fC1fvlyRSET79u3T448/rp07d+rLL7/Uiy++GHW/ZVlyOBxR584/BgAASHS2lrT9+/dr3rx5Wrp0qWbOnKlrr71W48aN0+jRo5WcnKx7771XBw8ejBqTlpamYDCoSCQiSQoEAsrIyLAzJgAAgHFsK2nt7e1auHChamtr5fF4JEl//ud/rk8//VTt7e2SpHfffVe33npr1Din06ns7Gz5fD5JktfrVW5url0xAQAAjGTb3p2bN29Wb2+vVq9e3X9uzpw5WrVqlRYsWKDe3l5997vf1d/93d9JkqqqqjR58mRNmTJF1dXVqqys1KZNm5SZmam1a9faFRMAAMBIDsuyrHiHGGxssI6hbtr8vQoEgvGOcdVyu1OZvxhhrmOL+Y6dy5nrpCSH0tNdF78+2KEAAABw5ShpAAAABqKkAQAAGIiSBgAAYCBKGgAAgIFsewVHPE2Z/Yt4RwDiKhzqjXcEAMAVSsiS1tnZrb6+hHuziJF4nDt2mGsAGFpY7gQAADAQJQ0AAMBAlDQAAAADJeRv0i61xQIGn9udGu8IQwZzHS0c6tWJk6F4xwAAWyRkSXt7+wM63d0R7xgAbPaXD/olUdIAJCaWOwEAAAxESQMAADAQJQ0AAMBAlDQAAAAD2frgwIYNG7R3715J0sSJE3XHHXdo7dq1/dc7Ojo0btw4/exnP4sa19bWpmXLlqmzs1NjxoxRbW2tRo4caWdUAAAAo9j2TdoHH3ygffv2qaGhQV6vV59++qlCoZB27dqlXbt26ec//7lcLpeeeOKJAWNrampUWloqv9+vrKws1dXV2RUTAADASLaVNLfbrcrKSg0bNkxOp1Njx45VW1tb//U1a9Zozpw5uvHGG6PGhcNhtbS0KC8vT5JUXFwsv99vV0wAAAAj2VbSbrrpJo0fP16SdOTIEe3du1cTJ07sP25ubtbcuXMHjOvq6pLL5VJKyrmVWLfbrY4O3nkGAACGFtsfHPjiiy80f/58LV++vP9bszfeeEOlpaUaNmzYgPsty5LD4Yg6d/4xAABAorO1pO3fv1/z5s3T0qVLNXPmzP7zjY2NKigouOCYtLQ0BYNBRSIRSVIgEFBGRoadMQEAAIxjW0lrb2/XwoULVVtbK4/H03/++PHjOnPmjEaPHn3BcU6nU9nZ2fL5fJIkr9er3Nxcu2ICAAAYybZXcGzevFm9vb1avXp1/7k5c+bo1ltv1Te+8Y0B91dVVWny5MmaMmWKqqurVVlZqU2bNikzMzPqtR0AAABDgcOyLCveIQYbG6wDQ8NfPuhXIBAc9L/rdqfa8ncxEHMdW8x37FzOXCclOZSe7rr49cEOBQAAgCtHSQMAADAQJQ0AAMBAlDQAAAADUdIAAAAMZNsrOOLJ85e/iHcEADEQDvXGOwIA2CYhS1pnZ7f6+hLuzSJG4nHu2GGuAWBoYbkTAADAQJQ0AAAAA1HSAAAADJSQv0m71BYLGHxud2q8IwwZzPXgC4fP6MSJcLxjAMAACVnSduwoVw97dwK4DA/M+2dJlDQA5mG5EwAAwECUNAAAAANR0gAAAAxESQMAADCQrSVtw4YN8ng88ng8WrNmjSRp3759mj59uqZNm6bly5crFAoNGNfW1qaysjLl5+eroqJCPT09dsYEAAAwjm0l7YMPPtC+ffvU0NAgr9erTz/9VL/85S9VVVWldevWac+ePTpz5ox27do1YGxNTY1KS0vl9/uVlZWluro6u2ICAAAYybaS5na7VVlZqWHDhsnpdGrs2LFqa2tTJBJRd3e3IpGIent7dc0110SNC4fDamlpUV5eniSpuLhYfr/frpgAAABGsu09aTfddFP/v48cOaK9e/fqtdde0ze/+U2Vl5fL5XLp+uuvV35+ftS4rq4uuVwupaSci+Z2u9XRwTvPAADA0GL7gwNffPGF5s+fr+XLl2vkyJGqra3Vnj17tG/fPo0bN07PPvts1P2WZcnhcESdO/8YAAAg0dla0vbv36958+Zp6dKlmjlzplpbW3XzzTfrW9/6lpKSkjR79mw1NzdHjUlLS1MwGFQkEpEkBQIBZWRk2BkTAADAOLaVtPb2di1cuFC1tbXyeDySpJtvvlkHDx7UsWPHJEmNjY363ve+FzXO6XQqOztbPp9PkuT1epWbm2tXTAAAACPZ9pu0zZs3q7e3V6tXr+4/N2fOHD322GOaO3eukpOTdcMNN2jVqlWSpKqqKk2ePFlTpkxRdXW1KisrtWnTJmVmZmrt2rV2xQQAADCSw7IsK94hBhsbrAO4XA/M+2cFAsGoc2536oBzsAdzHVvMd+xczlwnJTmUnu66+PXBDgUAAIArR0kDAAAwECUNAADAQJQ0AAAAA9n2dGc8zZq1Nd4RAFwlwuEz8Y4AABeUkCWts7NbfX0J99CqkXhSKHaYawAYWljuBAAAMBAlDQAAwECUNAAAAAMl5G/SLvX2Xgw+tzs13hGGDOY6dhJlrkPhMzp5IhzvGAD+CAlZ0l7ZOVfdPWwLBQCPlL8jiZIGXI1Y7gQAADAQJQ0AAMBAlDQAAAADUdIAAAAMZGtJ27Bhgzwejzwej9asWSNJ2rlzpwoKClRYWKinn35aZ8+eHTCura1NZWVlys/PV0VFhXp6euyMCQAAYBzbStoHH3ygffv2qaGhQV6vV59++qlefPFFPf/883rppZf01ltv6ezZs9q6deA+mzU1NSotLZXf71dWVpbq6ursigkAAGAk20qa2+1WZWWlhg0bJqfTqbFjxyoUCmn8+PHKyMiQJE2aNEm/+tWvosaFw2G1tLQoLy9PklRcXCy/329XTAAAACPZVtJuuukmjR8/XpJ05MgR7d27VwUFBTpw4IDa29sViUTk9/t17NixqHFdXV1yuVxKSTn3Cje3262ODt55BgAAhhbbX2b7xRdf6JFHHtHy5cv17W9/W0uXLlVFRYW+9rWvKT8/X//2b/8Wdb9lWXI4HFHnzj8GAABIdLaWtP3792vRokV68skn5fF41Nvbq+9///vyer2SpL1792r06NFRY9LS0hQMBhWJRJScnKxAINC/PAoAADBU2Lbc2d7eroULF6q2tlYej0eSdPr0ac2bN0/d3d0KhULatm2bCgoKosY5nU5lZ2fL5/NJkrxer3Jzc+2KCQAAYCTbvknbvHmzent7tXr16v5zc+bM0cKFC3Xffffp7NmzmjZtmgoLCyVJVVVVmjx5sqZMmaLq6mpVVlZq06ZNyszM1Nq1a+2KCQAAYCSHZVlWvEMMNjZYB4BzHil/R4FAMN4xLsrtTjU6X6JhvmPncuY6Kcmh9HTXxa8PdigAAABcOUoaAACAgShpAAAABqKkAQAAGIiSBgAAYCDbdxyIh7Lil+MdAQCMEAqfiXcEAH+khCxpnZ3d6utLuDeLGInHuWOHuY4d5hqACVjuBAAAMBAlDQAAwECUNAAAAAMl5G/SLrXFAgaf250a7whDBnMdO8y1/XioAbi0hCxpm3fN1Sn27gQAoy0pfSfeEQCjsdwJAABgIEoaAACAgShpAAAABqKkAQAAGMjWkvbCCy+ooKBAHo9HW7ZskSR98MEHKiws1NSpU7Vu3boLjmtra1NZWZny8/NVUVGhnp4eO2MCAAAYx7aS1tzcrA8//FC7d+/Wm2++qa1bt+o//uM/9OSTT6qurk4+n0+ffPKJ3nvvvQFja2pqVFpaKr/fr6ysLNXV1dkVEwAAwEi2lbTbb79dL7/8slJSUtTZ2alIJKJTp07phhtu0OjRo5WSkqLCwkL5/f6oceFwWC0tLcrLy5MkFRcXD7gHAAAg0dm63Ol0OrV+/Xp5PB7l5OTo97//vdxud//1jIwMdXREv8+sq6tLLpdLKSnnXuHmdrsH3AMAAJDobH9wYNGiRWpqalJ7e7uOHDkih8PRf82yrKjji507/xgAACDR2VbSDh06pM8++0ySNHz4cE2dOlUfffSRAoFA/z2BQEAZGRlR49LS0hQMBhWJRC56DwAAQKKzraQdPXpUK1asUCgUUigUUmNjo+bMmaPf/OY3+u1vf6tIJKI9e/YoNzc3apzT6VR2drZ8Pp8kyev1DrgHAAAg0dm2d+fEiRN18OBBzZgxQ8nJyZo6dao8Ho/S0tL06KOPqre3VxMnTlR+fr4kqaqqSpMnT9aUKVNUXV2tyspKbdq0SZmZmVq7dq1dMQEAAIzksCzLineIwcYG6wBgvv/cYD0QCMY5ydDhdqcy3zFyOXOdlORQerrr4tcHOxQAAACuHCUNAADAQJQ0AAAAA1HSAAAADERJAwAAMJBtr+CIpx8WvRzvCACArxAKn9Ew59fiHQMwVkKWtM7ObvX1JdybRYzE49yxw1zHDnMdO243JQ24GJY7AQAADERJAwAAMBAlDQAAwEAJ+Zu0S22xgMHndqfGO8KQwVzHDnMdOxea61D4jE6eCMchDWCOhCxp69+aq5On2bsTAK5WT933jiRKGoY2ljsBAAAMREkDAAAwECUNAADAQJQ0AAAAA9la0l544QUVFBTI4/Foy5Yt/efD4bAeeOABffTRRxcc19bWprKyMuXn56uiokI9PT12xgQAADCObSWtublZH374oXbv3q0333xTW7du1eHDh3X48GGVl5fr448/vujYmpoalZaWyu/3KysrS3V1dXbFBAAAMJJtJe3222/Xyy+/rJSUFHV2dioSiWjEiBHasWOHHnroIY0bN+6C48LhsFpaWpSXlydJKi4ult/vtysmAACAkWxd7nQ6nVq/fr08Ho9ycnJ03XXXafny5br77rsvOqarq0sul0spKede4eZ2u9XRwTvPAADA0GL7gwOLFi1SU1OT2tvbVV9f/5X3W5Ylh8MRde78YwAAgERnW0k7dOiQPvvsM0nS8OHDNXXqVH3++edfOS4tLU3BYFCRSESSFAgElJGRYVdMAAAAI9lW0o4ePaoVK1YoFAopFAqpsbFRt91221eOczqdys7Ols/nkyR5vV7l5ubaFRMAAMBItpW0iRMn6gc/+IFmzJihkpISTZgwQR6P56L3V1VVqbGxUZJUXV2t+vp6FRQUqLW1VYsXL7YrJgAAgJEclmVZ8Q4x2NhgHQCubk/d944CgWC8YyQctzuVeY2Ry5nrpCSH0tNdF78+2KEAAABw5ShpAAAABqKkAQAAGIiSBgAAYCBKGgAAgIFS4h3ADosKX453BADAFQiFz8Q7AhB3CVnSOju71deXcG8WMRKPc8cOcx07zHXsMNfAxbHcCQAAYCBKGgAAgIEScrnzUm/vxeBzu1PjHWHIYK5jh7kefL3hXp06EYp3DOCqkZAl7f/d+4C62BYKAIyytsQviZIGXC6WOwEAAAxESQMAADAQJQ0AAMBAlDQAAAAD2VrSXnjhBRUUFMjj8WjLli2SpDfeeEPTpk1TYWGhnnjiCYVCA39E2tbWprKyMuXn56uiokI9PT12xgQAADCObSWtublZH374oXbv3q0333xTW7du1eHDh7V582a9/vrr2r17t/r6+vTqq68OGFtTU6PS0lL5/X5lZWWprq7OrpgAAABGsq2k3X777Xr55ZeVkpKizs5ORSIRXXPNNaqurpbL5ZLD4dDNN9+stra2qHHhcFgtLS3Ky8uTJBUXF8vv99sVEwAAwEi2Lnc6nU6tX79eHo9HOTk5+pM/+RPdddddkqTjx4/rlVde0ZQpU6LGdHV1yeVyKSXl3Cvc3G63Ojp45xkAABhabH9wYNGiRWpqalJ7e7vq6+slSR0dHXrggQdUUlKiO+64I+p+y7LkcDiizp1/DAAAkOhsK2mHDh3SZ599JkkaPny4pk6dqs8//1yHDh3SnDlzNHPmTC1cuHDAuLS0NAWDQUUiEUlSIBBQRkaGXTEBAACMZFtJO3r0qFasWKFQKKRQKKTGxkZ9//vf1w9/+EM99thjmj9//gXHOZ1OZWdny+fzSZK8Xq9yc3PtigkAAGAk20raxIkT9YMf/EAzZsxQSUmJJkyYoBMnTujYsWPasmWLioqKVFRUpBdeeEGSVFVVpcbGRklSdXW16uvrVVBQoNbWVi1evNiumAAAAEZyWJZlxTvEYGODdQAwz9oSvwKBYNQ5tzt1wDnYh/mOncuZ66Qkh9LTXRe/PtihAAAAcOUoaQAAAAaipAEAABiIkgYAAGAgShoAAICBUuIdwA5P3fuLeEcAAJynN9wb7wjAVSUhS1pnZ7f6+hLuzSJG4nHu2GGuY4e5BmACljsBAAAMREkDAAAwECUNAADAQAn5m7RLbbGAwed2p8Y7wpDBXMcOc31hZ8K9Cp4IxTsGMCQkZEmr+OcHFPiSvTsBYLDtKPIrKEoaEAssdwIAABiIkgYAAGAgShoAAICBKGkAAAAGsrWkvfDCCyooKJDH49GWLVskSa+++qo8Ho8KCgr03HPPybIG7gzQ1tamsrIy5efnq6KiQj09PXbGBAAAMI5tJa25uVkffvihdu/erTfffFNbt27V4cOH9dJLL2n79u1666239PHHH+v9998fMLampkalpaXy+/3KyspSXV2dXTEBAACMZFtJu/322/Xyyy8rJSVFnZ2dikQiGjFihN5++22NGDFCp06dUnd3t0aNGhU1LhwOq6WlRXl5eZKk4uJi+f1+u2ICAAAYydblTqfTqfXr18vj8SgnJ0fXXXednE6n6uvrdffdd8vtduuWW26JGtPV1SWXy6WUlHOvcHO73ero4J1nAABgaLH9wYFFixapqalJ7e3tqq+vlyTNnj1bH330ka699lpt2LAh6n7LsuRwOKLOnX8MAACQ6GwraYcOHdJnn30mSRo+fLimTp2qAwcOaP/+/ZKklJQUeTweff7551Hj0tLSFAwGFYlEJEmBQEAZGRl2xQQAADCSbSXt6NGjWrFihUKhkEKhkBobG3X99ddr2bJlOnXqlCzL0jvvvKPbbrstapzT6VR2drZ8Pp8kyev1Kjc3166YAAAARrJt786JEyfq4MGDmjFjhpKTkzV16lT9zd/8jdLS0jRnzhwlJycrOztbDz74oCSpqqpKkydP1pQpU1RdXa3Kykpt2rRJmZmZWrt2rV0xAQAAjOSwLvSisqscG6wDgD12FPkVCAQH7e+53amD+vdwacx37FzOXCclOZSe7rr49cEOBQAAgCtHSQMAADAQJQ0AAMBAlDQAAAADUdIAAAAMZNsrOOJp09RfxDsCACSkM+HeeEcAhoyELGmdnd3q60u4N4sYice5Y4e5jh3mGoAJWO4EAAAwECUNAADAQJQ0AAAAAyXkb9IutcUCBp/bnRrvCEMGcx07zHXsDPZcnwmHFDzBAw64+iVkSXvgn6v0+y874x0DABAHe4v+UUFR0nD1Y7kTAADAQJQ0AAAAA1HSAAAADERJAwAAMJCtJe2FF15QQUGBPB6PtmzZEnVt27ZtKi8vv+C4trY2lZWVKT8/XxUVFerp6bEzJgAAgHFsK2nNzc368MMPtXv3br355pvaunWrDh8+LEn69a9/rRdffPGiY2tqalRaWiq/36+srCzV1dXZFRMAAMBItpW022+/XS+//LJSUlLU2dmpSCSiESNGKBQKaeXKlVq0aNEFx4XDYbW0tCgvL0+SVFxcLL/fb1dMAAAAI9m63Ol0OrV+/Xp5PB7l5OTouuuu009/+lOVlJRo9OjRFxzT1dUll8ullJRzr3Bzu93q6OiwMyYAAIBxbH9wYNGiRWpqalJ7e7veeOMNtbe3q6Sk5KL3W5Ylh8MRde78YwAAgERn244Dhw4dUigU0ne/+10NHz5cU6dO1YEDB/TFF1+oqKhIp0+f1rFjx7R48WI9//zz/ePS0tIUDAYViUSUnJysQCCgjIwMu2ICAAAYybZv0o4ePaoVK1YoFAopFAqpsbFRf/7nf669e/dq165devrpp5WVlRVV0KRzS6TZ2dny+XySJK/Xq9zcXLtiAgAAGMm2kjZx4kT94Ac/0IwZM1RSUqIJEybI4/Fc9P6qqio1NjZKkqqrq1VfX6+CggK1trZq8eLFdsUEAAAwksOyLCveIQYbG6wDwNC1t+gfFQgE4x3DSG53KnMTI5cz10lJDqWnuy5+fbBDAQAA4MpR0gAAAAxESQMAADAQJQ0AAMBAlDQAAAAD2fYy23j6xdSfxDsCACBOzoRD8Y4ADIqELGmdnd3q60u4N4sYice5Y4e5jh3mOnaYa+DiWO4EAAAwECUNAADAQJQ0AAAAAyXkb9IutcUCBp/bnRrvCEMGcx07zHXsMNf/x5lwSMETvfGOAUMkZEmb98+1+v3pE/GOAQDAH8Q342kFRUnDOSx3AgAAGIiSBgAAYCBKGgAAgIEoaQAAAAay9cGBF154Qe+8844cDodmzZqlBx98UE888YT279+v4cOHS5J+9KMf6Z577oka19bWpmXLlqmzs1NjxoxRbW2tRo4caWdUAAAAo9hW0pqbm/Xhhx9q9+7dOnv2rAoKCjRx4kR98skn2rZtmzIyMi46tqamRqWlpfJ4PNq4caPq6uq0bNkyu6ICAAAYx7blzttvv10vv/yyUlJS1NnZqUgkoq997Wtqa2vTk08+qcLCQq1fv159fX1R48LhsFpaWpSXlydJKi4ult/vtysmAACAkWz9TZrT6dT69evl8XiUk5Ojs2fP6s4779Qzzzyj+vp6tba2aseOHVFjurq65HK5lJJy7ks+t9utjo4OO2MCAAAYx/YHBxYtWqSmpia1t7erqalJGzduVEZGhoYPH67y8nK99957UfdbliWHwxF17vxjAACARGdbSTt06JA+++wzSdLw4cM1depU+Xw+vfPOO/33WJbV/43Zf0pLS1MwGFQkEpEkBQKBS/5+DQAAIBHZVtKOHj2qFStWKBQKKRQKqbGxUX/2Z3+mZ555RidPnlQ4HNYbb7wx4MlOp9Op7Oxs+Xw+SZLX61Vubq5dMQEAAIxk29OdEydO1MGDBzVjxgwlJydr6tSp+tGPfqSvf/3ruv/++3X27FlNnTpV06ZNkyRVVVVp8uTJmjJliqqrq1VZWalNmzYpMzNTa9eutSsmAACAkRyWZVnxDjHY2GAdAHA18s14WoFA0La/73an2vr38X9czlwnJTmUnu66+PXBDgUAAIArR0kDAAAwECUNAADAQJQ0AAAAA9m6wXq8vDT1b+MdAQCAP9iZcCjeEWCQhCxpnZ3d6utLuIdWjcSTQrHDXMcOcx07zDVwcSx3AgAAGIiSBgAAYCBKGgAAgIES8jdpl3p7Lwaf250a7whDBnMdO8x17CT6XJ8JhxU8cSbeMXAVSsiSNu+dTfr96ZPxjgEAgHwzKxUUJQ1/OJY7AQAADERJAwAAMBAlDQAAwECUNAAAAAPZWtJeeOEFFRQUyOPxaMuWLZKkjz/+WLNnz5bH49Hjjz+uUGjgFhhtbW0qKytTfn6+Kioq1NPTY2dMAAAA49hW0pqbm/Xhhx9q9+7devPNN7V161b9x3/8hx599FGtWrVKb7/9tiRpx44dA8bW1NSotLRUfr9fWVlZqqursysmAACAkWwrabfffrtefvllpaSkqLOzU5FIRJ999pnGjx+vW265RZK0YsUK3XPPPVHjwuGwWlpalJeXJ0kqLi6W3++3KyYAAICRbF3udDqdWr9+vTwej3JychQIBDRixAgtWbJERUVF+od/+AeNGjUqakxXV5dcLpdSUs69ws3tdqujo8POmAAAAMax/cGBRYsWqampSe3t7QqFQtq3b58ef/xx7dy5U19++aVefPHFqPsty5LD4Yg6d/4xAABAorOtpB06dEifffaZJGn48OGaOnWqXnzxRY0bN06jR49WcnKy7r33Xh08eDBqXFpamoLBoCKRiCQpEAgoIyPDrpgAAABGsq2kHT16VCtWrFAoFFIoFFJjY6NWrVqlTz/9VO3t7ZKkd999V7feemvUOKfTqezsbPl8PkmS1+tVbm6uXTEBAACMZNvenRMnTtTBgwc1Y8YMJScna+rUqZoxY4b+y3/5L1qwYIF6e3v13e9+V3/3d38nSaqqqtLkyZM1ZcoUVVdXq7KyUps2bVJmZqbWrl1rV0wAAAAjOSzLsuIdYrCxwToAwBS+mZUKBILxjiFJcrtTjcmS6C5nrpOSHEpPd138+mCHAgAAwJWjpAEAABiIkgYAAGAgShoAAICBKGkAAAAGsu0VHPH0Ul5FvCMAACBJOhMOxzsCrlIJWdI6O7vV15dwbxYxEo9zxw5zHTvMdeww18DFsdwJAABgIEoaAACAgShpAAAABkrI36RdaosFDD63OzXeEYYM5jp2hvJcnwmHFTxxJt4xgCEvIUvag/5/0u9P80NUAPhjvF38mIKipAHxxnInAACAgShpAAAABqKkAQAAGIiSBgAAYCBbHxx44YUX9M4778jhcGjWrFn69re/rbVr1/Zf7+jo0Lhx4/Szn/0salxbW5uWLVumzs5OjRkzRrW1tRo5cqSdUQEAAIxi2zdpzc3N+vDDD7V79269+eab2rp1q0aPHq1du3Zp165d+vnPfy6Xy6UnnnhiwNiamhqVlpbK7/crKytLdXV1dsUEAAAwkm0l7fbbb9fLL7+slJQUdXZ2KhKJaMSIEf3X16xZozlz5ujGG2+MGhcOh9XS0qK8vDxJUnFxsfx+v10xAQAAjGTrb9KcTqfWr18vj8ejnJwcXXfddZKkI0eOqLm5WXPnzh0wpqurSy6XSykp51Zi3W63Ojo67IwJAABgHNsfHFi0aJGamprU3t6u+vp6SdIbb7yh0tJSDRs2bMD9lmXJ4XBEnTv/GAAAINHZVtIOHTqkzz77TJI0fPhwTZ06VZ9//rkkqbGxUQUFBRccl5aWpmAwqEgkIkkKBALKyMiwKyYAAICRbCtpR48e1YoVKxQKhRQKhdTY2KjbbrtNx48f15kzZzR69OgLjnM6ncrOzpbP55Mkeb1e5ebm2hUTAADASLaVtIkTJ+oHP/iBZsyYoZKSEk2YMEEej0dHjx7VN77xjQH3V1VVqbGxUZJUXV2t+vp6FRQUqLW1VYsXL7YrJgAAgJEclmVZ8Q4x2NhgHQD+eG8XP6ZAIDb/H+p2p8bss8B8x9LlzHVSkkPp6a6LXx/sUAAAALhylDQAAAADUdIAAAAMREkDAAAwECUNAADAQCnxDmCHLfnz4x0BAK5aZ8LheEcAoAQtaZ2d3errS7g3ixiJx7ljh7mOHeYagAlY7gQAADAQJQ0AAMBAlDQAAAADJeRv0i61xQIGn9udGu8IQwZzHTtX81yfCYcVPHEm3jEAXKGELGkP7n1Fvz/dHe8YABAXb5c8oqAoacDVjuVOAAAAA1HSAAAADERJAwAAMBAlDQAAwEC2lrQXXnhBBQUF8ng82rJliyRp3759mj59uqZNm6bly5crFAoNGNfW1qaysjLl5+eroqJCPT09dsYEAAAwjm0lrbm5WR9++KF2796tN998U1u3btXhw4dVVVWldevWac+ePTpz5ox27do1YGxNTY1KS0vl9/uVlZWluro6u2ICAAAYybaSdvvtt+vll19WSkqKOjs7FYlENGLECEUiEXV3dysSiai3t1fXXHNN1LhwOKyWlhbl5eVJkoqLi+X3++2KCQAAYCRblzudTqfWr18vj8ejnJwcXXfddfrxj3+s8vJy/cVf/IW6urqUn58fNaarq0sul0spKede4eZ2u9XR0WFnTAAAAOPY/uDAokWL1NTUpPb2dm3cuFG1tbXas2eP9u3bp3HjxunZZ5+Nut+yLDkcjqhz5x8DAAAkOttK2qFDh/TZZ59JkoYPH66pU6dq7969uvnmm/Wtb31LSUlJmj17tpqbm6PGpaWlKRgMKhKJSJICgYAyMjLsigkAAGAk20ra0aNHtWLFCoVCIYVCITU2Nmr69Ok6ePCgjh07JklqbGzU9773vahxTqdT2dnZ8vl8kiSv16vc3Fy7YgIAABjJtr07J06cqIMHD2rGjBlKTk7W1KlT9cgjjygjI0Nz585VcnKybrjhBq1atUqSVFVVpcmTJ2vKlCmqrq5WZWWlNm3apMzMTK1du9aumAAAAEZyWJZlxTvEYGODdQBD2dsljygQCMY7xmVxu1OvmqyJgPmOncuZ66Qkh9LTXRe/PtihAAAAcOUoaQAAAAaipAEAABiIkgYAAGAgShoAAICBbHsFRzxtubcs3hEAIG7OhMPxjgBgECRkSevs7FZfX8K9WcRIPM4dO8x17DDXAEzAcicAAICBKGkAAAAGSsjlzku9vReDz+1OjXeEIYO5jo1Q5Gy8IwBAYpa0+Xu3sy0UgD/anpIH4x0BAFjuBAAAMBElDQAAwECUNAAAAANR0gAAAAxka0l74YUXVFBQII/Hoy1btkiSdu7cqYKCAhUWFurpp5/W2bMDn6Jqa2tTWVmZ8vPzVVFRoZ6eHjtjAgAAGMe2ktbc3KwPP/xQu3fv1ptvvqmtW7fq8OHDev755/XSSy/prbfe0tmzZ7V169YBY2tqalRaWiq/36+srCzV1dXZFRMAAMBItpW022+/XS+//LJSUlLU2dmpSCSigwcPavz48crIyJAkTZo0Sb/61a+ixoXDYbW0tCgvL0+SVFxcLL/fb1dMAAAAI9m63Ol0OrV+/Xp5PB7l5OTo+9//vg4cOKD29nZFIhH5/X4dO3YsakxXV5dcLpdSUs69ws3tdqujo8POmAAAAMax/cGBRYsWqampSe3t7WppadHSpUtVUVGhsrIyfec735HT6Yy637IsORyOqHPnHwMAACQ620raoUOH9Nlnn0mShg8frqlTp+rgwYP6/ve/L6/Xq9dff13XXXedRo8eHTUuLS1NwWBQkUhEkhQIBPqXRwEAAIYK20ra0aNHtWLFCoVCIYVCITU2NuqOO+7QvHnz1N3drVAopG3btqmgoCBqnNPpVHZ2tnw+nyTJ6/UqNzfXrpgAAABG+sqS1tPTo5qaGj3wwAM6ceKEVq5ceVmvxJg4caJ+8IMfaMaMGSopKdGECRM0ffp0LVy4UPfdd58KCwt15513qrCwUJJUVVWlxsZGSVJ1dbXq6+tVUFCg1tZWLV68+Mr+KwEAAK4yDsuyrEvd8MQTTygjI0ONjY3avn27qqqq5HA49NOf/jRWGf9gbLAO4ErsKXlQgUAw3jGGBLc7lbmOIeY7di5nrpOSHEpPd138+ld9yGeffaYlS5YoJSVFw4cPV21tbf9vzQAAAGCPryxpSUnRt0QikQHnAAAAMLhSvuqGP/uzP9Pf//3f68yZM/rXf/1XvfLKK7rjjjtikQ0AAGDI+sqvxP72b/9WI0aMUGpqqtatW6fvfOc7Wr58eSyyAQAADFlf+U2a0+nUwoULtXDhwljkAQAAgC6jpE2ePDnqjf8Oh0PDhw/XTTfdpMrKSiNfNPtP9/5lvCMAuIqFImfjHQEAvrqk3X333erp6VFZWZmSkpK0Y8cO9fT06Dvf+Y5Wrlypf/zHf4xFzj9IZ2e3+vou+WYRDBIe544d5jp23O7UeEcAgK/+TVpra6t+8pOf6E//9E91yy23aMWKFfriiy80b948/e53v4tFRgAAgCHnsnYc6O7+Py+G7e7u1pdffmlrKAAAgKHuK5c7S0pKNHv2bOXn56uvr0+//OUv9Zd/+ZfaunWrvv3tb8ciIwAAwJDzldtCSdK//uu/6l/+5V+UkpKiW2+9Vb/4xS/04x//WDfccINcrotvZwAApjoTDit44swFr/H7v9hhrmOL+Y6dwdgW6iu/SZOk73//+/rss8+0bds27dixQ+Xl5br11lv/sLQxNN/n1e9Pf/Um8ACGrj2zyhTUhUsaAJjgkiXt8OHD+sUvfqHdu3frm9/8pnp7e/U//sf/UGoqTz4BAADY6aIPDjz88MP6q7/6KzmdTr388svas2ePRo4cSUEDAACIgYuWtH//93/Xrbfeqptuukk33HCDJEW91BYAAAD2uWhJ+5//839q5syZ2rNnj/78z/9cixYtUm9vbyyzAQAADFkXLWkpKSkqKCjQ1q1btXPnTmVkZKi3t1dTp07Va6+9dtkf8Nxzz6myslKS9MEHH6iwsFBTp07VunXrLnh/W1ubysrKlJ+fr4qKCvX08AAAAAAYer7yZbaS9F//63/VihUr9C//8i/64Q9/qPr6+sv6401NTWpoaJAknTlzRk8++aTq6urk8/n0ySef6L333hswpqamRqWlpfL7/crKylJdXd0f8J8DAACQGC6rpP2n4cOH67777usvXpdy4sQJrVu3TgsWLJAkHTx4UDfccINGjx6tlJQUFRYWyu/3R40Jh8NqaWlRXl6eJKm4uHjAPQAAAEPBH1TS/hArV67UkiVLNGrUKEnS73//e7nd7v7rGRkZ6ujoiBrT1dUll8ullJRzbwZxu90D7gEAABgKbClp27dvV2ZmpnJycvrP9fX1RT0dalnWgKdFL3SOJ0oBAMBQdFk7DvyhfD6fAoGAioqKdPLkSZ0+fVq/+93vlJyc3H9PIBBQRkZG1Li0tDQFg0FFIhElJydf8B4AAIChwJaStmXLlv5/79y5U83NzaqpqdHUqVP129/+Vtdff7327NmjkpKSqHFOp1PZ2dny+XwqLCyU1+tVbm6uHREBAACMZttv0s53zTXXaPXq1Xr00UdVUFCgb3/728rPz5ckVVVVqbGxUZJUXV2t+vp6FRQUqLW1VYsXL45VRAAAAGM4LMuy4h1isLHBOoCvsmdWmQKB4AWvud2pF72GwcVcxxbzHTuXM9dJSQ6lp7sufn2wQwEAAODKUdIAAAAMREkDAAAwECUNAADAQJQ0AAAAA9nynrR4+6eCGfGOAMBwZ8LheEcAgEtKyJLW2dmtvr6Ee7OIkXicO3aYawAYWljuBAAAMBAlDQAAwECUNAAAAAMl5G/SLrXFAgaf250a7whDBnMdO18112fCZxU88WWM0gAYihKypM33va3fnz4d7xgAEtieWX8pHuMAYCeWOwEAAAxESQMAADAQJQ0AAMBAlDQAAAAD2V7SnnvuOVVWVvYfh8NhPfDAA/roo48ueH9bW5vKysqUn5+viooK9fT02B0RAADAOLaWtKamJjU0NPQfHz58WOXl5fr4448vOqampkalpaXy+/3KyspSXV2dnREBAACMZFtJO3HihNatW6cFCxb0n9uxY4ceeughjRs37oJjwuGwWlpalJeXJ0kqLi6W3++3KyIAAICxbCtpK1eu1JIlSzRq1Kj+c8uXL9fdd9990TFdXV1yuVxKSTn3+ja3262Ojg67IgIAABjLlpK2fft2ZWZmKicn5w8aZ1mWHA5H1LnzjwEAAIYCW3Yc8Pl8CgQCKioq0smTJ3X69Gk988wzevLJJy85Li0tTcFgUJFIRMnJyQoEAsrIyLAjIgAAgNFsKWlbtmzp//fOnTvV3Nz8lQVNkpxOp7Kzs+Xz+VRYWCiv16vc3Fw7IgIAABjNiPekVVVVqbGxUZJUXV2t+vp6FRQUqLW1VYsXL45vOAAAgDhwWJZlxTvEYGODdQB22zPrLxUIsMX6lXK7U5nHGGK+Y+dy5jopyaH0dNfFrw92KAAAAFw5ShoAAICBKGkAAAAGoqQBAAAYiJIGAABgIFvekxZv/1TgiXcEAAnuTPhsvCMASHAJWdI6O7vV15dwbxYxEo9zxw5zHTvMNQATsNwJAABgIEoaAACAgShpAAAABkrI36RdaosFDD63OzXeEYYM5voPcyZ8VsETX8Y7BgD8URKypD3k+2f9/jT/xwwMdbtnFYmf/wO4WrHcCQAAYCBKGgAAgIEoaQAAAAaipAEAABjI9pL23HPPqbKyUpL0xhtvaNq0aSosLNQTTzyhUCg04P62tjaVlZUpPz9fFRUV6unpsTsiAACAcWwtaU1NTWpoaJAk/eY3v9HmzZv1+uuva/fu3err69Orr746YExNTY1KS0vl9/uVlZWluro6OyMCAAAYybaSduLECa1bt04LFiyQJA0bNkzV1dVyuVxyOBy6+eab1dbWFjUmHA6rpaVFeXl5kqTi4mL5/X67IgIAABjLtpK2cuVKLVmyRKNGjZIkffOb39Rdd90lSTp+/LheeeUVTZkyJWpMV1eXXC6XUlLOvb7N7Xaro6PDrogAAADGsqWkbd++XZmZmcrJyRlwraOjQw888IBKSkp0xx13RF2zLEsOhyPq3PnHAAAAQ4EtOw74fD4FAgEVFRXp5MmTOn36tJ555hndd999euihh1ReXq758+cPGJeWlqZgMKhIJKLk5GQFAgFlZGTYEREAAMBotpS0LVu29P97586dam5u1qJFizRt2jQtXrxYM2bMuOA4p9Op7Oxs+Xw+FRYWyuv1Kjc3146IAAAARovZe9J27NihY8eOacuWLSoqKlJRUZFeeOEFSVJVVZUaGxslSdXV1aqvr1dBQYFaW1u1ePHiWEUEAAAwhsOyLCveIQYbG6wDkM5tsB4I/OFbrLvdqX/UOPzhmOvYYr5j53LmOinJofR018WvD3YoAAAAXDlKGgAAgIEoaQAAAAaipAEAABjIlldwxNvPC6bGOwIAA5wJn413BAD4oyVkSevs7FZfX8I9tGoknhSKHeYaAIYWljsBAAAMREkDAAAwECUNAADAQAn5m7RLvb0Xg8/tTo13hCEjUef6TPisgifYJQQA/m8JWdL+2vce20IBV5Fds/LFIxEAEI3lTgAAAANR0gAAAAxESQMAADAQJQ0AAMBAtpe05557TpWVlZKkV199VR6PRwUFBXruuedkWQN3BWhra1NZWZny8/NVUVGhnp4euyMCAAAYx9aS1tTUpIaGBknS//7f/1svvfSStm/frrfeeksff/yx3n///QFjampqVFpaKr/fr6ysLNXV1dkZEQAAwEi2lbQTJ05o3bp1WrBggSRp9OjRevvttzVixAidOnVK3d3dGjVqVNSYcDislpYW5eXlSZKKi4vl9/vtiggAAGAs20raypUrtWTJkqgi5nQ6VV9fr7vvvltut1u33HJL1Jiuri65XC6lpJx7fZvb7VZHR4ddEQEAAIxlS0nbvn27MjMzlZOTM+Da7Nmz9dFHH+naa6/Vhg0boq5ZliWHwxF17vxjAACAocCWHQd8Pp8CgYCKiop08uRJnT59Wk888YRmzZql2267TSkpKfJ4PHrttdeixqWlpSkYDCoSiSg5OVmBQEAZGRl2RAQAADCaLSVty5Yt/f/euXOnmpub9eCDD2rBggXyer1KTU3VO++8o9tuuy1qnNPpVHZ2tnw+nwoLC+X1epWbm2tHRAAAAKPF7D1pN998sx5++GHNmTNH06dP19e+9jU9+OCDkqSqqio1NjZKkqqrq1VfX6+CggK1trZq8eLFsYoIAABgDId1oZeVXeXYYB24uuyala9AwJwt1t3uVKPyJDLmOraY79i5nLlOSnIoPd118euDHQoAAABXjpIGAABgIEoaAACAgShpAAAABqKkAQAAGMiW96TF238vmBjvCAD+AGfCZ+MdAQCMk5AlrbOzW319CfdmESPxOHfsMNcAMLSw3AkAAGAgShoAAICBKGkAAAAGSsjfpF1qiwUMPrc7Nd4Rhoyvmusz4bMKnmBLNABIBAlZ0h7e+6ECp8/EOwYQcw0lPxCPFgBAYmC5EwAAwECUNAAAAANR0gAAAAxESQMAADCQ7SXtueeeU2VlZdS5bdu2qby8/IL3t7W1qaysTPn5+aqoqFBPT4/dEQEAAIxja0lrampSQ0ND1Llf//rXevHFFy86pqamRqWlpfL7/crKylJdXZ2dEQEAAIxkW0k7ceKE1q1bpwULFvSfC4VCWrlypRYtWnTBMeFwWC0tLcrLy5MkFRcXy+/32xURAADAWLaVtJUrV2rJkiUaNWpU/7mf/vSnKikp0ejRoy84pqurSy6XSykp517f5na71dHRYVdEAAAAY9lS0rZv367MzEzl5OT0n3v//ffV3t6ukpKSi46zLEsOhyPq3PnHAAAAQ4EtOw74fD4FAgEVFRXp5MmTOn36tBwOh7744gsVFRXp9OnTOnbsmBYvXqznn3++f1xaWpqCwaAikYiSk5MVCASUkZFhR0QAAACj2VLStmzZ0v/vnTt3qrm5Wc8++2z/uY8++kgbNmyIKmiS5HQ6lZ2dLZ/Pp8LCQnm9XuXm5toREQAAwGhGvCetqqpKjY2NkqTq6mrV19eroKBAra2tWrx4cXzDAQAAxIHDsiwr3iEGGxusY6hqKPmBAgG2WL9Sbncq8xgjzHVsMd+xczlznZTkUHq66+LXBzsUAAAArhwlDQAAwECUNAAAAANR0gAAAAxESQMAADCQLe9Ji7cX770z3hGAuDgTPhvvCACAQZKQJa2zs1t9fQn3ZhEj8Th37DDXADC0sNwJAABgIEoaAACAgShpAAAABkrI36RdaosFDD63OzXeEYaMWM/1mXBEwROnY/qZAIBzErKkLdj7sQKnQ/GOAVz13iy5QzyqAADxwXInAACAgShpAAAABqKkAQAAGIiSBgAAYCDbHxx47rnn1NXVpdWrV+uJJ57Q/v37NXz4cEnSj370I91zzz1R97e1tWnZsmXq7OzUmDFjVFtbq5EjR9odEwAAwCi2fpPW1NSkhoaG/uNPPvlE27Zt065du7Rr164BBU2SampqVFpaKr/fr6ysLNXV1dkZEQAAwEi2lbQTJ05o3bp1WrBggSTpyy+/VFtbm5588kkVFhZq/fr16uvrixoTDofV0tKivLw8SVJxcbH8fr9dEQEAAIxlW0lbuXKllixZolGjRkmSjh07pjvvvFPPPPOM6uvr1draqh07dkSN6erqksvlUkrKuVVYt9utjo4OuyICAAAYy5aStn37dmVmZionJ6f/3OjRo7Vx40ZlZGRo+PDhKi8v13vvvRc1zrIsORyOqHPnHwMAAAwFtjw44PP5FAgEVFRUpJMnT+r06dNauHChpk+f3r+UaVlW/zdm/yktLU3BYFCRSETJyckKBALKyMiwIyIAAIDRbClpW7Zs6f/3zp071dzcrHnz5umRRx7RnXfeqREjRuiNN97QzJkzo8Y5nU5lZ2fL5/OpsLBQXq9Xubm5dkQEAAAwWszek3bLLbfo4Ycf1v333y+Px6Pvfve7mjZtmiSpqqpKjY2NkqTq6mrV19eroKBAra2tWrx4cawiAgAAGMNhWZYV7xCDjQ3WgcHxZskdCgSG3hbrbnfqkPzvjgfmOraY79i5nLlOSnIoPd118euDHQoAAABXjpIGAABgIEoaAACAgShpAAAABrJ9g/V4+Md7J8Q7ApAQzoQj8Y4AAENWQpa0zs5u9fUl3EOrRuJJodhhrgFgaGG5EwAAwECUNAAAAANR0gAAAAyUkL9Ju9TbezH43O7UeEcYMkyc6zPhiIInTsc7BgAknIQsaX+z93MFTofjHQMYEraXZInHGQBg8LHcCQAAYCBKGgAAgIEoaQAAAAaipAEAABjI9pL23HPPqbKyUpL08ccfa/bs2fJ4PHr88ccVCoUG3N/W1qaysjLl5+eroqJCPT09dkcEAAAwjq0lrampSQ0NDZKk7u5uPfroo1q1apXefvttSdKOHTsGjKmpqVFpaan8fr+ysrJUV1dnZ0QAAAAj2VbSTpw4oXXr1mnBggWSpPfff1/jx4/XLbfcIklasWKF7rnnnqgx4XBYLS0tysvLkyQVFxfL7/fbFREAAMBYtpW0lStXasmSJRo1apQk6be//a1GjBihJUuWqKioSP/wD//Qf+0/dXV1yeVyKSXl3Ovb3G63Ojo67IoIAABgLFtK2vbt25WZmamcnJz+c5FIRPv27dPjjz+unTt36ssvv9SLL74YNc6yLDkcjqhz5x8DAAAMBbbsOODz+RQIBFRUVKSTJ0/q9OnTsixL2dnZGj16tCTp3nvv1bZt26LGpaWlKRgMKhKJKDk5WYFAQBkZGXZEBAAAMJotJW3Lli39/965c6eam5v12GOP6b777lN7e7syMzP17rvv6tZbb40a53Q6lZ2dLZ/Pp8LCQnm9XuXm5toREQAAwGgxe09aZmamVq1apQULFig/P18nT57UI488IkmqqqpSY2OjJKm6ulr19fUqKChQa2urFi9eHKuIAAAAxnBYlmXFO8RgY4N1IHa2l2QpEEisLdbd7tSE+28yFXMdW8x37FzOXCclOZSe7rr49cEOBQAAgCtHSQMAADAQJQ0AAMBAlDQAAAADUdIAAAAMZMt70uKt7t7vxDsCMGScCUfiHQEAElJClrTOzm719SXcm0WMxOPcscNcA8DQwnInAACAgShpAAAABqKkAQAAGCghf5N2qS0WMPjc7tR4RzDKmXBEwROn4x0DAHCVS8iSttR/VMdO88QZ4uMXxTeIn/cDAK4Uy50AAAAGoqQBAAAYiJIGAABgIEoaAACAgWx/cOC5555TV1eX7r33Xq1du7b/fEdHh8aNG6ef/exnUfe3tbVp2bJl6uzs1JgxY1RbW6uRI0faHRMAAMAotn6T1tTUpIaGBknSxIkTtWvXLu3atUs///nP5XK59MQTTwwYU1NTo9LSUvn9fmVlZamurs7OiAAAAEayraSdOHFC69at04IFCwZcW7NmjebMmaMbb7wx6nw4HFZLS4vy8vIkScXFxfL7/XZFBAAAMJZtJW3lypVasmSJRo0aFXX+yJEjam5u1ty5cweM6erqksvlUkrKuVVYt9utjo4OuyICAAAYy5aStn37dmVmZionJ2fAtTfeeEOlpaUaNmzYgGuWZcnhcESdO/8YAABgKLDlwQGfz6dAIKCioiKdPHlSp0+f1jPPPKMnn3xSjY2N2rx58wXHpaWlKRgMKhKJKDk5WYFAQBkZGXZEBAAAMJotJW3Lli39/965c6eam5v15JNP6vjx4zpz5oxGjx59wXFOp1PZ2dny+XwqLCyU1+tVbm6uHREBAACMFtP3pB09elTf+MY3BpyvqqpSY2OjJKm6ulr19fUqKChQa2urFi9eHMuIAAAARnBYlmXFO8RgY4N1xNMvim9QIDD4W6y73am2/F0MxFzHDnMdW8x37FzOXCclOZSe7rr49cEOBQAAgCtHSQMAADAQJQ0AAMBAlDQAAAADUdIAAAAMZMt70uLtp/nXxzsChrAzYZ4sBgBcuYQsaZ2d3errS7g3ixiJx7kBALAHy50AAAAGoqQBAAAYiJIGAABgoIT8TdqltljA4HO7U2PyOaFwRCdPnI7JZwEAEG8JWdLWv9Ohk+zdmXCemvkn8Y4AAEDMsNwJAABgIEoaAACAgShpAAAABqKkAQAAGMj2kvbcc8+psrJSkrRv3z5Nnz5d06ZN0/LlyxUKhQbc39bWprKyMuXn56uiokI9PT12RwQAADCOrSWtqalJDQ0N/cdVVVVat26d9uzZozNnzmjXrl0DxtTU1Ki0tFR+v19ZWVmqq6uzMyIAAICRbCtpJ06c0Lp167RgwYL+c5FIRN3d3YpEIurt7dU111wTNSYcDqulpUV5eXmSpOLiYvn9frsiAgAAGMu296StXLlSS5YsUXt7e/+5H//4xyovL5fL5dL111+v/Pz8qDFdXV1yuVxKSTkXy+12q6Ojw66IAAAAxrLlm7Tt27crMzNTOTk5/ecCgYBqa2u1Z88e7du3T+PGjdOzzz4bNc6yLDkcjqhz5x8DAAAMBbZ8k+bz+RQIBFRUVKSTJ0/q9OnTam5uVlZWlr71rW9JkmbPnq3FixdHjUtLS1MwGFQkElFycrICgYAyMjLsiAgAAGA0W0rali1b+v+9c+dONTc366//+q/1wx/+UMeOHdO1116rxsZGfe9734sa53Q6lZ2dLZ/Pp8LCQnm9XuXm5toREQAAwGgxe0/a2LFj9dhjj2nu3LkqLCzUJ598ouXLl0s699RnY2OjJKm6ulr19fUqKChQa2vrgG/bAAAAhgKHZVlWvEMMNjZYT0xPzfwTBQLBeMeIG7c7dUj/98cScx07zHVsMd+xczlznZTkUHq66+LXBzsUAAAArhwlDQAAwECUNAAAAANR0gAAAAxESQMAADCQbdtCxdOivOviHQE2CIV5YhcAMHQkZEnr7OxWX1/CvVnESDzODQCAPVjuBAAAMBAlDQAAwECUNAAAAAMl5LZQAAAAVyIU7tPJEz1/9PjB2BYqIR8ceMV/TN2n++IdAwAAXKUeKc6IdwSWOwEAAExESQMAADAQJQ0AAMBAlDQAAAAD2V7SnnvuOVVWVkqSdu7cqYKCAhUWFurpp5/W2bNnB9zf1tamsrIy5efnq6KiQj09f/yTFQAAAFcrW0taU1OTGhoaJEmHDx/W888/r5deeklvvfWWzp49q61btw4YU1NTo9LSUvn9fmVlZamurs7OiAAAAEayraSdOHFC69at04IFCyRJn3/+ucaPH6+MjHOPtE6aNEm/+tWvosaEw2G1tLQoLy9PklRcXCy/329XRAAAAGPZVtJWrlypJUuWaNSoUZKkW265RQcOHFB7e7sikYj8fr+OHTsWNaarq0sul0spKede3+Z2u9XR0WFXRAAAAGPZUtK2b9+uzMxM5eTk9J8bM2aMli5dqoqKCpWVlek73/mOnE5n1DjLsuRwOKLOnX8MAAAwFNiy44DP51MgEFBRUZFOnjyp06dP65lnnlFpaam8Xq8kae/evRo9enTUuLS0NAWDQUUiESUnJysQCPQvjwIAAAwltpS0LVu29P97586dam5uVkVFhWbOnKk9e/Zo2LBh2rZtm+bMmRM1zul0Kjs7Wz6fT4WFhfJ6vcrNzbUjIgAAgNFi9p60r3/961q4cKHuu+8+FRYW6s4771RhYaEkqaqqSo2NjZKk6upq1dfXq6CgQK2trVq8eHGsIgIAABjDYVmWFe8Qg40N1gEAwJV4pDhDgUDwjx7vdqd+5fikJIfS010Xv/5HfzoAAABsQ0kDAAAwECUNAADAQJQ0AAAAA9nyCo54K8u/Nt4RAADAVSwUjv8DiAlZ0jo7u9XXl3APrRrpcp5eweBgrmOHuY4d5jq2mO+rC8udAAAABqKkAQAAGIiSBgAAYKCE/E3apd7ei8HndqfGO0JchcN9OnGiJ94xAAAJJiFL2p69x3WabaEQI7NLeJoYADD4WO4EAAAwECUNAADAQJQ0AAAAA1HSAAAADGTrgwPl5eU6fvy4UlLOfcyqVavU09OjZ599Vr29vbr33nu1ZMmSAePa2tq0bNkydXZ2asyYMaqtrdXIkSPtjAoAAGAU20qaZVk6cuSI3n333f6SdubMGeXn52vr1q3KzMzUI488ovfee08TJ06MGltTU6PS0lJ5PB5t3LhRdXV1WrZsmV1RAQAAjGPbcufhw4clSfPnz9f06dO1bds2HTx4UDfccINGjx6tlJQUFRYWyu/3R40Lh8NqaWlRXl6eJKm4uHjAPQAAAInOtpJ26tQp5eTkaOPGjXrppZf0+uuvq62tTW63u/+ejIwMdXR0RI3r6uqSy+Xq//bN7XYPuAcAACDR2bbcOWHCBE2YMKH/eNasWVq/fr1uu+22/nOWZcnhcESNu9C5848BAAASnW3fpLW2tqqpqan/2LIsffOb31QgEOg/FwgElJGRETUuLS1NwWBQkUjkovcAAAAkOttKWjAY1Jo1a9Tb26vu7m41NDTo8ccf129+8xv99re/VSQS0Z49e5Sbmxs1zul0Kjs7Wz6fT5Lk9XoH3AMAAJDobFvunDRpkg4cOKAZM2aor69PpaWlmjBhglavXq1HH31Uvb29mjhxovLz8yVJVVVVmjx5sqZMmaLq6mpVVlZq06ZNyszM1Nq1a+2KCQAAYCSHZVlWvEMMNjZYRyzNLrlWgUDQ9s9xu1Nj8jlgrmOJuY4t5jt2Lmeuk5IcSk93Xfz6YIcCAADAlaOkAQAAGIiSBgAAYCBKGgAAgIEoaQAAAAay7RUc8TTt3rR4R8AQEg7zJDEAYPAlZEnr7OxWX1/CvVnESDzODQCAPVjuBAAAMBAlDQAAwECUNAAAAAMl5G/SLrXFAgaf250a7whGC4f7dOJET7xjAACuMglZ0v7nW8f1JXt3whD33ndtvCMAAK5CLHcCAAAYiJIGAABgIEoaAACAgShpAAAABrK1pJWXl8vj8aioqEhFRUU6cOCAJCkcDuuBBx7QRx99dMFxbW1tKisrU35+vioqKtTTw5NxAABgaLHt6U7LsnTkyBG9++67Skn5Px9z+PBhPfnkk/r3f//3i46tqalRaWmpPB6PNm7cqLq6Oi1btsyuqAAAAMax7Zu0w4cPS5Lmz5+v6dOna9u2bZKkHTt26KGHHtK4ceMuOC4cDqulpUV5eXmSpOLiYvn9frtiAgAAGMm2b9JOnTqlnJwcPfXUUwqHw5o7d67GjBmj5cuXS5J+8YtfXHBcV1eXXC5X/7dvbrdbHR0ddsUEAAAwkm0lbcKECZowYUL/8axZs/Tee+/prrvuuuQ4y7LkcDiizp1/DAAAkOhsW+5sbW1VU1NT/7FlWVG/TbuYtLQ0BYNBRSIRSVIgEFBGRoZdMQEAAIxkW0kLBoNas2aNent71d3drYaGBt1zzz1fOc7pdCo7O1s+n0+S5PV6lZuba1dMAAAAI9lW0iZNmqSJEydqxowZKikpUUlJSdTy5/mqqqrU2NgoSaqurlZ9fb0KCgrU2tqqxYsX2xUTAADASA7Lsqx4hxhsbLAOk9x737UKBIJX/Hfc7tRB+Tv4asx17DDXscV8x87lzHVSkkPp6a6LXx/sUAAAALhylDQAAAADUdIAAAAMREkDAAAwECUNAADAQLbtOBBPPyhMi3cEoF84zJPGAIA/XEKWtM7ObvX1JdybRYzE49wAANiD5U4AAAADUdIAAAAMREkDAAAwUEL+Ju1SWyxg8LndqXH77LPhPnWd6Inb5wMAYJeELGkf7exUbw9P1A0FueXueEcAAMAWLHcCAAAYiJIGAABgIEoaAACAgShpAAAABrL1wYHy8nIdP35cKSnnPmbVqlX6j//4D23dulUOh0NZWVmqqanRsGHDosa1tbVp2bJl6uzs1JgxY1RbW6uRI0faGRUAAMAotn2TZlmWjhw5ol27dvX/b9SoUdq8ebNef/117d69W319fXr11VcHjK2pqVFpaan8fr+ysrJUV1dnV0wAAAAj2VbSDh8+LEmaP3++pk+frm3btmnYsGGqrq6Wy+WSw+HQzTffrLa2tqhx4XBYLS0tysvLkyQVFxfL7/fbFRMAAMBIti13njp1Sjk5OXrqqacUDoc1d+5cjRkzRnfddZck6fjx43rllVf07LPPRo3r6uqSy+XqXyJ1u93q6OiwKyYAAICRbCtpEyZM0IQJE/qPZ82apffee0933XWXOjo69NBDD6mkpER33HFH1DjLsuRwOKLOnX8MAACQ6Gxb7mxtbVVTU1P/sWVZSklJ0aFDhzRnzhzNnDlTCxcuHDAuLS1NwWBQkUhEkhQIBJSRkWFXTAAAACPZVtKCwaDWrFmj3t5edXd3q6GhQZMnT9YPf/hDPfbYY5o/f/4FxzmdTmVnZ8vn80mSvF6vcnNz7YoJAABgJNtK2qRJkzRx4kTNmDFDJSUlKikp0SeffKJjx45py5YtKioqUlFRkV544QVJUlVVlRobGyVJ1dXVqq+vV0FBgVpbW7V48WK7YgIAABjJYVmWFe8Qg40N1oeO3HK3AoFgvGPEhNudOmT+W+ONuY4d5jq2mO/YuZy5TkpyKD3ddfHrgx0KAAAAV46SBgAAYCBKGgAAgIEoaQAAAAaydYP1eLmjOD3eERAjZ8M8IAIASEwJWdI6O7vV15dwD60aiSeFAACwB8udAAAABqKkAQAAGIiSBgAAYKCE/E3apd7ei8HndqfG9PPOhvrUdbInpp8JAECsJWRJ+7c3jinUzVN/ieq2H2bEOwIAALZjuRMAAMBAlDQAAAADUdIAAAAMREkDAAAwkK0PDpSXl+v48eNKSTn3MatWrdKnn36qV155RZZlaeLEiVq+fLkcDkfUuLa2Ni1btkydnZ0aM2aMamtrNXLkSDujAgAAGMW2b9Isy9KRI0e0a9eu/v+lpaXppZde0vbt2/XWW2/p448/1vvvvz9gbE1NjUpLS+X3+5WVlaW6ujq7YgIAABjJtpJ2+PBhSdL8+fM1ffp0bdu2TaNHj9bbb7+tESNG6NSpU+ru7taoUaOixoXDYbW0tCgvL0+SVFxcLL/fb1dMAAAAI9lW0k6dOqWcnBxt3LhRL730kl5//XW9//77cjqdqq+v19133y23261bbrklalxXV5dcLlf/Eqnb7VZHR4ddMQEAAIxkW0mbMGGC1qxZo9TUVKWlpWnWrFl67733JEmzZ8/WRx99pGuvvVYbNmyIGmdZ1oDfqJ1/DAAAkOhsK2mtra1qamrqP7YsS8FgUPv375ckpaSkyOPx6PPPP48al5aWpmAwqEgkIkkKBALKyOAN8wAAYGixraQFg0GtWbNGvb296u7uVkNDg0pKSrRs2TKdOnVKlmXpnXfe0W233RY1zul0Kjs7Wz6fT5Lk9XqVm5trV0wAAAAj2fYKjkmTJunAgQOaMWOG+vr6VFpaquzsbD388MOaM2eOkpOTlZ2drQcffFCSVFVVpcmTJ2vKlCmqrq5WZWWlNm3apMzMTK1du9aumAAAAEZyWJZlxTvEYGOD9cR22w8zFAgE4x0j5tzu1CH53x0PzHXsMNexxXzHzuXMdVKSQ+nprotfH+xQAAAAuHKUNAAAAANR0gAAAAxESQMAADAQJQ0AAMBAtr2CI56+d9+18Y4AG50N8eQuACDxJWRJ6+zsVl9fwr1ZxEg8zg0AgD1Y7gQAADAQJQ0AAMBAlDQAAAADJeRv0i61xQIGn9udOmh/62yoT10newbt7wEAcLVKyJJ2aGtA4SBPAF6Nbvmb6+IdAQAAI7DcCQAAYCBKGgAAgIEoaQAAAAaipAEAABjI1pJWXl4uj8ejoqIiFRUV6cCBA/3Xtm3bpvLy8guOa2trU1lZmfLz81VRUaGeHp72AwAAQ4ttT3dalqUjR47o3XffVUpK9Mf8+te/1osvvqgbbrjhgmNrampUWloqj8ejjRs3qq6uTsuWLbMrKgAAgHFs+ybt8OHDkqT58+dr+vTp2rZtmyQpFApp5cqVWrRo0QXHhcNhtbS0KC8vT5JUXFwsv99vV0wAAAAj2fZN2qlTp5STk6OnnnpK4XBYc+fO1ZgxY/Qv//IvKikp0fXXX3/BcV1dXXK5XP3fvrndbnV0dNgVEwAAwEi2lbQJEyZowoQJ/cezZs3S3//93+tb3/qWnnjiCX300UcXHGdZlhwOR9S5848BAAASnW0lrbW1VeFwWDk5OZLOla/vfve7+l//63+pqKhIp0+f1rFjx7R48WI9//zz/ePS0tIUDAYViUSUnJysQCCgjIwMu2ICAAAYybbfpAWDQa1Zs0a9vb3q7u5WQ0ODZs+erb1792rXrl16+umnlZWVFVXQJMnpdCo7O1s+n0+S5PV6lZuba1dMAAAAI9lW0iZNmqSJEydqxowZKikpUUlJSdTy5/mqqqrU2NgoSaqurlZ9fb0KCgrU2tqqxYsX2xUTAADASA7Lsqx4hxhsbLB+9brlb65TIBCMdwwjud2pzE2MMNexw1zHFvMdO5cz10lJDqWnuy5+fbBDAQAA4MpR0gAAAAxESQMAADAQJQ0AAMBAlDQAAAAD2fYy23gaW+6OdwT8kc6GeCoXAAApQUtaZ2e3+voS7s0iRuJxbgAA7MFyJwAAgIEoaQAAAAaipAEAABgoIX+TdqktFjD43O5UW/9+JNSn4yd7bP0MAABMk5Alrf2/BxQ5FYl3DAyS65d+I94RAACIOZY7AQAADERJAwAAMBAlDQAAwECUNAAAAAPZ+uBAeXm5jh8/rpSUcx+zatUqvf7669q/f7+GDx8uSfrRj36ke+65J2pcW1ubli1bps7OTo0ZM0a1tbUaOXKknVEBAACMYltJsyxLR44c0bvvvttf0iRpxYoV2rZtmzIyMi46tqamRqWlpfJ4PNq4caPq6uq0bNkyu6ICAAAYx7blzsOHD0uS5s+fr+nTp2vbtm368ssv1dbWpieffFKFhYVav369+vqiN9QOh8NqaWlRXl6eJKm4uFh+v9+umAAAAEay7Zu0U6dOKScnR0899ZTC4bDmzp2rlJQU3XnnnaqurlZqaqoeeeQR7dixQ7Nnz+4f19XVJZfL1f/tm9vtVkdHh10xAQAAjGRbSZswYYImTJjQfzxr1iwdPnxYGzdu7D9XXl4ur9cbVdIsy5LD4Yj6W+cfAwAAJDrbljtbW1vV1NTUf2xZln73u9/pnXfeiTr3f/9eTZLS0tIUDAYViZzbMSAQCFzy92sAAACJyLaSFgwGtWbNGvX29qq7u1sNDQ164IEH9Mwzz+jkyZMKh8N64403BjzZ6XQ6lZ2dLZ/PJ0nyer3Kzc21KyYAAICRbFvunDRpkg4cOKAZM2aor69PpaWluv322/Xwww/r/vvv19mzZzV16lRNmzZNklRVVaXJkydrypQpqq6uVmVlpTZt2qTMzEytXbvWrpgAAABGcliWZcU7xGBjg/XEcv3SbygQCMY7Rty53anMQ4ww17HDXMcW8x07lzPXSUkOpae7Ln59sEMBAADgylHSAAAADERJAwAAMBAlDQAAwECUNAAAAAPZ9gqOeMr8a3e8I2AQRUJ9X30TAAAJJiFLWmdnt/r6Eu7NIkbicW4AAOzBcicAAICBKGkAAAAGoqQBAAAYKCF/k3apLRYw+Nzu1D94TCQU0fGTp21IAwBAYkjIkhZ48f9T5NTZeMfAJXzjb78d7wgAABiN5U4AAAADUdIAAAAMREkDAAAwkK2/SSsvL9fx48eVknLuY1atWqW+vj49++yz6unp0Xe+8x2tXr1aw4YNixrX1tamZcuWqbOzU2PGjFFtba1GjhxpZ1QAAACj2PZNmmVZOnLkiHbt2tX/v7Fjx+rRRx/VqlWr9Pbbb0uSduzYMWBsTU2NSktL5ff7lZWVpbq6OrtiAgAAGMm2knb48GFJ0vz58zV9+nRt27ZN77//vsaPH69bbrlFkrRixQrdc889UePC4bBaWlqUl5cnSSouLpbf77crJgAAgJFsW+48deqUcnJy9NRTTykcDmvu3LmaPn26RowYoSVLlujw4cP6b//tv6mysjJqXFdXl1wuV/8SqdvtVkdHh10xAQAAjGTbN2kTJkzQmjVrlJqaqrS0NM2aNUs//elPtW/fPj3++OPauXOnvvzyS7344otR4yzLksPhiDp3/jEAAECis62ktba2qqmpqf/Ysix9/etf17hx4zR69GglJyfr3nvv1cGDB6PGpaWlKRgMKhKJSJICgYAyMjLsigkAAGAk20paMBjUmjVr1Nvbq+7ubjU0NGjDhg369NNP1d7eLkl69913deutt0aNczqdys7Ols/nkyR5vV7l5ubaFRMAAMBItv0mbdKkSTpw4IBmzJihvr4+lZaWKjs7W6tWrdKCBQvU29ur7373u/q7v/s7SVJVVZUmT56sKVOmqLq6WpWVldq0aZMyMzO1du1au2ICAAAYyWFZlhXvEIONvTvN942//bYCgWC8Y1xV3O5U5ixGmOvYYa5ji/mOncuZ66Qkh9LTXRe/PtihAAAAcOUoaQAAAAaipAEAABiIkgYAAGAgShoAAICBbHsFRzy5H/5WvCPgK0RCkXhHAADAaAlZ0jo7u9XXl3BvFjESj3MDAGAPljsBAAAMREkDAAAwECUNAADAQAn5m7RLbbFwNYqEIjp+8nS8YwAAgBhKyJIW+Pl/KHIqHO8Yg+Ybj38v3hEAAECMsdwJAABgIEoaAACAgShpAAAABqKkAQAAGMjWBwfKy8t1/PhxpaSc+5h77rlHv/zlL/uvd3R0aNy4cfrZz34WNa6trU3Lli1TZ2enxowZo9raWo0cOdLOqAAAAEaxraRZlqUjR47o3Xff7S9pkvSjH/1IkhQIBHT//ffriSeeGDC2pqZGpaWl8ng82rhxo+rq6rRs2TK7ogIAABjHtuXOw4cPS5Lmz5+v6dOna9u2bVHX16xZozlz5ujGG2+MOh8Oh9XS0qK8vDxJUnFxsfx+v10xAQAAjGTbN2mnTp1STk6OnnrqKYXDYc2dO1djxozRXXfdpSNHjqi5uVk/+clPBozr6uqSy+Xq//bN7Xaro6PDrpgAAABGsq2kTZgwQRMmTOg/njVrlt577z3dddddeuONN1RaWqphw4YNGGdZlhwOR9S5848BAAASnW3Lna2trWpqauo/tiyr/9uxxsZGFRQUXHBcWlqagsGgIpGIpHO/XcvIyLArJgAAgJFsK2nBYFBr1qxRb2+vuru71dDQoHvuuUfHjx/XmTNnNHr06AuOczqdys7Ols/nkyR5vV7l5ubaFRMAAMBItpW0SZMmaeLEiZoxY4ZKSkpUUlKiCRMm6OjRo/rGN74x4P6qqio1NjZKkqqrq1VfX6+CggK1trZq8eLFdsUEAAAwksOyLCveIQZbIm6wHggE4x3jgtzuVGOzJRrmOnaY69hhrmOL+Y6dy5nrpCSH0tNdF78+2KEAAABw5ShpAAAABqKkAQAAGIiSBgAAYCBKGgAAgIFs23EgntwP3RLvCIMqEorEOwIAAIixhCxpnZ3d6utLuDeLAACAIYTlTgAAAANR0gAAAAxESQMAADBQQv4m7VJbLCSCSCii4ydPxzsGAACwUUKWtMA//S/1nQrFO4Ztrlt8e7wjAAAAm7HcCQAAYCBKGgAAgIEoaQAAAAaipAEAABjI1gcHysvLdfz4caWknPuYVatWKRgMas2aNerr69Of/umf6umnn9awYcOixrW1tWnZsmXq7OzUmDFjVFtbq5EjR9oZFQAAwCi2fZNmWZaOHDmiXbt29f9v3Lhxqqqq0rp167Rnzx6dOXNGu3btGjC2pqZGpaWl8vv9ysrKUl1dnV0xAQAAjGRbSTt8+LAkaf78+Zo+fbq2bdsmSYpEIuru7lYkElFvb6+uueaaqHHhcFgtLS3Ky8uTJBUXF8vv99sVEwAAwEi2LXeeOnVKOTk5euqppxQOhzV37lyNGTNGP/7xj1VeXi6Xy6Xrr79e+fn5UeO6urrkcrn6l0jdbrc6OjrsigkAAGAk275JmzBhgtasWaPU1FSlpaVp1qxZ2rNnj2pra7Vnzx7t27dP48aN07PPPhs1zrIsORyOqHPnHwMAACQ620paa2urmpqa+o8ty9JHH32km2++Wd/61reUlJSk2bNnq7m5OWpcWlqagsGgIpGIJCkQCCgjI8OumAAAAEayraT951Ocvb296u7uVkNDg37605/q4MGDOnbsmCSpsbFR3/ve96LGOZ1OZWdny+fzSZK8Xq9yc3PtigkAAGAk236TNmnSJB04cEAzZsxQX1+fSktLNWHCBD322GOaO3eukpOTdcMNN2jVqlWSpKqqKk2ePFlTpkxRdXW1KisrtWnTJmVmZmrt2rV2xQQAADCSw7IsK94hBttQ2GA9EAjGO4Ykye1ONSZLomOuY4e5jh3mOraY79i5nLlOSnIoPd118euDHQoAAABXjpIGAABgIEoaAACAgShpAAAABqKkAQAAGMi2V3DEk3v++HhHsFUkFIl3BAAAYLOELGmdnd3q60u4N4sAAIAhhOVOAAAAA1HSAAAADERJAwAAMFBC/ibtUlss4KtFQmd1/OSX8Y4BAMCQlpAlLbDlQ/UFe+Md46p13aKJ8Y4AAMCQx3InAACAgShpAAAABqKkAQAAGIiSBgAAYCBbHxwoLy/X8ePHlZJy7mNWrVqlQ4cO6ec//7mSk5N1xx13qLKysv/6f2pra9OyZcvU2dmpMWPGqLa2ViNHjrQzKgAAgFFs+ybNsiwdOXJEu3bt6v9famqqnn/+eb300kt66623dPbsWW3dunXA2JqaGpWWlsrv9ysrK0t1dXV2xQQAADCSbSXt8OHDkqT58+dr+vTp2rZtmz7//HONHz9eGRkZkqRJkybpV7/6VdS4cDislpYW5eXlSZKKi4vl9/vtigkAAGAk20raqVOnlJOTo40bN+qll17S66+/rpMnT+rAgQNqb29XJBKR3+/XsWPHosZ1dXXJ5XL1L4G63W51dHTYFRMAAMBItv0mbcKECZowYUL/8axZs3T48GEtXbpUFRUV+trXvqb8/Hz927/9W9Q4y7LkcDiizp1/DAAAkOhsK2mtra0Kh8PKycmRdK58paSk6Pvf/768Xq8kae/evRo9enTUuLS0NAWDQUUiESUnJysQCPQvjwIAAAwVti13BoNBrVmzRr29veru7lZDQ4OmTJmiefPmqbu7W6FQSNu2bVNBQUHUOKfTqezsbPl8PkmS1+tVbm6uXTEBAACMZNs3aZMmTdKBAwc0Y8YM9fX1qbS0VLfddpsWLlyo++67T2fPntW0adNUWFgoSaqqqtLkyZM1ZcoUVVdXq7KyUps2bVJmZqbWrl1rV0wAAAAjOSzLsuIdYrCxwfqVuW7RRAUCwcu61+1Ovex7cWWY69hhrmOHuY4t5jt2Lmeuk5IcSk93Xfz6YIcCAADAlaOkAQAAGIiSBgAA/v/27jU2inqN4/hvaVcugYgLW0FsSDCoCcRKQrQooZbLdtntAi4kQrGF0zQK4WC8Em6mIUEkhFgFrfEF3iGWICBgrVSIDZeGW5CGgIYI1GLrUqChLZZ2uzvnhUkj0Aqe7nSnw/fzqjOdf/vMkyfNLzvTGVgQIQ0AAMCCTH3Bery4/5Ma7xK6tUhLa7xLAADgrmfLkHb5cqOiUdv90yoAALiLcLkTAADAgghpAAAAFkRIAwAAsCBb3pP2T0/v7Y4iLa26crUp3mUAAIAuZMuQdumzMkUb7BNqkv7rjXcJAACgi3G5EwAAwIIIaQAAABZESAMAALAgQhoAAIAFmRrS9u7dq2AwqMmTJ2vlypWSpIMHDyoQCMjj8aigoKDdddXV1Zo9e7a8Xq/mz5+va9eumVkmAACA5ZgW0qqqqpSfn6/CwkLt2LFDp06dUllZmZYuXarCwkIVFxfr5MmTKisru2XtihUrlJWVpZKSEo0cOVKFhYVmlQkAAGBJpoW00tJS+Xw+DRo0SE6nUwUFBerdu7eGDh2q5ORkJSYmKhAIqKSk5IZ14XBYR44cUUZGhiQpGAzecgwAAIDdmfactMrKSjmdTs2bN081NTV65plnNHz4cLnd7rZjkpKSFAqFblhXV1envn37KjHxr9LcbvctxwAAANidaSEtEono6NGj+uKLL9SnTx/Nnz9fvXr1ksPhaDvGMIwbtjvad/M2AACA3ZkW0gYOHKgxY8bI5XJJkiZOnKiSkhIlJCS0HVNbW6ukpKQb1rlcLjU0NCgSiSghIaHdYwAAAOzOtHvS0tPTtX//ftXX1ysSiWjfvn3yer06d+6cKisrFYlEtGvXLo0bN+6GdU6nU6NHj1ZxcbEkafv27bccAwAAYHemfZKWkpKivLw8ZWVlKRwO6+mnn9asWbM0bNgwLVy4UM3NzUpLS5PX+9d7KZctW6bx48drwoQJys/P1+LFi/Xhhx9q8ODBeuedd8wqEwAAwJIchmEY8S4i1uz4gvXa2oZ4l9Eut7ufZWuzG3rddeh116HXXYt+d5076XWPHg4NGNC34+/HuigAAAB0HiENAADAgghpAAAAFkRIAwAAsCBCGgAAgAWZ9giOeBo4Jy3eJcRUpKU13iUAAIAuZsuQdvlyo6JR2z1ZBAAA3EW43AkAAGBBhDQAAAALIqQBAABYkC3vSfunVyzciUhLq65ctc9rpQAAQPdjy5B26fPvFW348/9en7Tg2RhWAwAA8O9xuRMAAMCCCGkAAAAWREgDAACwIEIaAACABZka0vbu3atgMKjJkydr5cqVbfvD4bDmzJmjQ4cOtbuuurpas2fPltfr1fz583Xt2jUzywQAALAc00JaVVWV8vPzVVhYqB07dujUqVMqKyvT2bNnlZ2drePHj3e4dsWKFcrKylJJSYlGjhypwsJCs8oEAACwJNNCWmlpqXw+nwYNGiSn06mCggKlpKRoy5YtysvLU0pKSrvrwuGwjhw5ooyMDElSMBhUSUmJWWUCAABYkmkhrbKyUpFIRPPmzdPUqVO1adMm3XvvvVq0aJEmTpzY4bq6ujr17dtXiYl/PcLN7XYrFAqZVSYAAIAlmRbSIpGIysvLtWrVKhUVFamiokLbtm277TrDMORwOG7Yd/M2AACA3ZkW0gYOHKgxY8bI5XKpV69emjhxoioqKm67zuVyqaGhQZFIRJJUW1urpKQks8oEAACwJNNCWnp6uvbv36/6+npFIhHt27dPI0aMuO06p9Op0aNHq7i4WJK0fft2jRs3zqwyAQAALMm0kJaSkqK8vDxlZWXJ5/PpgQce0PTp0zs8ftmyZdqzZ48kKT8/X5s3b5bP59PRo0f18ssvm1UmAACAJTkMwzDiXUSsxeIF67W1DTGsyL7c7n70qovQ665Dr7sOve5a9Lvr3Emve/RwaMCAvh1/P9ZFAQAAoPMIaQAAABZESAMAALAgQhoAAIAFEdIAAAAsKDHeBZhhYE5Gp9ZHWlpjVAkAAMD/x5Yhra7umqLRzj1ZpEcPXkV1p+hV16HXXYdedx163bXod9e5Xa9v931bPicNAACgu+OeNAAAAAsipAEAAFgQIQ0AAMCCCGkAAAAWREgDAACwIEIaAACABRHSAAAALIiQBgAAYEGENAAAAAuyVUjbuXOnfD6fPB6PNm7cGO9ybCc7O1t+v19Tp07V1KlTdeLECR08eFCBQEAej0cFBQXxLrHba2xsVGZmpi5cuCBJHfb39OnTCgaDysjI0LJly9Tayvtm/62be71kyRJ5PJ62+S4tLZVEr2Ph/fffl9/vl9/v15o1ayQx22Zpr9fMtjnee+89+Xw++f1+ffLJJ5JMmGvDJv744w8jPT3dqKurM65du2YEAgHjzJkz8S7LNqLRqDF27FgjHA637WtqajLS0tKM3377zQiHw0Zubq7x448/xrHK7u2nn34yMjMzjREjRhhVVVX/2F+/328cP37cMAzDWLJkibFx48Y4Vt793NxrwzCMzMxMIxQK3XIsve6cAwcOGM8995zR3NxstLS0GDk5OcbOnTuZbRO01+vdu3cz2yY4dOiQMXPmTCMcDhtNTU1Genq6cfr06ZjPtW0+STt48KBSU1PVv39/9enTRxkZGSopKYl3WbZx9uxZSVJubq6mTJmiL7/8UhUVFRo6dKiSk5OVmJioQCBAzzth8+bNys/PV1JSkiR12N/ff/9d169f1+OPPy5JCgaD9P1furnXTU1Nqq6u1tKlSxUIBLRu3TpFo1F6HQNut1uLFy/WPffcI6fTqYceekjnz59ntk3QXq+rq6uZbRM88cQT+vzzz5WYmKjLly8rEomovr4+5nOdaOI5dKmLFy/K7Xa3bSclJamioiKOFdlLfX29xowZozfffFPhcFg5OTnKy8u7peehUCiOVXZvb7311g3b7c10KBS6Zb/b7abv/9LNvb506ZJSU1OVn5+vfv366cUXX9SWLVs0fPhwet1Jw4cPb/v6/Pnz+u677/T8888z2yZor9cbN27U4cOHmW0TOJ1OrVu3Th9//LG8Xq8pf7Nt80laNBqVw+Fo2zYM44ZtdM6oUaO0Zs0a9evXTy6XSzNmzNC6devouYk6mmlmPfaSk5P1wQcfKCkpSb1791Z2drbKysrodQydOXNGubm5WrRokZKTk5ltE/2918OGDWO2TfTSSy+pvLxcNTU1On/+fMzn2jYhbdCgQaqtrW3brq2tbbuUgc47evSoysvL27YNw9CQIUPouYk6mumb91+6dIm+d9Ivv/yi77//vm3bMAwlJibS6xg5duyY5s6dq9dee03PPvsss22im3vNbJvj119/1enTpyVJvXv3lsfj0aFDh2I+17YJaU899ZTKy8t15coVNTU1affu3Ro3bly8y7KNhoYGrVmzRs3NzWpsbNS2bdv06quv6ty5c6qsrFQkEtGuXbvoeQylpKS0298hQ4aoZ8+eOnbsmCTpm2++oe+dZBiGVq1apatXryocDquoqEiTJk2i1zFQU1OjBQsWaO3atfL7/ZKYbbO012tm2xwXLlzQ8uXL1dLSopaWFu3Zs0czZ86M+Vzb5p60+++/X6+88opycnIUDoc1Y8YMPfbYY/EuyzbS09N14sQJTZs2TdFoVFlZWRo1apRWr16thQsXqrm5WWlpafJ6vfEu1TZ69uzZYX/Xrl2r5cuXq7GxUSNGjFBOTk6cq+3eHn30Ub3wwguaNWuWWltb5fF4lJmZKYled9aGDRvU3Nys1atXt+2bOXMms22CjnrNbMdeWlqaKioqNG3aNCUkJMjj8cjv98vlcsV0rh2GYRhmnggAAAD+Pdtc7gQAALATQhoAAIAFEdIAAAAsiJAGAABgQYQ0AAAACyKkAYAJcnNzdeXKlXiXAaAbI6QBgAkOHDgQ7xIAdHOENAB3nS1btsjv9ysQCCgnJ0c1NTUqKipSZmampkyZotzcXJ07d06StHjxYm3YsKFt7d+3x48fr/Xr1ysrK0vp6el69913JUlLliyRJM2ZM0c1NTVde3IAbMM2bxwAgDvx888/a+3atdq2bZsGDx6sTz/9VHPnzlU0GlVRUZFcLpe2bt2qBQsW6Ntvv73tz/vzzz+1adMmhUIhTZo0SdOnT9fbb7+trVu36rPPPpPL5eqCswJgR3ySBuCuUl5errFjx2rw4MGSpLlz52rChAny+XxtgSoYDCoUCunChQu3/XkTJkyQ9Ner6QYMGKCrV6+aVzyAuwohDcBdJSEhQQ6Ho237+vXrqqqquuU4wzDU2toqh8Ohv789LxwO33Bcz549276++VgA6AxCGoC7ypNPPqny8nJdvHhRkvTVV1+prKxMxcXFbf+N+fXXX6t///4aOnSo7rvvPp08eVKSFAqFdPjw4Tv6PQkJCWptbTXnJADcFbgnDcBd5ZFHHtEbb7yhvLw8SZLb7VZpaal++OEHzZkzR9FoVC6XSx999JF69Oih7Oxsvf7668rIyNCDDz6o1NTUO/o9Xq9X2dnZWr9+vR5++GEzTwmATTkMPpsHAACwHC53AgAAWBAhDQAAwIIIaQAAABZESAMAALAgQhoAAIAFEdIAAAAsiJAGAABgQYQ0AAAAC/of0h/pMUF/zlgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Visualizing the number of personal loan customers per city\n",
"plt.figure(figsize=(10,20))\n",
"sns.countplot(data=data,y='Age')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "d05457fe",
"metadata": {},
"source": [
"Majority of customer database are between 29 to 38 and age of 49, age of 49 is the top count in the customer database."
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "92b14eee",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Displaying the distrubtion plot for 'DurationOfPitch' and 'MonthlyIncome'\n",
"colnames = ['DurationOfPitch','MonthlyIncome']\n",
"for name in colnames:\n",
" sns.displot(data=data, x=name)"
]
},
{
"cell_type": "markdown",
"id": "9a482f54",
"metadata": {},
"source": [
"Duration of pitch is right skewed, with majority of pitches have a maximum of 20 minutes or less.\n",
"Monthly income is also right skewed with majority of salaries up to 35,000."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "f4f8ee2f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'TypeofContact', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "58611aae",
"metadata": {},
"source": [
"71% of customer base self inquired."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "3d269d24",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'CityTier', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "54a05e17",
"metadata": {},
"source": [
"1st Tier City tour is having the highest interest among customers."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "9d1eeff6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'Occupation', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "6b33f917",
"metadata": {},
"source": [
"Above 80% of the customers are between salaried and working in small business."
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "3a31ccee",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'Gender', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "a12100fa",
"metadata": {},
"source": [
"Around 60% of customers are males."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "9ab1c470",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'NumberOfPersonVisiting', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "33c7c2c3",
"metadata": {},
"source": [
"Close to 50% of customers mentioned that 3 persons will be on the travel package, 29% 2 persons and 21% 4 persons."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "c301415a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'NumberOfFollowups', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "f050bd9b",
"metadata": {},
"source": [
"42% of customers customers had 4 followup calls, 30% had 3"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "df9f962e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'ProductPitched', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "3ce94f28",
"metadata": {},
"source": [
"Basic, Deluxe packages are having the highest interest among the customer database."
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "f8b7b713",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'PreferredPropertyStar', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "69409d23",
"metadata": {},
"source": [
"3 Star hotels, are having the highest interest"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "e2aa116c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EwlgaREQkjKVBRETCWBpERCSMpUFERMJYGkREJIylQUREwlgaREQkTK+lkZGRgQ4dOuDOnTsAgOPHj8PX1xceHh6YN2+ebrnLly+jS5cu8PT0xMSJE5GbmwsASE5ORq9eveDl5YXBgwcjMzNTn3HfiSRJCA0NxoYN6wAAT548xqRJ49GzZxf0798LW7dueu1zTJgQiLlzZ+puR0b+jG7dOuHzz/sgOTlJN/7NN8ORkHCr6FeCiOg19FYacXFx6NmzJxISEgAAWVlZmDBhAhYvXoyYmBjEx8fj8OHDAIDAwEBMmjQJu3fvhiRJCA8PBwBMmTIF/v7+iI2NhZOTExYvXqyvuO8kIeEWRowYjEOH9uvGFiyYizJlyuCnn7Zg6dLVOHnyOI4d+89Ln2P9+jW4cOF8obF168LRs2dvRERsAQAcOLAP1avXRPXqNfSzMkREr6C30ggPD0dwcDA0Gg0A4MKFC6hWrRqqVq0KlUoFX19fxMbGIikpCVlZWXB2dgYAdOnSBbGxsdBqtTh9+jQ8PT0LjBujiIhwdOjgB3f3trqxq1cvw9PTG0qlEmq1Gs2atShQKn917twZ/PLLCXTq9GmBcaVShezsLGRkZECtViMrKwsbN65D//4D9Lo+REQvI3yW2zc1derUArfv378Pa2tr3W2NRoOUlJRC49bW1khJScHDhw9Rrlw5qFSqAuNvysqq3Fuugbjp00MBABcvnkW5cuawtrZA48aNcPjwXrRu3QI5OTk4fvww1Go1rK0tCjw2JSUFixbNw/Lly7F582bk5mbplhkz5huMHDkYGo0GM2fOxLp169CvXwCqVaus93Wid/f3/9dExYHeSuPv8vPzoVAodLclSYJCoXjp+P/+/Ku/3xaRnp6B/HzDXGUwK0uLjIxspKY+xRdffIVFi+bD17cjLC0rwdX1n7h48QJSU5/qls/NzcXIkSMxZMhIKBRlkJmZjefPc3TLNG7cHCtXNgcA3LlzB6dPn4W/f38EBU3G7duJcHVtgh49ehtk3UTwl2RBf/1/TWQqSpVSvPLNtsFKo3LlygWuK56amgqNRlNoPC0tDRqNBpaWlnj69Cny8vKgVCp1y5uKzMxMDBkyHOXLVwAArF27ElWqVCmwzJUrl5CcnITvv//zoIAHD9KRn5+HnJwcjBv3bYFlv/9+LoYOHYEzZ07h2bNMzJ4dhlGjhqJFi5aoUqWqYVaKTJYkSZg6dTJq1rSHv38f5OXlYd68Wfj113MAgI8++hhDh44o9MYsKGiM7kAWALh7NwnOzo0xc+Y8REb+jA0b1sLCojy++24GPvjADsCfB2p89dUozrsVUwY75LZhw4a4desWEhMTkZeXh+joaLi5ucHOzg7m5uY4e/YsACAqKgpubm5Qq9VwdXVFTEwMACAyMhJubm6GivvOoqJ+xvLlSwD8WQY7dkShXTuvAss4OTVARMROrF69AatXb0CnTl3QunW7QoVx7Nh/UKmSBnXqOCAnJwdKpRIKhQIKhQLZ2dkGWycyTS86UGP37hj88Uci1qzZhNWrN+LXX8/h4MHCc26hobN0P59jx05EuXIWGD16LAAeqFFSGaw0zM3NMWPGDAwbNgze3t6oWbMmvLz+/CU6Z84cTJ8+HV5eXnj27BkCAgIAAMHBwQgPD4e3tzfOnDmDkSNHGiruO+vTpx9SU++jT59uGD58ML74YhA+/NARALB8+RJdobxOTk4OVq9ejoEDBwMAmjT5CPfu3UP37n6wtf0AtWrZ620dqHh40YEa+fl5eP78ObRaLXJycqDVamFmZvbS59BqtZg6dTKGD/8aNjZ/zqnxQI2SSSFJkmF2+MvEkHMaJZ21tQVcAtfKHcMonJ0dYHRzGlOnTkaNGrV0u6cCA0fi0qV45OXloUmTppg6dfZLH7tt21YcOnQAYWH/f9j7oUP7sWrVMlhZVUJQ0BRs2bIJ1apVh5eXjyFWh/TkdXMa/EY4UQm0atUyVKz4Pnbs2INt22Lw5MkTbNz400uX37x5A/r27V9grFWrNlizZhPmzl2I58+f49KleHh4tEdY2L/xzTfDsWnTy5+PTJfBJsKN1fsVy0KtUsodwyhoc/Pw6OEzuWOQARw+fACjRo2BWq2GWq1G+/YdcOjQfvTsWfhovGvXriAvLw+NGrm89Pl4oEbJUeJLQ61SYtvZG3LHMAqdXTg/UlLUqeOAAwf2onFjV+Tm5uLo0SOoV8/phcv++us5uLi4vvSQ978eqHH06BEeqFHMcfcUUQk0fPhoPH36FP7+n6JfP39oNBr06tUXQOEDNW7fvo3KlW1f+Dw8UKPkKfET4dbWFvyk8V+dXezfafKWE+H/zxgnwolEcCKciIiKTImf0yAyVu9XLA21Si13DKOgzdXi0cMsuWMQWBpERkutUiP6N+O8HIChdXAcAoClYQy4e4qIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiI3oAkSQgNDcaGDesK3TdhQiDmzp35wsfl5eUhLOzf8Pf/FN27+yEycqvuvsjIn9GtWyd8/nkfJCcn6ca/+WY4EhJuFf1KvAOWBhGRoISEWxgxYjAOHdpf6L7169fgwoXzL31sVFQEbt9OxNq1m7Fs2VqEh2/EpUvxuseuWxeOnj17IyJiCwDgwIF9qF69JqpXr6GflXlLLA0iIkEREeHo0MEP7u5tC4yfO3cGv/xyAp06ffrSxx45chDe3h2hUqlQvnx5tGnjgT17dgEAlEoVsrOzkJGRAbVajaysLGzcuA79+w/Q6/q8DZYGEZGg0aPHwsPDq8BYWloqwsL+jUmTQlGq1Mt/pd6/nwKNxkZ3W6Oxwf379wEAX345FMOGDcKRIwfxr3/1wJo1K/Dpp91Qtux7+lmRd6CSOwARkanKzc3F5MkTMXz4aFSqVOmVy+bnS1AoFLrbkiTpSqZVqzZo1aoNACAp6Q4uXYrHgAGDERb2b9y+nQhX1ybo0aO3/lbkDbA0iIje0pUrl5CcnITvv58HAHjwIB35+XnIycnBuHHfFljWxqYy0tJSdbfT0lKh0WgKPef338/F0KEjcObMKTx7lonZs8MwatRQtGjRElWqVNXvCglgaRARvSUnpwaIiNipu71ixVI8fvwIo0ePLbTsJ5+4YefO7fj440/w/Plz7N+/B998M77AMseO/QeVKmlQp44Djh49AqVSCYVCAYVCgezsbL2vjwjOaRAR6cny5UuwfPkSAICfX1fY2VVBv37+GDAgAD4+ndCokYtu2ZycHKxevRwDBw4GADRp8hHu3buH7t39YGv7AWrVspdlHf5OIUmSJHcIfUpPz0B+/stX0draAtvO3jBgIuPV2cUeqalP3/rx1tYWcAlcW4SJTNfZ2QHvtC2BP7dn9G+LiyiRaevgOOSdtyeJKVVKASurci+/34BZiIjIxHFOg4hKBIsKapQ2Ky13DKOQlZOFp4+1b/VYlgYRlQilzUrD/fuP5Y5hFA4OO4aneLvS4O4pIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYbJc7rVPnz548OABVKo///mQkBBkZmZi+vTpyM7ORvv27TFq1CgAwOXLlzFx4kRkZmbC1dUVU6ZM0T2OiIgMy+C/fSVJQkJCAg4ePKj75Z+VlQUvLy+sW7cOtra2GDRoEA4fPoyWLVsiMDAQoaGhcHZ2xoQJExAeHg5/f39DxyYiIsiwe+rmzZsAgP79+6Njx4746aefcOHCBVSrVg1Vq1aFSqWCr68vYmNjkZSUhKysLDg7OwMAunTpgtjYWENHJiKi/zJ4aTx58gTNmjXDokWLsHr1amzatAnJycmwtrbWLaPRaJCSkoL79+8XGLe2tkZKSoqhIxMR0X8ZfPdUo0aN0KhRI93trl27YsGCBXBxcdGNSZIEhUKB/Px8KBSKQuNvwsqq3LuHLkGsrS3kjlBscFsWLW7PovW229PgpXHmzBlotVo0a9YMwJ9FYGdnh9TUVN0yqamp0Gg0qFy5coHxtLQ0aDSaN/r30tMzkJ8vvfR+/iAWlJr69K0fy21Z0LtsS4Db8++4PYvWy7ZnqVKKV77ZNvjuqadPn2LWrFnIzs5GRkYGtm3bhtGjR+PWrVtITExEXl4eoqOj4ebmBjs7O5ibm+Ps2bMAgKioKLi5uRk6MhER/ZfBP2m4u7sjLi4Ofn5+yM/Ph7+/Pxo1aoQZM2Zg2LBhyM7ORsuWLeHl5QUAmDNnDoKCgpCRkQFHR0cEBAQYOjIREf2XLF94GDlyJEaOHFlgrFmzZti+fXuhZR0cHLB161YDJSMiolfhN8KJiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISJhJlMaOHTvg7e0NDw8PrF+/Xu44REQllkruAK+TkpKCefPmISIiAmZmZujRoweaNm0Ke3t7uaMREZU4Rv9J4/jx4/joo4/w/vvvo2zZsvD09ERsbKzcsYiISiSj/6Rx//59WFtb625rNBpcuHBB+PGlSileu0xZM6PfDAYjsr1exbbie0WUxPS967YEgDJqiyJIUjwUxfa0sahcBEmKh5dtz9dtZ6P/bZmfnw+F4v9XQpKkArdfp6LALzHP+tXfJlqxZGVV7p0eHz3h0yJKYvredVsCQJs6fYogSfFQFNtzU7+fiyBJ8fC229Pod09VrlwZqamputupqanQaDQyJiIiKrmMvjSaN2+OEydO4MGDB3j+/Dn27NkDNzc3uWMREZVIRr97ysbGBqNGjUJAQAC0Wi26du2KBg0ayB2LiKhEUkiSJMkdgoiITIPR754iIiLjwdIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgohe6c+cOcnNzodVq5Y5CRoSlQUQ6kiRh0aJFcHZ2hoeHB+7evYsxY8ZgwoQJLA8CYAKnESkuHBwchM/Oe/nyZT2nMX0BAQHCy65du1aPSYqXVatWYevWrfjuu+/w7bffAgC8vb0RHByM+fPnIzAwUOaExq+4v9ZZGgYyc+ZM3Q/SnTt3sGzZMvj7+8PZ2RlqtRoXL17ETz/9hIEDB8qc1DT84x//0P39+fPn2LlzJ+rXr4+GDRtCrVYjPj4e586dQ/fu3WVMaXq2bt2KSZMmwd3dHZMmTQIAtGvXDmq1GpMnT2ZpCCj2r3WJDK579+7Stm3bCo3v3LlT6tSpk8HzmLpvvvlGmjNnTqHxRYsWSYMGDZIhkemqX7++9Mcff0iSJEnOzs66vyckJEj169eXM5pJKo6vdc5pyODy5ctwdnYuNO7g4ICEhASD5zF1+/btw6efFr74k7e3N06cOCFDItNVo0YNnDlzptD47t27UaNGDRkSmbbi+FpnacigVq1a2LhxY4Gx/Px8rFy5EvXq1ZMplemytLR84S+6I0eOoHJlXt7zTQwbNgwhISGYPXs28vLysH37dowZMwZhYWGmuztFRsXxtc5To8vgxIkTGDRoEGxtbVGvXj1IkoSLFy/i8ePHWL16NZycnOSOaFLWr1+PGTNmwM/PT7c9z58/j127dmH69Onw9fWVO6JJOXz4MJYuXYrLly9DrVbD3t4egwcPxieffCJ3NJNTHF/rLA2ZJCYmIjw8HDdu3IBCoYCDgwN69OjBd8ZvKTIyEhs2bMD169cBAB9++CH69++Ptm3bypyMSrri9lpnacgsNzcXSqVS+BA9oqK2ZMkS4WW//PJLPSYp3orLa52lIZPIyEgsWbIEd+7cwa5du7B8+XJoNBoMHTpU7mgmKTExEStXrsTNmzcxZ84c7Nu3D/b29mjatKnc0Yxe69athZZTKBTYv3+/ntMUP8Xttc7vacggMjIS06ZNQ//+/bF48WIAfx5NMXPmTJiZmWHAgAEyJzQtcXFx6Nu3Lxo3bozz588jJycHN27cwLRp07Bw4UK4u7vLHdGoHThwQO4IxVaxfK3LdKhviebr6ytt375dkqSCx8Jv375datu2rZzRTFLv3r2lhQsXSpJUcHvOnTtX6ty5s5zRTE5SUtIL/0tOTpZSU1OlvLw8uSOalOL4WucnDRkkJia+8NhtZ2dnpKSkGD6Qibt06RJCQ0MLjXft2hVr1qyRIZHpat269Sv3uZuZmcHb2xuTJ0+Gubm5AZOZpuL4Wuf3NGRga2uLK1euFBo/ceIEbG1tZUhk2sqUKYP09PRC47du3UK5cuVkSGS6QkNDUaFCBQQFBWHbtm3Ytm0bgoODUbFiRQwdOhTTpk3D+fPnMX/+fLmjmoTi+FrnJw0Z9O/fH5MnT0ZqaiokScKpU6cQERGB1atXY/To0XLHMzkdOnTA9OnTMWPGDCgUCmRnZ+PEiRP47rvv4OXlJXc8k7Jq1SqEhITAw8NDN+bg4ABra2vMnz8fO3bsgLW1NQIDAzF27FgZk5qG4vha59FTMtmwYQOWLl2q+4hqY2ODwYMHo0ePHjInMz05OTkYM2YMYmNjAUC3e6V9+/aYOnUqypQpI2c8k9KwYUNs27YNNWvWLDB+8+ZN+Pn54cKFC7h79y68vLwQFxcnU0rTUtxe6ywNGeXk5CAjIwNmZmZ48uQJPvjgA7kjmbTExETdt5hr165d4Ey4JKZHjx6oU6cOpkyZoitfSZIQHByM+Ph4REREICYmBgsWLNCVNIl58OABzMzMTH6XKUtDBqmpqRgxYgRcXV11H1GbNWsGe3t7hIWFwdLSUuaExi8lJQU2Nja6v7/K/5aj14uLi0P//v1haWmJ+vXrIz8/H7/99hvS09Px448/QqVSISAgAGPHjkWvXr3kjmuUduzYAU9PT5iZmWHHjh2vXNYUT3HD0pDBiBEjkJaWhqlTp6J69eoAgN9//x2TJk2CnZ0dZs2aJW9AE/Dhhx/i6NGjsLKyeulFbyRJgkKhMMkL3cgpJSUFmzdvxqVLl6BSqVC3bl34+/vDysoKv//+O+7evYsWLVrIHdNoOTg44NixY7qfzZcx1Z9NloYMmjRpgvXr16N27doFxq9cuYJ+/frh5MmTMiUzHadOnULjxo2hUqlw6tSpVy7bpEkTA6UiKigtLQ2VKlWSO0aR4tFTMlAoFHj+/Hmh8by8PF6HWdBfi2DRokUICgoqVML05p49e4a1a9fi119/hVarxd/fU65cuVKmZKapa9eu+P7771G/fn25oxQZfk9DBi1atMC0adOQnJysG7t79y5mzJiBjz/+WMZkpunKlSsoXbq03DGKheDgYCxevBhKpRIajQY2NjYF/qM3I0kSzMzM5I5RpLh7Sgbp6en47LPPcP36dd2k94MHD1CvXj0sXryYL8439MMPP+DUqVMYMGAAqlSpUuibytye4po3b45Jkybx+y1FZMGCBdiyZQu6dOmCKlWqFHpzw4lwEpKbmwuFQoHjx4/j+vXrUKlUqFWrFpo3b27yp02WQ4MGDZCTkwMABbYfJ8LfXNOmTbF582bdARr0bjgRTkXCx8cHc+bMwYcffih3lGKBE+FFJzQ0FJIkISgoiG9gigAnwqlIPHz4kPvgixAnwovO8+fPsX37duzduxf/+Mc/Cu2P50T4mymOE+EsDRn07dsXI0aMQJ8+fV64D75x48YyJTNNnAgvOvn5+ejQoYPcMYoNToRTkSiO+znlxIlwMlacCKcikZSU9Mr77ezsDJSkeOBE+LsRPe2FQqHgp5A3VBzfILI0ZPT777/j2rVrUKvVqFmzZqEzi5IYToS/m+J+2gsqWiwNGWRlZWHUqFE4ePCgbkyhUKBly5YICwvjFdGKUHZ2NrfnW9Jqtbh69SoqVaqEypUryx2nWMnJycHFixfh4uIid5Q3xolwGcyZMwdXr17Fjz/+CFdXV0iShDNnziAkJATz58/nxW3e0MOHD7FkyRJcu3YNeXl5AP7cNaXVanHjxg2cOXNG5oTGLzIyEmvXrsXChQvxwQcf4Pfff8eAAQNw9+5dKBQKdO7cGSEhIVAqlXJHNSkXL17EpEmTcO3aNeTn5xe63xQ/ufE0IjLYtWsXpkyZAjc3N5QtWxbvvfceWrZsicmTJ2Pnzp1yxzM5wcHBiI6Oho2NDc6cOYMPPvgAWq0Wv/76K7788ku54xm9mJgYjB8/HnXq1NFdsCowMBAZGRlYvnw5Nm3ahLi4OF5v/S1MmzYN5ubmCAkJgVqtxuTJk/HFF1/AzMwM8+bNkzve25HI4Bo1aiTdvHmz0PjNmzel+vXry5DItLm6ukpHjhyRJEmSOnToIF26dEmSJEmaMmWKNHr0aDmjmYQePXpIS5Ys0d2+fPmyVLduXSksLEw3tmfPHsnHx0eOeCatYcOG0sWLFyVJkqRu3bpJv/zyiyRJkrR27VopICBAzmhvjZ80ZODo6IitW7cWGg8PD0fdunVlSGTanj9/Dnt7ewBAjRo1cOnSJQBAz549cfr0aTmjmYSrV6+ibdu2utvHjx+HQqGAu7u7bqxu3br4448/5Ihn0iRJ0p1frlq1arh27RoAwN3dHVeuXJEz2lvjnIYMRowYgX79+uHcuXO6L/KdO3cOFy9exNKlS2VOZ3rs7Oxw8+ZN2NraokaNGrr9xEqlEk+ePJE5nfGT/vYFtNOnT8PCwgJOTk66saysLB5Q8BZq166Nw4cPo2fPnrC3t8e5c+fQu3dvpKenv3COwxSwNGTg6uqK9evXY+XKlTh06BDMzc1hb2+PkJAQngrjLXTq1AmBgYGYMWMG3N3d8dlnn6FKlSo4evQoP7kJsLe3x9mzZ1G1alVkZGTg5MmTaNWqVYHvvOzZs4c/m29hwIABGDVqFJRKJXx8fLBw4UIMGTIEV65cQdOmTeWO91Z4yK0MTpw4gWbNmskdo9iQJAmrVq1CzZo10apVK/zwww9Yu3YtrKysMHPmTDg6Osod0ahFRkYiNDQU//rXv3D+/HnExcVh48aNcHZ2Rnp6Onbs2IG5c+ciJCQEfn5+csc1ORcvXoRarYaDgwNOnjyJjRs3wtLSEsOHD0fFihXljvfGWBoycHR0hEajQceOHeHn54caNWrIHcnkvOqby39niqdqMLTNmzdj8+bNUCqV+Pzzz3XX05gyZQq2bNmCL774AiNHjpQ3JBkFloYM0tLSEB0djejoaMTHx6NBgwbw8/ODj48PKlSoIHc8k+Dg4KDbffKqH2F+i/nd3Lt3D+bm5ib5jtgYPHr0CCtWrMD169d1p7r5K1M8azBLQ2a3bt3Czp07sWvXLvzxxx9wd3fHggUL5I5l9D7//HOcOnUKDRs2hLe3N7y8vHRHqRAZi4EDByIuLg7Nmzd/4ZmYp0+fLkOqd8PSkJkkSTh58iR2796NHTt2wMLCAocOHZI7lkl4/Pgx9uzZg9jYWJw5cwaNGzeGt7c3PDw8+ImNjEKjRo2wdOnSYnX+M5aGTOLi4hAdHY1du3YhMzMTbdq0gZ+fHz7++GNeMe0tPHjwAHv37sWuXbtw7tw5NG3aFN7e3mjbti0sLCzkjkcllKenJ+bNm4d69erJHaXIsDRk0KZNGyQnJ6Nx48bw8/ND+/btUa5cObljFRsPHjxAVFQUFi5cCK1WiwsXLsgdiUqovXv3YsWKFfj6669RtWrVQm8ITfFaL/yehgz8/Pzg5+eHqlWryh2lWHn69Cn279+P2NhYHD9+HBUqVICnp6fcsagEU6lUuH79OgICAgqMSyZ8rRd+0jCQlJQU3buKlJSUVy5riu8+5PLo0SPs3bsXe/bswYkTJ2BpaQkPDw94eXnBxcWFu/pIVm3btkXdunXRrVs33ckg/8oU5zpYGgby4Ycf4ujRo7oL3SgUCt2hon89dNRU330Y2qZNm7B7926cPn0alSpVgoeHBzw9PU3y+gRUfDVs2BDR0dHFaq8CS8NATp06hcaNG0OlUvFKc0XAwcEBarUazZo1Q6NGjV75iYKnRye5fPnll/D29kbHjh3ljlJkWBoGlJubi59//hk7d+7EtWvXkJGRgfLly6NevXrw9fVFx44duTtFUOvWrYWWUygU2L9/v57TEL3Y+vXrMXfuXLRu3RrVqlWDSlVwGtkU39CwNAwkMzMTAwYMwPnz5+Hi4oI6deqgfPnyyMjIwKVLl3D+/Hk0adIEP/74I88mSlRMvOrNjam+oeHRUwaycOFCJCUl4eeff37hMdtXrlzBwIEDsWbNGgwcOFCGhERU1A4cOCB3hCLHTxoG0rZtW3z99ddo3779S5eJjIzEypUrsX37dgMmIyJ9e/jwIbRabaHzpJnikZL8pGEgKSkpBS5q8yIuLi4IDQ01UCIi0rezZ89iwoQJha56aMpHSrI0DESr1aJs2bKvXKZMmTLIzMw0UCIi0rdZs2bh/fffR2BgIMqXLy93nCLB0iAi0pNr165h48aNcHBwkDtKkWFpGNDatWtf+K3Q/3n27JkB0xCRvlWuXLnYva45EW4got8rAIrnERdEJdG+ffuwePFijB49GtWqVYOZmVmB+01xIpylQUSkJw0aNNAdNfXXL+5yIpyIiApZvny53BGKHEuDiKgILVmy5IXjKpUKFSpUQP369U16Ypy7p4iIitDL5i8lScLjx4/x/PlzuLu7IywsDGq12sDp3h1Lg4jIgK5cuYLRo0fD09MTI0aMkDvOGysldwAiopLEwcEBo0ePRkxMjNxR3gpLg4jIwOrWrYt79+7JHeOtsDSIiAwsMzPztacVMlYsDSIiA9u4cSMaNmwod4y3wkNuiYiK0MsOuc3Pz0dGRgbOnTuHy5cvY/369QZOVjR49BQRURF62SG3arUa5cuXh6OjI/r06YNatWoZOFnRYGkQEZEwzmkQEZEwlgYREQljaVCJcufOHdStWxe9e/cudN+4ceNQt25dPHjwQPj5wsLCEBkZCQBYuHAh9u3b99rH/PXfOHToELp3746OHTvCx8cHI0aM0B2///TpUwQEBLz2+USXIyoKPHqKShxzc3PcunULSUlJsLOzA/DnBbDOnTv3xs/119NA/PLLL7C3txd+bEpKCsaOHYuIiAhdjh9++AEjR47Epk2b8PjxY1y8ePG1zyO6HFFRYGlQiaNUKtG+fXvs2LEDX375JQBgz549aNOmDVauXAlJkhAaGoq4uDhkZmbqbru4uGDcuHF49OgRbt++jVatWiE9PR21a9dG6dKlER8fj1mzZkGpVMLe3h4hISHIzMxEamoqHBwcMH/+fJibm+tyPHz4EFqttsCV3fr27as7A+r48eORlZWFTp06ISIiAtu2bcPmzZuh1Wrx+PFjDBgwAP7+/oWWq1evHk6cOAFLS0sAf36yOXHiBMzNzTF+/HgkJiaiVKlScHR0REhICEqV4g4HEsefFiqR/Pz8EBUVpbsdGRmJzp07AwBu3bqF+/fvY/PmzYiJiUHnzp2xbNky3bJZWVnYuXMnAgMDdWO9evWCk5MTxowZg3bt2iE8PBx+fn4IDw/Hnj17cOfOHRw6dKhABgcHB3Tr1g2dO3eGt7c3goKCcPDgQXzyyScAgOnTp6N06dKIiopCVlYWtmzZgh9//BGRkZGYN28eZs+eXWg5pVL50nXeu3cvMjMzERUVha1btwIAbt++/W4bkkocftKgEsnJyQlKpRLx8fGwsrJCZmYm6tSpAwCoWbOmbhfR7du38csvv+C9997TPdbFxeW1zx8YGIhjx45h2bJlSEhIwP379194rehx48Zh0KBBOHXqFE6fPo1Zs2Zh3bp1hb749d5772HJkiU4fPgwEhIScOXKlTe+9rSLiwvmzZuHPn36oHnz5ujbty+qVav2Rs9BxE8aVGJ17NgR27dvR1RUFDp16qQbP3z4MAYNGgQAaNOmDXr27FngcSLnDBo9ejTCw8NhZ2eHfv36wdHREX//StT+/fvx888/o2LFivD09ERQUBBiYmJw48YNXLp0qcCy9+7dg5+fH5KSkuDi4oKRI0cKrWNOTo7u71WrVsXevXsxcOBAZGRk4LPPPuP16OmNsTSoxOrUqRNiY2MRExODDh066MYvXrwId3d3+Pv7w8nJCfv27UNeXt5rn0+pVCI3NxcAcPToUQwdOhTe3t4AgLi4uELP8d5772Hu3Lm4ceOGbuz27dtQKpX4xz/+AZVKhby8PEiShPj4eFhaWmLIkCFo0aIFDh48CADIy8srsBwAWFpa6ibGo6Ojdc+9YcMGjB8/Hi1atEBgYCBatGhRqJyIXoe7p6jEsrGxQa1atWBhYYH3339fN+7t7Y3Q0FD4+voiNzcXH3/8Mfbs2YP8/PxXPl/r1q0xd+5caLVajBo1CkOHDkXZsmVRrlw5/POf/8Qff/xRYPmPPvoI3377LcaOHYunT59CqVTC2toay5YtQ4UKFVCuXDk0aNAAPj4+WLVqFWxsbODl5QWFQoEmTZrA0tISiYmJqFatmm659evXIygoCCEhIShfvjyaN28Oa2trAH/O45w6dQre3t4oU6YMbG1t0adPnyLfrlS88TQiREQkjLuniIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiE/R/Mnnv3NjfhdwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'MaritalStatus', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "54e5f4e1",
"metadata": {},
"source": [
"Around 48% of customers are Married"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "4a74c0a1",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'NumberOfTrips', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "67fbb0e1",
"metadata": {},
"source": [
"55% of the customers travelled 2 or 3 trips."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "ebac1de8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'PitchSatisfactionScore', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "325a88a7",
"metadata": {},
"source": [
"Only around 20% are satisfied with package pitch."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "1b869d23",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'NumberOfChildrenVisiting', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "f66e915e",
"metadata": {},
"source": [
"Minority of customer are travelling without children"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "102a997e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labeled_barplot(data, 'Designation', perc=True)"
]
},
{
"cell_type": "markdown",
"id": "3aeb7ed4",
"metadata": {},
"source": [
"Executives and Managers are the majority of customer designation"
]
},
{
"cell_type": "markdown",
"id": "f2272679",
"metadata": {},
"source": [
"#### Bivariate Analysis"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "ac75e192",
"metadata": {},
"outputs": [],
"source": [
"# function to plot stacked bar chart\n",
"\n",
"\n",
"def stacked_barplot(data, predictor, target):\n",
" \"\"\"\n",
" Print the category counts and plot a stacked bar chart\n",
"\n",
" data: dataframe\n",
" predictor: independent variable\n",
" target: target variable\n",
" \"\"\"\n",
" count = data[predictor].nunique()\n",
" sorter = data[target].value_counts().index[-1]\n",
" tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(\n",
" by=sorter, ascending=False\n",
" )\n",
" print(tab1)\n",
" print(\"-\" * 120)\n",
" tab = pd.crosstab(data[predictor], data[target], normalize=\"index\").sort_values(\n",
" by=sorter, ascending=False\n",
" )\n",
" tab.plot(kind=\"bar\", stacked=True, figsize=(count + 5, 6))\n",
" plt.legend(\n",
" loc=\"lower left\", frameon=False,\n",
" )\n",
" plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "b08fc8b2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"TypeofContact \n",
"All 3968 920 4888\n",
"Self Enquiry 2859 610 3469\n",
"Company Invited 1109 310 1419\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": 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AI0QYAAAjRBgAACNEGAAAI0QYAAAjVxThgoICpaSkaNSoUVq3bt0l63fs2KG0tDSNHTtW999/v8rLy6/5oAAAtDZBI1xSUqKcnBytX79eeXl5ys3N1ZEjRwLrq6qq9PTTT2vFihXavHmz+vTpoxdffLFJhwYAoDUIGuHCwkIlJiYqJiZG7dq1U3JysrZu3RpYX1tbq/nz5ysuLk6S1KdPH33yySdNNzEAAK1ERLANSktLFRsbG1j2er06cOBAYLlz58664447JEk1NTVasWKFpk2b1qghunbt0KjtgS8TG9vRegSgWWBfaBmCRtjv98vj8QSWHcdpsPxXlZWVeuCBB9S3b1+NGzeuUUOcOVMlv99p1HOaE37Zmw+fr9J6BFdjX2g+WvK+EBbmcc3BWdDT0d27d5fP5wss+3w+eb3eBtuUlpZqypQp6tOnj7Kzs6/9lAAAtEJBI5yUlKQ9e/aorKxM1dXV2r59u4YOHRpYX19fr1mzZmn06NHKzMy87FEyAAC4VNDT0XFxcZo7d66mT5+u2tpaTZw4UQkJCcrIyNDs2bN16tQpHT58WPX19dq2bZskqX///hwRAwAQRNAIS1JqaqpSU1MbPLZy5UpJUnx8vIqLi6/9ZAAAtHLcMQsAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI0QYAAAjRBgAACNEGAAAI1cU4YKCAqWkpGjUqFFat27dJeuLioo0fvx4JScnKzMzU3V1ddd8UAAAWpugES4pKVFOTo7Wr1+vvLw85ebm6siRIw22efTRR/XUU09p27ZtchxHGzZsaLKBAQBoLYJGuLCwUImJiYqJiVG7du2UnJysrVu3BtafPHlSNTU1uvnmmyVJ48ePb7AeAABcXkSwDUpLSxUbGxtY9nq9OnDgwJeuj42NVUlJSaOGCAvzNGr75sjbOdp6BKh1/C61dOwLzUNL3hda8uyNFTTCfr9fHs///xfiOE6D5WDrr0Tnzu0btX1z9FLWKOsRIKlr1w7WI7ge+0LzwL7QMgQ9Hd29e3f5fL7Ass/nk9fr/dL1p0+fbrAeAABcXtAIJyUlac+ePSorK1N1dbW2b9+uoUOHBtb36NFDkZGRevfddyVJ+fn5DdYDAIDL8ziO4wTbqKCgQMuXL1dtba0mTpyojIwMZWRkaPbs2YqPj1dxcbGysrJUVVWlfv36aeHChWrbtm0o5gcAoMW6oggDAIBrjztmAQBghAgDAGCECAMAYIQIAwBghAgDAGCECANwtc/fhhcINd6iBMDVpk2bpnPnziktLU1paWkN7oUPNDUi7DJ9+/ZtcG/viIgIhYeH68KFC+rQoYPeeecdw+kAGydPnlR+fr7eeOMNXXfddRo3bpxuv/12tWnTxno0tHJE2KXmz5+vf/zHf9TYsWPl8Xi0bds2/f73v9eCBQusRwNMfPzxx3r99df1X//1X/r617+u06dP65FHHtEdd9xhPRpaMSLsUuPGjdOmTZsaPJaenq68vDybgQAjv/71r5Wfny+fz6f09HSNGzdO3bt3V0lJicaNG6fCwkLrEdGKBf0oQ7RO0dHR2rhxo0aPHi2/36/8/Hx16tTJeiwg5N5++2099NBDuvXWWxs8HhcXp/nz5xtNBbfgSNilTp48qWeeeUZ79+5VWFiYkpKSlJWVpbi4OOvRgJC63FkhIFQ4EnapHj16aNmyZTp37pxiYmKsxwHMdOvWTX/4wx+UkJDAp78h5DgSdqmioiLNnTtXNTU1ys3N1dSpU7VkyRL169fPejQgpBITE3Xu3LkGj3k8HhUVFdkMBFchwi51zz336F//9V/14x//WHl5edq9e7dycnL06quvWo8GAK7B6WiXqq6u1g033BBYHjRokBYtWmQ4ERBaubm5mjRpkn72s59ddv2DDz4Y4ongRty20qViYmJUXFwcuHHH5s2buToarsJJQDQHnI52qePHj2vevHk6ePCgoqKi1LNnTy1evFi9evWyHg0AXIPT0S514cIFvfLKKzp//rz8fr86dOig999/33osIOS+eCtXSfJ6vXrrrbeMJoKbEGGXeffdd+X3+5WVlaXs7OzAKbm6ujo9/fTT2rZtm/GEQGgVFxcHvq6trdWOHTv4CylChgi7TGFhofbt26fS0lK98MILgccjIiI0adIkw8kAe23atNHo0aO1bNky61HgEkTYZR566CFJUl5entLT022HAZqBz98v3XEc/d///Z8iIvi/RoQGv2ku8+KLL+qhhx7S3r17tXfv3kvWL1y40GAqwM4X94POnTtryZIlNsPAdYiwy/z1jljf+c53jCcBmgf+4glLRNhlRowYIUk6fPiw0tLS1L9/f+OJAFsjRoy45Opo6bNT0x6PRzt37jSYCm5BhF2qZ8+eys7OVnl5uVJTU5WamqpvfOMb1mMBIZeamqo2bdro7rvvVkREhAoKCnTw4EHNnTvXejS4ADfrcLlPPvlEW7Zs0ebNm9W+fXutX7/eeiQgpMaPH6/XXnst6GNAU+C2lS5WWVmp3bt3a/fu3aqvr9egQYOsRwJMFBYWBr5+88031b59e8Np4CYcCbvUrFmzdPjwYd1xxx0aO3asbrrpJuuRABOHDh3SvHnzdPr0aUlS7969tWjRIn3zm980ngxuQIRdateuXRo6dCjvhwT+oqysTFFRUWrXrp31KHARIuxSJ0+e1Msvv6zy8vIGnybD2zXgNocPH9ayZcsu2RfWrFljOBXcgsMgl5ozZ45uueUW3XLLLZd9ewbgFvPmzdOkSZN04403si8g5IiwS9XV1WnevHnWYwDmoqKiNHXqVOsx4FJcHe1SAwYM0K5du3Tx4kXrUQBTgwcP1tq1a/XnP/9ZH3/8ceAPEAq8JuxSgwcPDlwN6vF4AncHKioqMp4MCK2/3kXu87hTFkKFCAMAYITXhF3mnXfe+ZvrBw4cGKJJAFtLlizRnDlzJEm7d+9ucLOahx9+uMHnbQNNhQi7zH/8x3986TqPx8PbMuAab731ViDCixcvbhDhY8eOGU0FtyHCLrN27VrrEYBm4fOvxH3xVTneqoRQ4epoAK5HdGGFCANwJcKL5oCro13q4sWLatu2rfUYgJm+ffsGQvzXt+h9/mverodQIMIuddttt2n48OEaN26cEhISrMcBAFciwi5VXV2tbdu2KS8vT2fOnFF6errGjh2r2NhY69EAwDWIMPTb3/5WCxYsUEVFhf7pn/5J8+bNU8+ePa3HAoBWjwuzXOrYsWN68cUXlZycrPXr1+uRRx7R22+/rUmTJikjI8N6PKDJbdmyRdJnnyMMWCHCLnXvvffK4/HoP//zP7Vq1SqlpqYqMjJSw4YN02233WY9HtDkcnJyVFdXpxkzZliPAhfjdLRLff5qUMCNHn/8ceXl5QX2hS/+L1dHIxSIsEtt2rRJzz33nCoqKiTxtgy4149+9CP94he/sB4DLkWEXWrkyJFaunSpvv3tb1uPApjgw0zQHHDvaJfyer0EGK7Gh5mgOeBI2KWys7NVUlKiQYMGKTIyMvB4enq63VAA4DJcHe1SVVVVat++vd5//33t3bs38Adwm5MnT+ree+/VqFGj5PP5NH36dH300UfWY8ElOBJGQE1NjaKioqzHAEJqxowZuvfee7V48WJt2rRJv/71r5Wfn69169ZZjwYX4EjYpXbt2qWxY8dq5MiRuv322zV8+HANHz7ceiwg5M6ePavBgwdL+uy14LvvvltVVVXGU8EtuDDLpRYuXKhnnnlGq1at0qxZs7Rjxw5VV1dbjwWEXFRUlE6dOhV43/wf/vAHPmEMIUOEXapjx45KTEzUe++9p8rKSj366KNKSUmxHgsIuccff1w//OEPdfz4caWlpam8vFwvvPCC9VhwCSLsUlFRUfrzn/+sG264Qfv27VNiYqJqa2utxwJCLj4+Xq+++qo+/PBD1dfXq3fv3hwJI2R4Tdil5syZoyVLlmj48OHas2ePBg0apJEjR1qPBYTUm2++qRMnTqhNmzY6duyYlixZomXLlvEXUoQMV0e73Llz5xQeHi6/369OnTpZjwOEzEsvvaQtW7Zo0aJFqqur0+TJk5WZmamioiKFh4crMzPTekS4AKejXaq4uFg/+clPVFJSIsdx1Lt3bz3//PP65je/aT0aEBL5+fnKzc1VdHS0Fi9erBEjRui73/2uHMfh+giEDKejXeqJJ57Q3LlztXfvXu3bt08zZszQY489Zj0WEDIej0fR0dGSpL1792rIkCGBx4FQIcIu5ThOg/cF33HHHTp//rzhREBohYeHq6KiQqdOnVJRUZEGDRok6bM7aEVEcJIQocFvmkslJSVp6dKluvvuuxUeHq4tW7bohhtu0McffyxJuu6664wnBJrWzJkzlZ6errq6Ok2cOFFer1dbtmxRTk6OHnjgAevx4BJcmOVSI0aM+NJ1Ho9HO3fuDOE0gI2SkhKdPXtWffv2lSS99dZbioqK0q233mo8GdyCCAMAYITT0S519OhRbdiwQeXl5Q0eX7hwodFEAOA+RNilHnzwQaWkpKhPnz7WowCAaxFhl/ra176mBx980HoMAHA1XhN2qdzcXH388cdKTExs8HaMgQMHGk4FAO7CkbBL7d+/X++9957ee++9wGMej0dr1qwxnAoA3IUIu9QHH3yg7du3W48BAK7GHbNc6sYbb1RxcbH1GADgahwJu9TRo0c1btw4xcbGqk2bNnIch5t0AECIcWGWS508efKyj/fo0SPEkwCAexFhl3IcR6+88orefvtt1dXVKTExUVOnTlVYGK9QAECocDrapZ5//nkdO3ZMEyZMkOM4eu2113TixAk+yBwAQogIu9Tu3buVl5cXOPK97bbblJqaajwVALgL5x5dqr6+XnV1dQ2Ww8PDDScCAPfhSNilUlNTNX36dN11112SpN/85jcaM2aM8VQA4C5cmOViv/vd77Rnzx45jqPExETddttt1iMBgKsQYRcqLy9XfX29unTpIknau3evbrzxxsAyACA0eE3YZQ4fPqy77rpLhw4dCjxWWFiotLQ07qAFACHGkbDLfP/739f999+vW2+9tcHjv//97/XSSy9p9erVNoMBgAtxJOwyFRUVlwRYkoYMGaKzZ88aTAQA7kWEXaaurk5+v/+Sx/1+v2praw0mAgD3IsIuM3DgQP3sZz+75PGlS5eqf//+BhMBgHvxmrDLVFVVaebMmTp16pT69u2ryMhIHT58WF26dNEvfvELxcTEWI8IAK5BhF3IcRy9/fbbKioqUlhYmPr3769bbrnFeiwAcB0iDACAEV4TBgDACBEGAMAIH+AA11mwYIHeeecdSdKf/vQn9ejRQ1FRUZKk3NzcwNdN5ZNPPlFGRobCw8P19NNPKyEhQWvWrFFBQYHq6+tVW1ur4cOH6+GHH1bbtm2v6mdUVlbqgQce0Jo1a0yeD+DKEGG4TlZWVuDrESNGaPHixYqPjw/Zz9+7d6+6desWuDvZk08+qfLycv3qV79Sx44ddf78eT3yyCPKzMzUv/3bv13VzygvL9fBgwevesav+nwAV4YIA5LOnz+vYcOGacOGDerVq5ck6Qc/+IGmTp2qHTt2KDIyUsXFxTpz5owGDRqkrKwstWnTRn/605+UnZ2tc+fOqb6+XtOmTdPEiRMlfXZUvXbtWoWFhalbt2568sknVVJSoiVLlqiyslLTpk3TwoULVVBQoP/5n/9Rhw4dJEnt2rXTT3/6U7333nuSPjsq/elPf6ri4mJ5PB4NGTJE//Iv/6KIiAjFx8dr5syZ2r17t0pLS/XP//zPmjJlih5//HHV1NQoLS1Nr732mjZt2qTc3FzV1taqvLxcGRkZmjJliiRp+fLl2rRpkyIiItSzZ08999xzlzyfz5oGmogDuNjw4cOdAwcOOI7jOAsWLHAWLVrkOI7jHDt2zBk2bJhTV1fnzJs3z0lPT3eqqqqcCxcuOPfcc4+zdu1ap7a21klJSXEOHTrkOI7jVFRUOKNHj3b279/vFBYWOiNHjnTOnDnjOI7jbNy40Rk9erTj9/udjRs3OjNnznQcx3G2bt3qTJgw4W/O+JOf/MR55plnHL/f71y4cMG57777nOXLlzuO4zjf/va3nbVr1zqO4zgHDx50+vfv79TU1DgnTpxwbr75ZsdxHKeqqsq5++67nbKyMsdxHGf//v2BdTt27HBGjRrlnDt3znEcx3n22WedpUuXNng+gKbDkTDwF1OmTNHUqVM1d+5c5ebmauLEiYEjwHHjxql9+/aSpLS0NO3cuVOJiYk6fvy4nnjiicD3qKmp0eHDh/XRRx8pJSUl8PGQ48ePV3Z2tj766KMGPzMsLOyytxH9vN/97nd65ZVX5PF41LZtW02ePFm/+tWvNHPmTEnS7bffLknq16+fLl68qPPnzzd4fvv27bVs2TK99dZb+vDDD1VcXBzYZs+ePbrzzjvVqVMnSdLjjz8uSZfMCaBpcHU08Be9evVSnz59tHPnTr3++uv67ne/G1j3+dOxjuMoLCxM9fX16tixo/Lz8wN/NmzYoAkTJlw2rI7jqK6ursFjCQkJOnr0qKqqqho8XlJSopkzZ6qmpkZ+v18ejyewzu/3N/g+kZGRkhTYxvnCW/9PnTql9PR0nTx5UgMGDNCcOXMa/HN9/ntXVFQQYCCEiDDwOVOmTNHzzz+vhIQExcXFBR5/4403dPHiRV24cEGbNm3S8OHD1atXL0VFRSk/P1/SZ1c9jxkzRocOHdKQIUO0ZcsWlZWVSZI2btyomJgY9ezZs8HPi4uLU2pqqp544olAiKuqqvT0008rJiZGUVFRGjx4sF5++WU5jqOLFy9qw4YNSkpK+pv/HBEREaqvr5fjODp06JC6dOmi+++/X4MHD9abb74pSaqvr1dSUpJ++9vfBn72iy++qNWrVzd4PoCmw+lo4HOGDx+urKwsTZ48ucHjUVFRmjJliioqKpScnKwJEyYoLCxMS5cuVXZ2tn75y1+qrq5ODz/8sAYMGCDpswu7vv/978vv96tLly5avny5wsIu/Xvv/PnztXTpUk2ePFnh4eG6ePGiRo4cqYceekjSZ1dzL1iwQKmpqaqtrdWQIUM0a9asv/nPERsbq4SEBN11111atWqV4uLidOedd8rj8eg73/mOunTpomPHjmnYsGE6cuSIvve970mSvvWtb+mZZ55RdHR04Pnr1q1T586dr8W/XgBfwG0rgc/Zv3+/srKy9PrrrwdO0z722GO68cYbNWPGDOPpALQ2HAkDfzFv3jzt27dPOTk5DV4nBYCmwpEwAABGuDALAAAjRBgAACNEGAAAI0QYAAAjRBgAACP/D8jSghTrcV37AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"TypeofContact\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "6d77fabc",
"metadata": {},
"source": [
"Asset customers are around the same percentage of both company invited and self inquired. Albeit the self inquired asset customers are double company invited ones, This means that with increasing company targeted customers there is a chance that it is possible to increase the asset customer base."
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "3ce09d70",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"Occupation \n",
"All 3968 920 4888\n",
"Salaried 1954 414 2368\n",
"Small Business 1700 384 2084\n",
"Large Business 314 120 434\n",
"Free Lancer 0 2 2\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"Occupation\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "179fce3d",
"metadata": {},
"source": [
"Freelancers are only 2 customers so they can be considred as outliers, Salaried customers are having the highest number of asset customer while customer in large business are having the largest percentage of asset customers. Customer in large businesses present a good hit rate chance."
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "5c07dcd1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"Gender \n",
"All 3968 920 4888\n",
"Male 2338 578 2916\n",
"Female 1630 342 1972\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"Gender\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "5edf1f29",
"metadata": {},
"source": [
"Male and female asset customer percentage are the same, albiet Male asset customers are majority in count."
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "15c1d6fc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"NumberOfPersonVisiting \n",
"All 3968 920 4888\n",
"3 1942 460 2402\n",
"2 1151 267 1418\n",
"4 833 193 1026\n",
"1 39 0 39\n",
"5 3 0 3\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"NumberOfPersonVisiting\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "569251bc",
"metadata": {},
"source": [
"20% of asset customers are travelling in a group of 2,3 and 4 persons"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "2bcb5c27",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"NumberOfFollowups \n",
"All 3968 920 4888\n",
"4.0 1689 379 2068\n",
"3.0 1222 244 1466\n",
"5.0 577 191 768\n",
"6.0 82 54 136\n",
"2.0 205 24 229\n",
"1.0 156 20 176\n",
"0.0 37 8 45\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"NumberOfFollowups\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "8acbfb87",
"metadata": {},
"source": [
"Data shows that with higher number of followups the higher the chance to be an asset customer, 4 followups is the largest in number for asset customers in numbers with around 30% of total number of asset customers."
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "6b8e6a4a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"PreferredPropertyStar \n",
"All 3968 920 4888\n",
"3.0 2531 488 3019\n",
"5.0 706 250 956\n",
"4.0 731 182 913\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"PreferredPropertyStar\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "b9bd9ece",
"metadata": {},
"source": [
"percentage wise there property star interest is almost the same, albeit 3 star property is the one having more interest count wise."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "6186372f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"MaritalStatus \n",
"All 3968 920 4888\n",
"Married 2014 326 2340\n",
"Single 612 304 916\n",
"Unmarried 516 166 682\n",
"Divorced 826 124 950\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"MaritalStatus\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "e909fdc4",
"metadata": {},
"source": [
"From the trend visually pecentages are variying, however Single and married customers are the same, Divorced and Unmarried customers are half of those married or Single."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "4bfde698",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"NumberOfTrips \n",
"All 3968 920 4888\n",
"2.0 1165 299 1464\n",
"3.0 990 229 1219\n",
"1.0 508 112 620\n",
"6.0 258 64 322\n",
"5.0 396 62 458\n",
"7.0 156 62 218\n",
"4.0 417 61 478\n",
"8.0 76 29 105\n",
"19.0 0 1 1\n",
"20.0 0 1 1\n",
"21.0 1 0 1\n",
"22.0 1 0 1\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1224x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"NumberOfTrips\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "10af62c4",
"metadata": {},
"source": [
"From this relation, we can easily spot that customers who have taken 19-20 trips are asset customers, there is a tendency that with larger number of trip history, customer is likely to take a trip package from the company."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "7fc19e7c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"Passport \n",
"All 3968 920 4888\n",
"1 928 494 1422\n",
"0 3040 426 3466\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"Passport\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "20e140a4",
"metadata": {},
"source": [
"Percentage wise, customer who hold travel passport are taking more travel packages, albeit number wise both who don't hold passport and passport holders have the same number of asset customers."
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "b6c52e6b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"PitchSatisfactionScore \n",
"All 3968 920 4888\n",
"3 1162 316 1478\n",
"5 760 210 970\n",
"4 750 162 912\n",
"1 798 144 942\n",
"2 498 88 586\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"PitchSatisfactionScore\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "da3b0660",
"metadata": {},
"source": [
"From the trend vissualy it is clear that pitch satisfaction is not a factor in taking a package as percentage wise all ratings have almost the same percentage of asset customers."
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "5857debd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"OwnCar \n",
"All 3968 920 4888\n",
"1 2472 560 3032\n",
"0 1496 360 1856\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"OwnCar\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "4491c3fe",
"metadata": {},
"source": [
"Percentage wise customers with or without a car ownership have same percentage of customer taking company products, albeit customer who own a car are larger in number."
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "2eb3ea7a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"NumberOfChildrenVisiting \n",
"All 3968 920 4888\n",
"1.0 1729 397 2126\n",
"2.0 1082 253 1335\n",
"0.0 898 204 1102\n",
"3.0 259 66 325\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 648x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"NumberOfChildrenVisiting\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "a2f76d5c",
"metadata": {},
"source": [
"Percentage wise it shows that customers with or without children ragrdless of their number have the same percentage of asset customers."
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "f0b0ba5c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ProdTaken 0 1 All\n",
"Designation \n",
"All 3968 920 4888\n",
"Executive 1290 552 1842\n",
"Manager 1528 204 1732\n",
"Senior Manager 618 124 742\n",
"AVP 322 20 342\n",
"VP 210 20 230\n",
"------------------------------------------------------------------------------------------------------------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_barplot(data, \"Designation\", \"ProdTaken\")"
]
},
{
"cell_type": "markdown",
"id": "af5a09b9",
"metadata": {},
"source": [
"From the above chart it is visible that the lower the designation rank the higher the asset customer base."
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "29dd3d83",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1440 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Visualizing income profile against designation with Product taken on hue\n",
"plt.figure(figsize=(15,20))\n",
"sns.swarmplot(data=data,x='Designation',y='MonthlyIncome',hue='ProdTaken')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "cba15fd2",
"metadata": {},
"source": [
"From the above chart it is visible that the lower the designation rank the higher the asset customer base."
]
},
{
"cell_type": "markdown",
"id": "31295fce",
"metadata": {},
"source": [
"#### EDA Observations"
]
},
{
"cell_type": "markdown",
"id": "f247e2cc",
"metadata": {},
"source": [
"- Less than 25% have taken the `PodTaken`, exactly 18.8% of customer database have taken the company products.\n",
"- Majority of customers don't have `passports`. Percentage wise, customers who hold travel passport are taking more travel packages, albeit number wise both who don't hold passport and passport holders have the same number of asset customers.\n",
"- 75% of customer base are 45 of `Age` and below, Majority of customer database are between 29 to 38 and age of 49, age of 49 is the top count in the customer database.\n",
"- From `Gender` perspective, around 60% of customers are males, Male and female asset customer percentage are the same, albeit Male asset customers are majority in count.\n",
"- `MaritalStatus` Around 48% of customers are Married, percentages are varying, however Single and married customers are the same, Divorced and Unmarried customers are half of those married or Single.\n",
"- Executive `Designation` is the most frequent in the customer data base, Executives and Managers are the majority of customer designation\n",
"- Above 80% of the customers are between salaried and working in small business `occupations`, Freelancers are only 2 customers so they can be considered as outliers, Salaried customers are having the highest number of asset customer while customer in large business are having the largest percentage of asset customers. Customer in large businesses present a good hit rate chance, it was observed also that the lower the designation rank the higher the asset customer base.\n",
"- Customer database have a mean `MonthlyIncome` of 24,162, Majority of customers have income band between 20,000 and 40,000\n",
"- Percentage wise customers with or without a `car ownership` have same percentage of customer taking company products, albeit customer who own a car are larger in number.\n",
"- Self inquiry is the preferred `TypeofContact`, 71% of customer base self-inquired. Asset customers are around the same percentage of both companies invited and self-inquired. Albeit the self-inquired asset customers are double company invited ones, this means that with increasing company targeted customers there is a chance that it is possible to increase the asset customer base.\n",
"- 75% of customers listened to a `DurationOfPitch` 19 minutes, only around 20% are satisfied with package pitch. \n",
"- 75% of customer had 4 `NumberOfFollowups`, 42% of customers had 4 follow-up calls, 30% had 3, Data shows that with higher number of follow-ups the higher the chance to be an asset customer, 4 follow-ups is the largest in number for asset customers in numbers with around 30% of total number of asset customers.\n",
"- `PitchSatisfaction` is not a factor in taking a package as percentage wise all ratings have almost the same percentage of asset customers.\n",
"- 75% of customers have `NumberOfPersonVisiting` up to 3 persons, Close to 50% of customers mentioned that 3 persons will be on the travel package, 29% 2 persons and 21% 4 persons. 20% of asset customers are travelling in a group of 2,3 and 4 persons\n",
"- Minority of customer are travelling without children, Percentage wise it shows that customers with or without children regardless of their number have the same percentage of asset customers.\n",
"- 1st Tier City tour is having the highest interest among customers.\n",
"- 75% of customers had `NumberOfTrips` from 1 to 4, 55% of the customers travelled 2 or 3 trips. From this relation, we can easily spot that customer who have taken 19-20 trips are asset customers, there is a tendency that with larger number of trip history, customer is likely to take a trip package from the company.\n",
"- Basic, Deluxe packages are having the highest interest among the customer database, 3 Star hotels, are having the highest interest. Percentage wise their property star interest is almost the same, albeit 3 star property is the one having more interest count wise.\n"
]
},
{
"cell_type": "markdown",
"id": "6f117603",
"metadata": {},
"source": [
"## C. Bagging Model Building"
]
},
{
"cell_type": "markdown",
"id": "3260c164",
"metadata": {},
"source": [
"### Model evaluation criterion\n",
"\n",
"### The model can make wrong predictions as:\n",
"1. Predicting a person doesn't take a company product but he/she actually take it.\n",
"2. Predicting a person take a product, but he/she doesn't actually take it.\n",
"\n",
"### Which case is more important? \n",
"* Predicting a person doesn't take a company product but he/she actually take it.\n",
"\n",
"### Which metric to optimize?\n",
"* We would want Recall to be maximized, the greater the Recall higher the chances of minimizing false negatives because if a model predicts that a person is not going to take a travel package product then he/she will be neglected and will not folowup with him/her and this will maximise the company loss."
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "183074e9",
"metadata": {},
"outputs": [],
"source": [
"#Making a list of columns and then iterating on the list items in a for loop changing its data type\n",
"catcols = ['CityTier','NumberOfPersonVisiting','NumberOfFollowups','PreferredPropertyStar','NumberOfTrips','Passport','PitchSatisfactionScore','OwnCar','NumberOfChildrenVisiting']\n",
"for colname in catcols:\n",
" data[colname]=data[colname].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "acd73386",
"metadata": {},
"outputs": [],
"source": [
"#Preparing Dummy columns data\n",
"oneHotCols=['TypeofContact','Occupation','Gender','ProductPitched','MaritalStatus','Designation','CityTier','NumberOfPersonVisiting','NumberOfFollowups','PreferredPropertyStar','NumberOfTrips','Passport','PitchSatisfactionScore','OwnCar','NumberOfChildrenVisiting']\n",
"data=pd.get_dummies(data, columns=oneHotCols)"
]
},
{
"cell_type": "markdown",
"id": "dc5245ce",
"metadata": {},
"source": [
"Splitting the data"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "84522d63",
"metadata": {},
"outputs": [],
"source": [
"X = data.drop('ProdTaken',axis=1)\n",
"y = data['ProdTaken'] "
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "48705627",
"metadata": {},
"outputs": [],
"source": [
"# Splitting data into training and test set:\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1,stratify=y)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "a463e008",
"metadata": {},
"outputs": [],
"source": [
"# defining a function to compute different metrics to check performance of a classification model built using sklearn\n",
"def model_performance_classification_sklearn(model, predictors, target):\n",
" \"\"\"\n",
" Function to compute different metrics to check classification model performance\n",
"\n",
" model: classifier\n",
" predictors: independent variables\n",
" target: dependent variable\n",
" \"\"\"\n",
"\n",
" # predicting using the independent variables\n",
" pred = model.predict(predictors)\n",
"\n",
" acc = accuracy_score(target, pred) # to compute Accuracy\n",
" recall = recall_score(target, pred) # to compute Recall\n",
" precision = precision_score(target, pred) # to compute Precision\n",
" f1 = f1_score(target, pred) # to compute F1-score\n",
"\n",
" # creating a dataframe of metrics\n",
" df_perf = pd.DataFrame(\n",
" {\n",
" \"Accuracy\": acc,\n",
" \"Recall\": recall,\n",
" \"Precision\": precision,\n",
" \"F1\": f1,\n",
" },\n",
" index=[0],\n",
" )\n",
"\n",
" return df_perf"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "2ebd4679",
"metadata": {},
"outputs": [],
"source": [
"def confusion_matrix_sklearn(model, predictors, target):\n",
" \"\"\"\n",
" To plot the confusion_matrix with percentages\n",
"\n",
" model: classifier\n",
" predictors: independent variables\n",
" target: dependent variable\n",
" \"\"\"\n",
" y_pred = model.predict(predictors)\n",
" cm = confusion_matrix(target, y_pred)\n",
" labels = np.asarray(\n",
" [\n",
" [\"{0:0.0f}\".format(item) + \"\\n{0:.2%}\".format(item / cm.flatten().sum())]\n",
" for item in cm.flatten()\n",
" ]\n",
" ).reshape(2, 2)\n",
"\n",
" plt.figure(figsize=(6, 4))\n",
" sns.heatmap(cm, annot=labels, fmt=\"\")\n",
" plt.ylabel(\"True label\")\n",
" plt.xlabel(\"Predicted label\")"
]
},
{
"cell_type": "markdown",
"id": "6e91e54c",
"metadata": {},
"source": [
"### Decision Tree"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "3ff2cb01",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training performance:\n",
" Accuracy Recall Precision F1\n",
"0 1.0 1.0 1.0 1.0\n",
"Testing performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.882072 0.728261 0.672241 0.69913\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Fitting the model\n",
"d_tree = DecisionTreeClassifier(random_state=1)\n",
"d_tree.fit(X_train,y_train)\n",
"\n",
"#Calculating different metrics\n",
"dtree_model_train_perf=model_performance_classification_sklearn(d_tree,X_train,y_train)\n",
"print(\"Training performance:\\n\",dtree_model_train_perf)\n",
"dtree_model_test_perf=model_performance_classification_sklearn(d_tree,X_test,y_test)\n",
"print(\"Testing performance:\\n\",dtree_model_test_perf)\n",
"#Creating confusion matrix\n",
"confusion_matrix_sklearn(d_tree, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"id": "61bec087",
"metadata": {},
"source": [
"- The decision tree is overfitting the training data as there is a huge difference between training and test scores for all the metrics.\n",
"- The test recall is very low at only 72%."
]
},
{
"cell_type": "markdown",
"id": "b3f5ebf0",
"metadata": {},
"source": [
"### Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "e919c516",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training performance:\n",
" Accuracy Recall Precision F1\n",
"0 1.0 1.0 1.0 1.0\n",
"Testing performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.90593 0.547101 0.920732 0.686364\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Fitting the model\n",
"rf_estimator = RandomForestClassifier(random_state=1)\n",
"rf_estimator.fit(X_train,y_train)\n",
"\n",
"#Calculating different metrics\n",
"rf_estimator_model_train_perf=model_performance_classification_sklearn(rf_estimator,X_train,y_train)\n",
"print(\"Training performance:\\n\",rf_estimator_model_train_perf)\n",
"rf_estimator_model_test_perf=model_performance_classification_sklearn(rf_estimator,X_test,y_test)\n",
"print(\"Testing performance:\\n\",rf_estimator_model_test_perf)\n",
"\n",
"#Creating confusion matrix\n",
"confusion_matrix_sklearn(rf_estimator, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"id": "293e96ff",
"metadata": {},
"source": [
"- The decision tree is overfitting the training data as there is a huge difference between training and test scores for all the metrics.\n",
"- The test recall tumbled in Random Forest to 54%\n"
]
},
{
"cell_type": "markdown",
"id": "8ed0fd04",
"metadata": {},
"source": [
"### Bagging Clasifier"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "fa3b8530",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.993277 0.967391 0.9968 0.981875\n",
"Testing performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.904567 0.59058 0.857895 0.699571\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Fitting the model\n",
"bagging_classifier = BaggingClassifier(random_state=1)\n",
"bagging_classifier.fit(X_train,y_train)\n",
"\n",
"#Calculating different metrics\n",
"bagging_classifier_model_train_perf=model_performance_classification_sklearn(bagging_classifier,X_train,y_train)\n",
"print(\"Training performance:\\n\",bagging_classifier_model_train_perf)\n",
"bagging_classifier_model_test_perf=model_performance_classification_sklearn(bagging_classifier,X_test,y_test)\n",
"print(\"Testing performance:\\n\",bagging_classifier_model_test_perf)\n",
"#Creating confusion matrix\n",
"confusion_matrix_sklearn(bagging_classifier, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"id": "530d9d46",
"metadata": {},
"source": [
"- Bagging classifier giving a slightly better performance than random forest but still worse than a decision tree.\n",
"- It is also overfitting the training data and lower test recall than decision tree.\n"
]
},
{
"cell_type": "markdown",
"id": "993beabf",
"metadata": {},
"source": [
"### Tuninig Desicion Tree"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "ba425491",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight={0: 0.188, 1: 0.812}, max_depth=1,\n",
" max_leaf_nodes=2, min_impurity_decrease=0.1,\n",
" random_state=1)"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Choose the type of classifier. \n",
"dtree_estimator = DecisionTreeClassifier(class_weight={0:0.188,1:0.812},random_state=1)\n",
"\n",
"# Grid of parameters to choose from\n",
"parameters = {'criterion':['gini','entropy'],\n",
" 'max_depth': [1,5,9,13,15,17], \n",
" 'min_samples_leaf': [1, 2, 5, 12, 14],\n",
" 'max_leaf_nodes' : [1, 2, 3, 5, 7, 9],\n",
" 'min_impurity_decrease': [0.0001,0.001,0.01,0.1]\n",
" }\n",
"\n",
"# Type of scoring used to compare parameter combinations\n",
"scorer = metrics.make_scorer(metrics.recall_score)\n",
"\n",
"# Run the grid search\n",
"grid_obj = GridSearchCV(dtree_estimator, parameters, scoring=scorer,n_jobs=-1)\n",
"grid_obj = grid_obj.fit(X_train, y_train)\n",
"\n",
"# Set the clf to the best combination of parameters\n",
"dtree_estimator = grid_obj.best_estimator_\n",
"\n",
"# Fit the best algorithm to the data. \n",
"dtree_estimator.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "ae997c99",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.188249 1.0 0.188249 0.316851\n",
"Testing performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.188139 1.0 0.188139 0.316695\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Calculating different metrics\n",
"dtree_estimator_model_train_perf=model_performance_classification_sklearn(dtree_estimator,X_train,y_train)\n",
"print(\"Training performance:\\n\",dtree_estimator_model_train_perf)\n",
"dtree_estimator_model_test_perf=model_performance_classification_sklearn(dtree_estimator,X_test,y_test)\n",
"print(\"Testing performance:\\n\",dtree_estimator_model_test_perf)\n",
"#Creating confusion matrix\n",
"confusion_matrix_sklearn(dtree_estimator, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"id": "55deb698",
"metadata": {},
"source": [
"- The test recall has increased significantly after hyperparameter tuning and the decision tree is over fitting both on train and test\n",
"- The confusion matrix shows that the model can identify false positives while failing on other levels"
]
},
{
"cell_type": "markdown",
"id": "7aba03a7",
"metadata": {},
"source": [
"### Tuning Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "d8525e96",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(class_weight={0: 0.188, 1: 0.812}, max_depth=1,\n",
" max_features=0.6, max_samples=0.7,\n",
" min_impurity_decrease=0.1, n_estimators=50,\n",
" random_state=1)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Choose the type of classifier. \n",
"rf_tuned = RandomForestClassifier(class_weight={0:0.188,1:0.812},random_state=1)\n",
"\n",
"parameters = { \n",
" 'max_depth': [1,5,9,13,15,17],\n",
" 'max_features': [0.6,0.8,0.9,1.0],\n",
" 'max_samples': [0.7,0.8,0.9,1.0],\n",
" 'min_samples_split': [2,4,5,7,10,12],\n",
" 'n_estimators': [10,20,30,50,80,100],\n",
" 'min_impurity_decrease': [0.0001,0.001,0.01,0.1]\n",
"}\n",
"\n",
"\n",
"# Type of scoring used to compare parameter combinations\n",
"scorer = metrics.make_scorer(metrics.recall_score)\n",
"\n",
"# Run the grid search\n",
"grid_obj = GridSearchCV(rf_tuned, parameters, scoring=scorer,cv=5,n_jobs=-1)\n",
"grid_obj = grid_obj.fit(X_train, y_train)\n",
"\n",
"# Set the clf to the best combination of parameters\n",
"rf_tuned = grid_obj.best_estimator_\n",
"\n",
"# Fit the best algorithm to the data. \n",
"rf_tuned.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "36795a3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.188249 1.0 0.188249 0.316851\n",
"Testing performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.188139 1.0 0.188139 0.316695\n"
]
},
{
"data": {
"image/png": 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gOjoakZGRKC4uhouLCwIDq09KVxsmKCOTxARlVJ2GJigrGjNE57p2O79rUFuGwJk8EZE2ceUnY5AnItLG3DVERCImVDDIExGJF5driIjES2TfDGGQJyKqpKkE+aKiohovtLOze8RdISIyviYzk+/Xrx8kEgketo1eIpEgLS1Nrx0jIjIGof6p2xulaoP8xYsXDdkPIqJGQWwz+Vpz16jVamzYsAFz5sxBcXEx1q5dC5VKXJ/HIiL6m6DW/TAFtT54XbFiBQoLC3Hu3DkIgoDjx49DoVAgMjLSEP0jIjIsof5pfRujWmfyp06dQlRUFKysrNCiRQts3LgRJ0+eNETfiIgMrsnN5KVSKczM/vl3gaWlJaRS7rwkInES1OKaydcarbt06YLt27dDpVLhypUr2Lx5M7p27WqIvhERGZxaJa4gX+tyTUREBM6fP4+CggKMGTMGd+7cQXh4uCH6RkRkcE1uucbW1hZLly41RF+IiIxObMs1tc7kCwoKMGvWLPTt2xfPP/88wsPDoVQqDdE3IiKDEwTdD1NQa5CPjIyEk5MT9uzZg88//xwtW7bE/PnzDdE3IiKDE9QSnQ9TUOtyTU5ODlavXq05nz17Nvz8/PTaKSIiY2lyD17lcjmysrI05zdu3IC9vb1eO0VEZCxNZiYfHBwMACgsLMTIkSMxYMAAmJmZ4fTp03j66acN1kEiIkMSRPbGa7VB3svL66HlgwcP1ldfiIiMzlS2Ruqq2iA/atSoh5YLgoDMzEy9dYiIyJjUTWUm/7cvvvgCK1asQElJiaasVatWzF9DRKLUZJZr/vbZZ59h06ZNWL16NWbMmIHvvvsON27cMETfiIgMrsntrrGzs4OrqyueeeYZFBQUYMqUKfj5558N0TciIoMT2+6aWoO8VCrFrVu34OzsjLNnzwIAPxpCRKKlFiQ6H6ag1iD/6quvYvLkyRg8eDB27doFf39/dOzY0RB9IyIyOEGQ6HzUxdGjR+Hv74+hQ4di8eLFAIDk5GT4+fnB09MTMTExmrppaWnw9/eHl5cXIiIiUFFR/w/PSoSHfan7AXfv3oWNjQ1yc3Nx7tw5DBo0CFZWVvVutK6klo4Ga4tMQ8m148buAjVSFq0bNgk9+6Tub/T3yNivU72srCwEBARg9+7dePzxxxEUFITJkydjwYIF2LZtG9q2bYvJkycjMDAQHh4e8PX1xeLFi+Hm5obw8HB069YNAQEB9RpPtQ9eN23aVO1FO3bswLhx4+rVIBFRY6aPZZjDhw9j2LBhcHBwAADExMQgMzMTzs7OcHJyAgD4+fkhKSkJnTt3RmlpKdzc3AAA/v7+WLVq1aMP8n/++We9bkhEZMrUdXigqlQqH5qVVyaTQSaTac4zMzNhYWGB4OBgXL9+HYMHD8ZTTz1VKUWMXC5Hbm4u8vLyKpXb29sjNze3nqOpIcgvW7as3jcl0regnm8buwvUSO3ITGjQ9XWZyW/ZsgWxsbFVykNCQhAaGqo5V6lUOHPmDLZt2wYbGxtMmTIFzZo1g0TyT1uCIEAikUCtVj+0vL74sVYiIi11eaAaFBT00OwA2rN4AGjdujX69++PVq1aAQBeeuklJCUlwdzcXFNHoVBALpfDwcEBCoVCU56fnw+5XF7XYWjUuruGiKgpqcsWSplMhvbt21c5HgzyQ4YMwYkTJ6BUKqFSqXD8+HF4e3sjPT0dmZmZUKlUOHDgANzd3eHo6AgrKyukpKQAABITE+Hu7l7v8XAmT0SkRR8ffHJ1dcWECRMQEBCA8vJyDBw4EGPGjEHHjh0RGhqKsrIyeHh4wNvbGwAQHR2NyMhIFBcXw8XFBYGBgfVuu9YtlGq1Ghs3bsRff/2FefPmYfv27ZgwYUKlv2boG7dQ0oNebdvH2F2gRqqha/InHV7Rue7AG3sa1JYh1DqTX7FiBQoLC3Hu3DkAwPHjx6FQKBAZGan3zhERGZrIMg3XviZ/6tQpREVFwcrKCra2tti4cSMzUBKRaAmQ6HyYglpn8lKpFGZm//y7wNLSElIpl/KJSJzU+liUN6Jao3WXLl2wfft2qFQqXLlyBZs3b0bXrl0N0TciIoNTm8gMXVe1LtdERETg/PnzKCgowJgxY3Dnzh2Eh4cbom9ERAbX5JZrbG1tsXTpUkP0hYjI6FQmErx1VWuQ/zsl5oO4u4aIxKjJ7a6xs7PTHM2bN8dPP/1kiH4RERmFug6HKah1Jh8SElLpfOLEiZgyZYreOkREZEymstauqzrvhbS1tUVeXp4++kJEZHQm8ulWndUa5BctWqRJcykIAs6fP8/P/xGRaIltC2WtQf6xxx6rdD58+HAMHz5cbx0iIjImlbE78IjVGuSvXr2KFStWGKIvRERGp27ABzoao1qD/MWLFxv8ZRIiIlMhsqwGtQd5e3t7+Pj4wNXVFc2bN9eUc588EYmRqWyN1FW1Qf7evXuwtLTEs88+i2effdaQfSIiMpoms7vmtddeQ0JCQpV98kREYtZk0hrU8sEoIiJRajIz+bKyMly4cKHaYO/i4qK3ThERGUuTWZPPyspCaGjoQ4O8RCLBkSNH9NoxIiJjENsaRrVBvnPnzti3b58Bu0JEZHxNZrmGiKgpajLLNb169TJkP4iIGgVVU5nJ82UnImqKmsxMnoioKWKQJyISsSazu4aIqCni7hoiIhET23JNrR/yJiJqSlR1OOpj+fLlmDNnDgAgOTkZfn5+8PT0RExMjKZOWloa/P394eXlhYiICFRUVNR7PAzyRERa1BLdj7o6deoUEhISAAClpaUIDw9HXFwcDh48iNTUVBw7dgwAEBYWhvnz5+PQoUMQBAHx8fH1Hg+DPBGRFnUdDqVSiezs7CqHUqmsct+ioiLExMQgODgYAHD27Fk4OzvDyckJUqkUfn5+SEpKQk5ODkpLS+Hm5gYA8Pf3R1JSUr3HwzV5IiItddlds2XLFsTGxlYpDwkJQWhoaKWy+fPnY+bMmbh+/ToAIC8vD/b29prf5XI5cnNzq5Tb29sjNze3boPQwiBPRKRFXYcwHxQUhFGjRlUpl8lklc53796Ntm3bon///ti7d+/9dtTqSp9V/fszq9WV1xeDPBGRlro8UJXJZFUC+sMcPHgQCoUCI0aMwK1bt3D37l3k5OTA3NxcU0ehUEAul8PBwQEKhUJTnp+fD7lcXpchVMIgT0SkRR9bKDdt2qT557179+Knn37C+++/D09PT2RmZqJ9+/Y4cOAARo8eDUdHR1hZWSElJQU9e/ZEYmIi3N3d6902gzwRkRZDvQxlZWWFqKgohIaGoqysDB4eHvD29gYAREdHIzIyEsXFxXBxcUFgYGC925EIJvCdP6mlo7G7QI3Mq237GLsL1EjtyExo0PWRTwboXHdxxo4GtWUInMkTEWlp9LPeOmKQJyLSIra0BgzyJmLY0BexePEcWFlZ4dy5NEyc9DZu3y42drfIAAaO8oDvpBEQBOBeaRm2LFiP4W/5o41zW00duZMcaafP48MJy9C8pS3eXDgBjk85wdLKEvti9+BEwjEjjsC0qEQ2l2eQNwGtW7fC+nUr4T54JC5dSseypeFYuiQcodPCjd010rO2HdshIDwQET7voCjvJtyGPIeZa2dj2oBJmjode3TG9NVh2DTvMwBA8IehyLmUjU+nf4RWDo9j+Tcf4cKpVBTeKDDWMEyK2GbyTGtgAl5+2QNnzvyOS5fSAQBr1m5FwJiqL2CQ+JTfK8e62XEoyrsJALhy9jLs7O1gbnF/fmZuIUXwymnYtnAjCq8XoHlLW3Qf5Iq9H+0CABTeKMC8EbNRXHTbaGMwNWoIOh+mgDN5E+DUvh2ysq9pzrOzr6NlSxlatLDlko3I5WcrkJ/9z4sxY+eNQ8q3P0NVfj8r4ZDXXkRRbiHOHDoNAHB4si2K8m5i2MThcB38HCwsLfDfzxJxI/3aQ+9PVZlG6NYdg7wJMDMzw8N2uqpU9U12SqbGytoKwR9OQ6u2j2N50EJN+dD/DMf6uXGac3MLc8ifcEDJ7RK8PzocbZwdMH/PEtxIv4b01CvG6LrJ4XINGdzVrBy0a9dGc+7o6IDCwpu4e7fEiL0iQ3m8XWu8t3cZ1CoVFr8+H3eVdwEAzi4dYC41Q9qP5zV1b+YWAgCO7T4CAMjNvIE/f76ITm5PGb7jJkoFQefDFDDIm4DDh4+hb5/n0LlzBwDA5Elv4Kv93xi5V2QIzZo3w7xdi/Bz0o/4JHQlysvuaX57pq8Lziefq1RfkZWH9HOX4f7KEACArHVLPNXzaVw5e9mg/TZlXJMng1MoCjBh4izs+uIzWFpa4MrlTLw5frqxu0UG4Bk0DK0d7dHLqx96efXTlC8NWACHDu2gyM6rcs3KSVEYt2gSXhrrDYmZGfZ+HI8rZy8ZstsmzTRCt+6Y1oBMEtMaUHUamtZg8pP/0rnu2ozdDWrLEDiTJyLSIrYHr3oJ8teu1bxdq127dvpoloiowQSRLdjoJchPnjwZGRkZkMvlVbb+SSQSHDlyRB/NEhE1mKnsmtGVXnbX7Ny5Ex06dMCKFStw9OjRSkdTD/DDhr6IX1IO43zqD/hi51q0aGGrcx0zMzN8GP0+Us8dw8ULJzBp4huaayZOGIs/0k7ix1MH8eSTTpry/Ylb0bVrZ/0PjOot+MNp8Jk0AgAgMTPD+MWTseLbVVjx7SoERAQ99Bpd6nm8+iLe2fBP6gtzCyne3RyJlcfi8J+lwZpy+RMOCN/+3qMdlAmry4e8TYFegrytrS0WL16Mffv26eP2JuvvHDSvvjYJLt3ckZ6eiaVLwnWuM2niG+jyVAe4ur2AfgN8MG3aBPTu5QYAeDdsKnq4vYCVMWvwVvCbAIDRo32RlvYXLl7kzorGqF3n9ojYuRB9hvXXlA3y90DbTo6Y7TkDc71n4pm+Lug7bECVa2uq17ylLcYvCUbggv8AWt8GdfV4FgXX8jHL4y20drRH+y5PALj/Fu3nizfrd7AmRC0IOh+mQG/75Hv06IFFixbp6/YmSZccNDXVGTnCG5u3xkOlUqGo6Bbi4xMREOAPACivqICNjTVaymS4V14Oa+tmeHvmZCxcvNKAI6S68Awciu++OIzT/03WlJmZm8HK2goWllJILS0gtZBW2huvS71+vgNxM7cQ25dsrnRNxb1yWNk0g7mFFJbWVqgor8CzL/RC4fV8XE3L0OdQTYpQh8MU8GUoA6opB40uddo7tUN2VuXf2re/n242InIZjny7G6NGDsWqT9YjfO50fLp6M4qL7xhgZFQfm+evQ3Li8Uplx3Z/hzu3ivHpTxsQ9/NG5GbewC9HzlS5tqZ6R7YfQsKqeFTcK690zbnjv6O87B6Wfb0SF06lIj8nDyNDX8Hu6Mb/dSNDEtvLUAzyBqRLDpqa6jz4m0QigUp1f2UwIeEgnuv5Mob5/hu2ts3Rt89z2LFjLz6Mfh/7E7dixvRJVe5Jjc/oGa9BWahEcM9xCOk3Ac3tbDFs4vB619MmCALWzY7Duy9Nw5cxX8AveBS+33UELVrJMHPtbLyzMQLOLh30NTSTIdThP6aAQd6AdMlBU1OdrKs5aKv1W7t2bZCTfb1KO9EfLMC7cxbipRcHoUWL5vAbEQhvryHo1OlJ/QyMHpne3v1wLP4IVOUVKLl9F8f3fIf/69+93vWq83i71ug20BXf7/oWo2e9joPrv8LG8DUIem/CoxyOSaqAoPNhChjkDUiXHDQ11flq/yGMe/N1mJubo2VLGV59dQQSv0qqdL3PsJeQk3MDv/12HlZWVqiouP+3BEEQYG3dTN9DpAbKSL2Mfj4DAQDmUnM893IfXPr1j3rXq87YyHHYGbUVgiDAwtICapUaarUaVtZWj2YgJkxsM3m+8WpA1eWg6flcD6xdG41evT1rzFOzZu1WdOz4JH5JOQxLC0usW78NPxz/UXN/S0tLRIRPh4/f/a2V3xw+hinBQbh44QSOfncSqakXjTJu0t22hZvw5sKJiD7yCdRqNVJPnsX+NfsAAK/MGgMA2LNyZ431atNtYA+U3inBpV//BAD8d10iJn8QAkgk+HzRRn0My6SYytZIXTF3DZkk5q6h6jQ0d82oJ/x0rptwdX+D2jIEzuSJiLSYyq4ZXTHIExFpEVtaAwZ5IiItnMkTEYmYCTymrBNuoSQi0qKvBGWxsbHw8fGBj48PVqxYAQBITk6Gn58fPD09ERMTo6mblpYGf39/eHl5ISIiAhUVFfUeD4M8EZEWfeyTT05OxokTJ5CQkIB9+/bh/PnzOHDgAMLDwxEXF4eDBw8iNTUVx44dAwCEhYVh/vz5OHToEARBQHx8fL3HwyBPRKRFH7lr7O3tMWfOHFhaWsLCwgKdOnVCRkYGnJ2d4eTkBKlUCj8/PyQlJSEnJwelpaVwc3MDAPj7+yMpKanmBmrANXkiIi0qQfeFGKVSCaVSWaVcJpNBJpNpzp966inNP2dkZODrr7/G2LFjYW9vrymXy+XIzc1FXl5epXJ7e3vk5ubWdRgaDPJERFrqsgyzZcsWxMbGVikPCQlBaGholfK//voLkydPxrvvvgtzc3NkZGT8064gQCKRQK1WQ6L1HYC/y+uLQZ6ISEtdPgYSFBSEUaNGVSnXnsX/LSUlBdOmTUN4eDh8fHzw008/QaFQaH5XKBSQy+VwcHCoVJ6fnw+5XF7HUfyDQZ6ISEtdNlA+uCxTnevXr2Pq1KmIiYlB//73vwTm6uqK9PR0ZGZmon379jhw4ABGjx4NR0dHWFlZISUlBT179kRiYiLc3d3rORoGeSKiSvTxMtSGDRtQVlaGqKgoTdnrr7+OqKgohIaGoqysDB4eHvD29gYAREdHIzIyEsXFxXBxcUFgYGC922aCMjJJTFBG1WlogrL+jkN0rnsq57sGtWUInMkTEWmpy+4aU8AgT0SkxVQ+BqIrBnkiIi0msIJdJwzyRERamIWSiEjEOJMnIhIxlci+8sogT0SkpS5vvJoCBnkiIi3cXUNEJGKcyRMRiRhn8kREIsaZPBGRiDGtARGRiHG5hohIxATO5ImIxItpDYiIRIxpDYiIRIwzeSIiEVOpuSZPRCRa3F1DRCRiXJMnIhIxrskTEYkYZ/JERCLGB69ERCLG5RoiIhHjcg0RkYgx1TARkYhxnzwRkYiJbSZvZuwOEBE1JmpBrfNRF/v378ewYcPg6emJ7du366n3VXEmT0SkRR8PXnNzcxETE4O9e/fC0tISr7/+Ovr27YvOnTs/8rYexCBPRKSlLkFeqVRCqVRWKZfJZJDJZJrz5ORk9OvXD3Z2dgAALy8vJCUlISQkpMH9rY1JBPmKeznG7gIRNRHldYg3n3zyCWJjY6uUh4SEIDQ0VHOel5cHe3t7zblcLsfZs2cb1lEdmUSQJyJqjIKCgjBq1Kgq5dqzeABQq9WQSCSac0EQKp3rE4M8EVE9PbgsUx0HBwecOXNGc65QKCCXy/XZNQ3uriEi0rMBAwbg1KlTKCwsRElJCb755hu4u7sbpG3O5ImI9KxNmzaYOXMmAgMDUV5ejldeeQU9evQwSNsSQWyJGoiISIPLNUREIsYgT0QkYgzyREQixiBPRCRiDPImwljJjajxKy4uhq+vL7Kzs43dFWqEGORNwN/JjXbs2IF9+/Zh165duHTpkrG7RY3A77//jjFjxiAjI8PYXaFGikHeBGgnN7KxsdEkNyKKj4/HggULDPb2JJkevgxlAoyZ3IgatyVLlhi7C9TIcSZvAoyZ3IiITBuDvAlwcHCAQqHQnBsyuRERmTYGeRNgzORGRGTauCZvAoyZ3IiITBsTlBERiRiXa4iIRIxBnohIxBjkiYhEjEGeiEjEGOSJiESMQZ6qlZ2djWeeeQYjRozQHMOHD8eePXsafO/Jkydj7969AIARI0ZAqVRWW/f27dsIDAyscxtJSUl44403qpSfPn0avr6+tV7/9NNPo7CwsE5tzpkzBxs2bKjTNUT6xH3yVKNmzZohMTFRc56bmwtfX19069YNXbt2fSRtaN//YW7duoVz5849kraImhoGeaqTNm3awNnZGRkZGbhw4QL27NmDkpIS2NraYtu2bdi9ezd27twJtVoNOzs7zJs3D506dUJubi7mzJmDvLw8tGvXDgUFBZp7Pv300zh16hRatWqFtWvXIiEhAVKpFM7OzoiKisLcuXNRWlqKESNGYO/evcjIyMCSJUtQVFQElUqFN954A6+88goA4OOPP8b+/fthZ2cHZ2fnWseTnp6OhQsX4s6dO1AoFOjatSs++ugjWFlZAQA++ugjnDt3Dmq1GjNmzMCQIUMAoNpxEjU6AlE1srKyBDc3t0plv/zyi9C7d2/h2rVrwpdffin07t1buH37tiAIgnD69GkhICBAuHv3riAIgnD8+HHB29tbEARBeOutt4SYmBhBEAQhIyNDcHNzE7788ktBEAShS5cuQkFBgfDtt98Knp6eQlFRkSAIgrB06VIhLi6uUj/Ky8uFYcOGCampqYIgCIJSqRSGDh0q/Prrr8Lhw4eFYcOGCbdv3xbKy8uFSZMmCWPHjq0yrh9//FHw8fERBEEQoqKihH379gmCIAj37t0TfH19haSkJE2/1q5dKwiCIPzxxx9Cnz59hIKCghrHOXv2bGH9+vUN+t+d6FHiTJ5q9PcMGgBUKhUee+wxfPDBB2jbti2A+7NwW1tbAMD333+PzMxMvP7665rrlUolioqKkJycjNmzZwMAnJ2d0bdv3yptnTp1Ct7e3mjZsiUAYO7cuQBQ6YtHGRkZuHr1KsLDwyv18cKFC7h8+TJefvllTX9Gjx6Nbdu21Ti+sLAwnDx5EuvWrUNGRgby8vJw9+5dze9jxowBAHTp0gWdOnXCr7/+ipSUlGrHSdTYMMhTjR5ck3+QjY2N5p/VajVGjBiBsLAwzXleXh5atmwJiUQCQSuDhlRa9Y+eubl5pRTKSqWyygNZlUqFFi1aVOpTfn4+WrRogRUrVlRqw9zcvNbxzZo1CyqVCkOHDsXgwYNx/fr1SvcwM/tnb4JarYZUKq1xnESNDXfX0CPz/PPP47///S/y8vIAADt37kRQUBAAYNCgQdi1axcA4Nq1azh9+nSV6wcMGIDDhw+juLgYAPDJJ59g8+bNkEqlUKlUEAQBHTp0qPQvnuvXr8PX1xepqalwd3dHUlISlEol1Gp1rQ90AeDEiROYOnUqhg0bBuD+5/RUKpXm94SEBADA+fPncfXqVbi6utY4TqLGhjN5emSef/55TJw4EePHj4dEIoGtrS1iY2MhkUiwYMECzJ07F0OHDoWDg8NDd+Z4eHjg0qVLmiWSzp07Y9GiRbC2tkaPHj3g4+OD7du3Iy4uDkuWLMH69etRUVGB6dOno2fPngCAP/74A6NHj4ZMJkPXrl1x8+bNGvs8c+ZMTJ06FTY2NrC1tUXv3r1x9epVze9ZWVkYOXIkJBIJVq5cCTs7uxrHSdTYMAslEZGIcbmGiEjEGOSJiESMQZ6ISMQY5ImIRIxBnohIxBjkiYhEjEGeiEjEGOSJiETs/wFS8aC1vm/HHgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Calculating different metrics\n",
"rf_tuned_model_train_perf=model_performance_classification_sklearn(rf_tuned,X_train,y_train)\n",
"print(\"Training performance:\\n\",rf_tuned_model_train_perf)\n",
"rf_tuned_model_test_perf=model_performance_classification_sklearn(rf_tuned,X_test,y_test)\n",
"print(\"Testing performance:\\n\",rf_tuned_model_test_perf)\n",
"\n",
"#Creating confusion matrix\n",
"confusion_matrix_sklearn(rf_tuned, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"id": "cdea0a5f",
"metadata": {},
"source": [
"- Overall performance of the model is terrible.\n",
"- Test Recall is overfitting on train and test data."
]
},
{
"cell_type": "markdown",
"id": "191dda57",
"metadata": {},
"source": [
"### Tuning Bagging Classifier"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "a9da3cf2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BaggingClassifier(max_features=0.95, max_samples=0.9, n_estimators=50,\n",
" random_state=1)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Choose the type of classifier. \n",
"bagging_estimator_tuned = BaggingClassifier(random_state=1)\n",
"\n",
"# Grid of parameters to choose from\n",
"parameters = {'max_samples': [0.8,0.85,0.9,0.95,1], \n",
" 'max_features': [0.8,0.9,0.95,1],\n",
" 'n_estimators' : [10,20,30,40,50],\n",
" }\n",
"\n",
"# Type of scoring used to compare parameter combinations\n",
"acc_scorer = metrics.make_scorer(metrics.recall_score)\n",
"\n",
"# Run the grid search\n",
"grid_obj = GridSearchCV(bagging_estimator_tuned, parameters, scoring=acc_scorer,cv=5)\n",
"grid_obj = grid_obj.fit(X_train, y_train)\n",
"\n",
"# Set the clf to the best combination of parameters\n",
"bagging_estimator_tuned = grid_obj.best_estimator_\n",
"\n",
"# Fit the best algorithm to the data.\n",
"bagging_estimator_tuned.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "5063b1a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.999415 0.996894 1.0 0.998445\n",
"Testing performance:\n",
" Accuracy Recall Precision F1\n",
"0 0.925699 0.67029 0.91133 0.772443\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Calculating different metrics\n",
"bagging_estimator_tuned_model_train_perf=model_performance_classification_sklearn(bagging_estimator_tuned,X_train,y_train)\n",
"print(\"Training performance:\\n\",bagging_estimator_tuned_model_train_perf)\n",
"bagging_estimator_tuned_model_test_perf=model_performance_classification_sklearn(bagging_estimator_tuned,X_test,y_test)\n",
"print(\"Testing performance:\\n\",bagging_estimator_tuned_model_test_perf)\n",
"\n",
"#Creating confusion matrix\n",
"confusion_matrix_sklearn(bagging_estimator_tuned, X_test, y_test)"
]
},
{
"cell_type": "markdown",
"id": "87a8db0e",
"metadata": {},
"source": [
"- Model performance improved significantly on all metrics\n",
"- Test Recall is overfitting and recall score dropped dramaticaly on test data."
]
},
{
"cell_type": "markdown",
"id": "fa348bdf",
"metadata": {},
"source": [
"### Comparing Models"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "07ca08a8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training performance comparison:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Decision Tree</th>\n",
" <th>Decision Tree Estimator</th>\n",
" <th>Random Forest Estimator</th>\n",
" <th>Random Forest Tuned</th>\n",
" <th>Bagging Classifier</th>\n",
" <th>Bagging Estimator Tuned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>1.0</td>\n",
" <td>0.188249</td>\n",
" <td>1.0</td>\n",
" <td>0.188249</td>\n",
" <td>0.993277</td>\n",
" <td>0.999415</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>1.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.967391</td>\n",
" <td>0.996894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>1.0</td>\n",
" <td>0.188249</td>\n",
" <td>1.0</td>\n",
" <td>0.188249</td>\n",
" <td>0.996800</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1</th>\n",
" <td>1.0</td>\n",
" <td>0.316851</td>\n",
" <td>1.0</td>\n",
" <td>0.316851</td>\n",
" <td>0.981875</td>\n",
" <td>0.998445</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Decision Tree Decision Tree Estimator Random Forest Estimator \\\n",
"Accuracy 1.0 0.188249 1.0 \n",
"Recall 1.0 1.000000 1.0 \n",
"Precision 1.0 0.188249 1.0 \n",
"F1 1.0 0.316851 1.0 \n",
"\n",
" Random Forest Tuned Bagging Classifier Bagging Estimator Tuned \n",
"Accuracy 0.188249 0.993277 0.999415 \n",
"Recall 1.000000 0.967391 0.996894 \n",
"Precision 0.188249 0.996800 1.000000 \n",
"F1 0.316851 0.981875 0.998445 "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# training performance comparison\n",
"\n",
"models_train_comp_df = pd.concat(\n",
" [dtree_model_train_perf.T,dtree_estimator_model_train_perf.T,rf_estimator_model_train_perf.T,rf_tuned_model_train_perf.T,\n",
" bagging_classifier_model_train_perf.T,bagging_estimator_tuned_model_train_perf.T],\n",
" axis=1,\n",
")\n",
"models_train_comp_df.columns = [\n",
" \"Decision Tree\",\n",
" \"Decision Tree Estimator\",\n",
" \"Random Forest Estimator\",\n",
" \"Random Forest Tuned\",\n",
" \"Bagging Classifier\",\n",
" \"Bagging Estimator Tuned\"]\n",
"print(\"Training performance comparison:\")\n",
"models_train_comp_df"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "f28f6f8a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing performance comparison:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Decision Tree</th>\n",
" <th>Decision Tree Estimator</th>\n",
" <th>Random Forest Estimator</th>\n",
" <th>Random Forest Tuned</th>\n",
" <th>Bagging Classifier</th>\n",
" <th>Bagging Estimator Tuned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.882072</td>\n",
" <td>0.188139</td>\n",
" <td>0.905930</td>\n",
" <td>0.188139</td>\n",
" <td>0.904567</td>\n",
" <td>0.925699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.728261</td>\n",
" <td>1.000000</td>\n",
" <td>0.547101</td>\n",
" <td>1.000000</td>\n",
" <td>0.590580</td>\n",
" <td>0.670290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.672241</td>\n",
" <td>0.188139</td>\n",
" <td>0.920732</td>\n",
" <td>0.188139</td>\n",
" <td>0.857895</td>\n",
" <td>0.911330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1</th>\n",
" <td>0.699130</td>\n",
" <td>0.316695</td>\n",
" <td>0.686364</td>\n",
" <td>0.316695</td>\n",
" <td>0.699571</td>\n",
" <td>0.772443</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Decision Tree Decision Tree Estimator Random Forest Estimator \\\n",
"Accuracy 0.882072 0.188139 0.905930 \n",
"Recall 0.728261 1.000000 0.547101 \n",
"Precision 0.672241 0.188139 0.920732 \n",
"F1 0.699130 0.316695 0.686364 \n",
"\n",
" Random Forest Tuned Bagging Classifier Bagging Estimator Tuned \n",
"Accuracy 0.188139 0.904567 0.925699 \n",
"Recall 1.000000 0.590580 0.670290 \n",
"Precision 0.188139 0.857895 0.911330 \n",
"F1 0.316695 0.699571 0.772443 "
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# testing performance comparison\n",
"\n",
"models_test_comp_df = pd.concat(\n",
" [dtree_model_test_perf.T,dtree_estimator_model_test_perf.T,rf_estimator_model_test_perf.T,rf_tuned_model_test_perf.T,\n",
" bagging_classifier_model_test_perf.T, bagging_estimator_tuned_model_test_perf.T],\n",
" axis=1,\n",
")\n",
"models_test_comp_df.columns = [\n",
" \"Decision Tree\",\n",
" \"Decision Tree Estimator\",\n",
" \"Random Forest Estimator\",\n",
" \"Random Forest Tuned\",\n",
" \"Bagging Classifier\",\n",
" \"Bagging Estimator Tuned\"]\n",
"print(\"Testing performance comparison:\")\n",
"models_test_comp_df"
]
},
{
"cell_type": "markdown",
"id": "3dab3fdc",
"metadata": {},
"source": [
"### Conclusion"
]
},
{
"cell_type": "markdown",
"id": "a8040fc7",
"metadata": {},
"source": [
"Decision Tree, Random Forest performed good albeit they are highly ovefitting,Decision tree has a higher recall than tuned bagging estimator. Tuned bagging estimator performed best as a model over all metrics and it is not overfitting. "
]
},
{
"cell_type": "markdown",
"id": "6f0ca54c",
"metadata": {},
"source": [
"## D. Boosting Model Building"
]
},
{
"cell_type": "markdown",
"id": "37b8f0ee",
"metadata": {},
"source": [
"### AdaBoost Classifier"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "a37936bc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Accuracy Recall Precision F1\n",
"0 0.844198 0.302795 0.698925 0.422535\n",
" Accuracy Recall Precision F1\n",
"0 0.846626 0.304348 0.717949 0.427481\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Fitting the model\n",
"ab_classifier = AdaBoostClassifier(random_state=1)\n",
"ab_classifier.fit(X_train,y_train)\n",
"\n",
"#Calculating different metrics\n",
"ab_classifier_model_train_perf=model_performance_classification_sklearn(ab_classifier,X_train,y_train)\n",
"print(ab_classifier_model_train_perf)\n",
"ab_classifier_model_test_perf=model_performance_classification_sklearn(ab_classifier,X_test,y_test)\n",
"print(ab_classifier_model_test_perf)\n",
"\n",
"#Creating confusion matrix\n",
"confusion_matrix_sklearn(ab_classifier,X_test,y_test)"
]
},
{
"cell_type": "markdown",
"id": "e7f3d181",
"metadata": {},
"source": [
"AdaBoost model is having a terrible performance on all metrics"
]
},
{
"cell_type": "markdown",
"id": "8f315947",
"metadata": {},
"source": [
"#### Hyper parameter tuning"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "a6a7683c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3),\n",
" learning_rate=1.2000000000000002, n_estimators=100,\n",
" random_state=1)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Choose the type of classifier. \n",
"abc_tuned = AdaBoostClassifier(random_state=1)\n",
"\n",
"# Grid of parameters to choose from\n",
"parameters = {\n",
" #Let's try different max_depth for base_estimator\n",
" \"base_estimator\":[DecisionTreeClassifier(max_depth=1),DecisionTreeClassifier(max_depth=2),\n",
" DecisionTreeClassifier(max_depth=3)],\n",
" \"n_estimators\": np.arange(10,110,10),\n",
" \"learning_rate\":np.arange(0.1,2,0.1)\n",
"}\n",
"\n",
"# Type of scoring used to compare parameter combinations\n",
"scorer = metrics.make_scorer(metrics.recall_score)\n",
"\n",
"# Run the grid search\n",
"grid_obj = GridSearchCV(abc_tuned, parameters, scoring=scorer,cv=5)\n",
"grid_obj = grid_obj.fit(X_train, y_train)\n",
"\n",
"# Set the clf to the best combination of parameters\n",
"abc_tuned = grid_obj.best_estimator_\n",
"\n",
"# Fit the best algorithm to the data.\n",
"abc_tuned.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "9f169895",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Accuracy Recall Precision F1\n",
"0 0.989184 0.959627 0.982512 0.970935\n",
" Accuracy Recall Precision F1\n",
"0 0.869121 0.59058 0.673554 0.629344\n"
]
},
{
"data": {
"image/png": "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