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TELCO CUSTOMER CHURN
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telco_customer_churn.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TELCO CUSTOMER CHURN\n",
"### Focused customer retention programs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Who is Telco ?\n",
"Telco Systems is market-leading solutions enable service providers to create and operate high quality, service assured, carrier-grade, intelligent networks. They bring over 40 years of experience to the design and development of advanced, high-performance telecom network communications solutions. \n",
"\n",
"Telco provide the capabilities for service differentiation that enable new forms of revenue production, maximizing network profitability. Service providers, large and small, depend on our consistent delivery of advanced solutions, enabling them to stay ahead of the capacity crunch while keeping total cost of ownership to a minimum.\n",
"\n",
"(Refrence - http://www.telco.com/index.php?page=company-profile)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Business Objective\n",
"\n",
"Every retailer is concerned about high customer churn rate. Churn rate the number of customers who drop out of the buying cycle. It could be non renewal of a loyalty program, or unhappy customers going in search of a better service. One of the key things for the busines to run is loyal customeers , meaning minimize the churn rate.\n",
"\n",
"#### Business objective of this exercise :\n",
"1. Analyze customer data to understand reason for churn and who could be the next potential customer to leave the company\n",
"2. What contributes to the higher churn rate of customer and what could be some of the probable solution to address the same.\n",
"\n",
"#### What type of problem is it ?\n",
"Supervised Machine Learning - Classfication problem\n",
"\n",
"#### How should performance be measured ?\n",
"Model performance of at least 70% is expected\n",
"\n",
"#### Assumptions made :\n",
"1. The sample data is correct represetation of the entire population and is randomly selected\n",
"2. The columns in the dataset are exhaustive list of features that determine churn rate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Customer churn rate in the United States in 2017, by industry\n",
"Credit : https://www.statista.com/statistics/816735/customer-churn-rate-by-industry-us/\n",
"\n",
"Looks like cable industry has highest churn rate.\n",
"<img src=\"files/customer_churn_rate.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Content of the dataset\n",
"Each row represents a customer, each column contains customer’s attributes described on the column Metadata.\n",
"\n",
"The data set includes information about:\n",
"\n",
"1. Customers who left within the last month – the column is called Churn\n",
"2. Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies\n",
"3. Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges\n",
"4. Demographic info about customers – gender, age range, and if they have partners and dependents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#importing the libraries\n",
"\n",
"#Data Processing Libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"#Data Vizuaization Libraries\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Pretty display for notebooks\n",
"%matplotlib inline\n",
"\n",
"# Machine Learning Library\n",
"from sklearn.preprocessing import LabelEncoder # Encode Categorical Variable to Numerical Variable\n",
"from sklearn.preprocessing import Imputer # Imputer Class to replace missing values\n",
"from sklearn.metrics import confusion_matrix # Library for model evaluation\n",
"from sklearn.metrics import accuracy_score # Library for model evaluation\n",
"from sklearn.model_selection import train_test_split # Library to split datset into test and train\n",
"\n",
"from sklearn.linear_model import LogisticRegression # Logistic Regression Classifier\n",
"from sklearn.linear_model import SGDClassifier # Stochastic Gradient Descent Classifier\n",
"from sklearn.tree import DecisionTreeClassifier # Decision Tree Classifier\n",
"from sklearn.ensemble import RandomForestClassifier # Random Forest Classifier\n",
"from sklearn.neighbors import KNeighborsClassifier # K Nearest neighbors Classifier\n",
"from sklearn.naive_bayes import GaussianNB #Naive Bayes Classifier\n",
"from sklearn.svm import SVC #Support vector Machine Classifier\n",
"from sklearn.ensemble import AdaBoostClassifier # Ada Boost Classifier\n",
"from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.metrics import precision_recall_curve\n",
"from sklearn.metrics import average_precision_score\n",
"\n",
"#Ignoring the warnings\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get Data\n",
"\n",
"1. Source of data - https://www.kaggle.com/blastchar/telco-customer-churn\n",
"2. Space - 955 KB\n",
"3. Legal Obligations - Free Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Read .csv file from location and load into pandas DataFrame\n",
"datset_churn = pd.read_csv('https://github.com/jbanerje/Data-Science-and-Machine-Learning/tree/master/data/WA_Fn-UseC_-Telco-Customer-Churn.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"datset_churn_copy = datset_churn.copy() # Keeping a backup of original datset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Basic Overview of dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Find out total rows and columns or shapes of the dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7043, 21)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datset_churn.shape # output = (rows, columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 7043 rows and 21 columns including the target/output variable."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Let's get the column names/information"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['customerID' 'gender' 'SeniorCitizen' 'Partner' 'Dependents' 'tenure'\n",
" 'PhoneService' 'MultipleLines' 'InternetService' 'OnlineSecurity'\n",
" 'OnlineBackup' 'DeviceProtection' 'TechSupport' 'StreamingTV'\n",
" 'StreamingMovies' 'Contract' 'PaperlessBilling' 'PaymentMethod'\n",
" 'MonthlyCharges' 'TotalCharges' 'Churn']\n"
]
}
],
"source": [
"# Getting the column names\n",
"print(datset_churn.columns.values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observation - Notice 'customerID' , 'gender' and 'tenure' in lowercase. We will rename to convert the first letter to uppercase"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['CustomerID' 'Gender' 'SeniorCitizen' 'Partner' 'Dependents' 'Tenure'\n",
" 'PhoneService' 'MultipleLines' 'InternetService' 'OnlineSecurity'\n",
" 'OnlineBackup' 'DeviceProtection' 'TechSupport' 'StreamingTV'\n",
" 'StreamingMovies' 'Contract' 'PaperlessBilling' 'PaymentMethod'\n",
" 'MonthlyCharges' 'TotalCharges' 'Churn']\n"
]
}
],
"source": [
"# Renaming the 3 columns.\n",
"datset_churn = datset_churn.rename(columns={'customerID' : 'CustomerID' , 'gender': 'Gender', 'tenure':'Tenure'})\n",
"print(datset_churn.columns.values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Target Variable or The variable we want to predict is 'Churn'\n",
"\n",
"Feature Variable - All Other columns (First 20 columnns)\n",
"\n",
"1. Customers who left within the last month – 'Churn'\n",
"2. Customer Services – 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup' 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies'\n",
"3. Customer Account information – 'Tenure', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges'\n",
"4. Customer Personal Information – 'Gender', 'SeniorCitizen', 'Partner', 'Dependents'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7043 entries, 0 to 7042\n",
"Data columns (total 21 columns):\n",
"CustomerID 7043 non-null object\n",
"Gender 7043 non-null object\n",
"SeniorCitizen 7043 non-null int64\n",
"Partner 7043 non-null object\n",
"Dependents 7043 non-null object\n",
"Tenure 7043 non-null int64\n",
"PhoneService 7043 non-null object\n",
"MultipleLines 7043 non-null object\n",
"InternetService 7043 non-null object\n",
"OnlineSecurity 7043 non-null object\n",
"OnlineBackup 7043 non-null object\n",
"DeviceProtection 7043 non-null object\n",
"TechSupport 7043 non-null object\n",
"StreamingTV 7043 non-null object\n",
"StreamingMovies 7043 non-null object\n",
"Contract 7043 non-null object\n",
"PaperlessBilling 7043 non-null object\n",
"PaymentMethod 7043 non-null object\n",
"MonthlyCharges 7043 non-null float64\n",
"TotalCharges 7032 non-null float64\n",
"Churn 7043 non-null object\n",
"dtypes: float64(2), int64(2), object(17)\n",
"memory usage: 1.1+ MB\n",
"None\n"
]
}
],
"source": [
"print(datset_churn.info())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Which features are numerical?\n",
"SeniorCitizen, Tenure, MonthlyCharges, TotalCharges\n",
"\n",
"Continous - Tenure, MonthlyCharges, TotalCharges\n",
"Discrete - SeniorCitizen\n",
"\n",
"#### Which features are categorical?\n",
"PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, gender, Partner, Dependents\n",
"\n",
"#### Which features are Nominal or Ordinal ?\n",
"Ordinal data (variables with a meaningful order) - No.\n",
"\n",
"Nominal data (categories that have no meaningful order) - All Columns.\n",
"\n",
"#### Which features are mixed data types?\n",
"None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Taking first look into data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CustomerID</th>\n",
" <th>Gender</th>\n",
" <th>SeniorCitizen</th>\n",
" <th>Partner</th>\n",
" <th>Dependents</th>\n",
" <th>Tenure</th>\n",
" <th>PhoneService</th>\n",
" <th>MultipleLines</th>\n",
" <th>InternetService</th>\n",
" <th>OnlineSecurity</th>\n",
" <th>...</th>\n",
" <th>DeviceProtection</th>\n",
" <th>TechSupport</th>\n",
" <th>StreamingTV</th>\n",
" <th>StreamingMovies</th>\n",
" <th>Contract</th>\n",
" <th>PaperlessBilling</th>\n",
" <th>PaymentMethod</th>\n",
" <th>MonthlyCharges</th>\n",
" <th>TotalCharges</th>\n",
" <th>Churn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7590-VHVEG</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>No</td>\n",
" <td>No phone service</td>\n",
" <td>DSL</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Electronic check</td>\n",
" <td>29.85</td>\n",
" <td>29.85</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5575-GNVDE</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>34</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>One year</td>\n",
" <td>No</td>\n",
" <td>Mailed check</td>\n",
" <td>56.95</td>\n",
" <td>1889.50</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3668-QPYBK</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Mailed check</td>\n",
" <td>53.85</td>\n",
" <td>108.15</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7795-CFOCW</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>45</td>\n",
" <td>No</td>\n",
" <td>No phone service</td>\n",
" <td>DSL</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>Yes</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>One year</td>\n",
" <td>No</td>\n",
" <td>Bank transfer (automatic)</td>\n",
" <td>42.30</td>\n",
" <td>1840.75</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9237-HQITU</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Fiber optic</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Electronic check</td>\n",
" <td>70.70</td>\n",
" <td>151.65</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" CustomerID Gender SeniorCitizen Partner Dependents Tenure PhoneService \\\n",
"0 7590-VHVEG Female 0 Yes No 1 No \n",
"1 5575-GNVDE Male 0 No No 34 Yes \n",
"2 3668-QPYBK Male 0 No No 2 Yes \n",
"3 7795-CFOCW Male 0 No No 45 No \n",
"4 9237-HQITU Female 0 No No 2 Yes \n",
"\n",
" MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n",
"0 No phone service DSL No ... No \n",
"1 No DSL Yes ... Yes \n",
"2 No DSL Yes ... No \n",
"3 No phone service DSL Yes ... Yes \n",
"4 No Fiber optic No ... No \n",
"\n",
" TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n",
"0 No No No Month-to-month Yes \n",
"1 No No No One year No \n",
"2 No No No Month-to-month Yes \n",
"3 Yes No No One year No \n",
"4 No No No Month-to-month Yes \n",
"\n",
" PaymentMethod MonthlyCharges TotalCharges Churn \n",
"0 Electronic check 29.85 29.85 No \n",
"1 Mailed check 56.95 1889.50 No \n",
"2 Mailed check 53.85 108.15 Yes \n",
"3 Bank transfer (automatic) 42.30 1840.75 No \n",
"4 Electronic check 70.70 151.65 Yes \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datset_churn.head() # This will print first 5 rows in pandas dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Understanding the summary statistics , central tendency and dispersion of dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Summary Statistics of Object/Categorical variable. "
]
},
{
"cell_type": "code",
"execution_count": 447,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>PhoneService</th>\n",
" <th>MultipleLines</th>\n",
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" <th>OnlineSecurity</th>\n",
" <th>OnlineBackup</th>\n",
" <th>DeviceProtection</th>\n",
" <th>TechSupport</th>\n",
" <th>StreamingTV</th>\n",
" <th>StreamingMovies</th>\n",
" <th>Contract</th>\n",
" <th>PaperlessBilling</th>\n",
" <th>PaymentMethod</th>\n",
" <th>Churn</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>count</th>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
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" <td>7043</td>\n",
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" <td>7043</td>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
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" <td>7043</td>\n",
" <td>7043</td>\n",
" <td>7043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>7043</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
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" <td>3090-QFUVD</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Fiber optic</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Month-to-month</td>\n",
" <td>Yes</td>\n",
" <td>Electronic check</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>1</td>\n",
" <td>3555</td>\n",
" <td>3641</td>\n",
" <td>4933</td>\n",
" <td>6361</td>\n",
" <td>3390</td>\n",
" <td>3096</td>\n",
" <td>3498</td>\n",
" <td>3088</td>\n",
" <td>3095</td>\n",
" <td>3473</td>\n",
" <td>2810</td>\n",
" <td>2785</td>\n",
" <td>3875</td>\n",
" <td>4171</td>\n",
" <td>2365</td>\n",
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"</div>"
],
"text/plain": [
" CustomerID Gender Partner Dependents PhoneService MultipleLines InternetService OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV StreamingMovies Contract PaperlessBilling PaymentMethod Churn\n",
"count 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043 7043\n",
"unique 7043 2 2 2 2 3 3 3 3 3 3 3 3 3 2 4 2 \n",
"top 3090-QFUVD Male No No Yes No Fiber optic No No No No No No Month-to-month Yes Electronic check No \n",
"freq 1 3555 3641 4933 6361 3390 3096 3498 3088 3095 3473 2810 2785 3875 4171 2365 5174"
]
},
"execution_count": 447,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datset_churn.describe(include=['O'])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gender - ['Female' 'Male']\n",
"Partner - ['Yes' 'No']\n",
"Dependents - ['No' 'Yes']\n",
"PhoneService - ['No' 'Yes']\n",
"MultipleLines - ['No phone service' 'No' 'Yes']\n",
"InternetService - ['DSL' 'Fiber optic' 'No']\n",
"OnlineSecurity - ['No' 'Yes' 'No internet service']\n",
"OnlineBackup - ['Yes' 'No' 'No internet service']\n",
"DeviceProtection - ['No' 'Yes' 'No internet service']\n",
"TechSupport - ['No' 'Yes' 'No internet service']\n",
"StreamingTV - ['No' 'Yes' 'No internet service']\n",
"StreamingMovies - ['No' 'Yes' 'No internet service']\n",
"Contract - ['Month-to-month' 'One year' 'Two year']\n",
"PaperlessBilling - ['Yes' 'No']\n",
"PaymentMethod - ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
" 'Credit card (automatic)']\n",
"Churn - ['No' 'Yes']\n"
]
}
],
"source": [
"#Creating the list of columns\n",
"datset_churn_column = list(datset_churn.columns)\n",
"\n",
"#Removing numerical columns\n",
"datset_churn_column.remove('CustomerID')\n",
"datset_churn_column.remove('SeniorCitizen')\n",
"datset_churn_column.remove('Tenure')\n",
"datset_churn_column.remove('MonthlyCharges')\n",
"datset_churn_column.remove('TotalCharges')\n",
"\n",
"# Printing Unique values in each categorical column\n",
"for col in datset_churn_column:\n",
" print(col, \"-\", datset_churn[col].unique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observations :\n",
"1. Gender, Partner, Dependents, PhoneService, PaperlessBilling and Churn - They have 2 unique categories - Yes/No and for gender Male/Female\n",
"2. MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract - They have 3 unique categories\n",
"3. Payment Method - 4 unique categories or 4 methods by which customer pays for their service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Summary Statistics of Numeric variable."
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SeniorCitizen</th>\n",
" <th>Tenure</th>\n",
" <th>MonthlyCharges</th>\n",
" <th>TotalCharges</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>7043.000000</td>\n",
" <td>7043.000000</td>\n",
" <td>7043.000000</td>\n",
" <td>7032.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.162147</td>\n",
" <td>32.371149</td>\n",
" <td>64.761692</td>\n",
" <td>2283.300441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.368612</td>\n",
" <td>24.559481</td>\n",
" <td>30.090047</td>\n",
" <td>2266.771362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>18.250000</td>\n",
" <td>18.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>9.000000</td>\n",
" <td>35.500000</td>\n",
" <td>401.450000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>29.000000</td>\n",
" <td>70.350000</td>\n",
" <td>1397.475000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.000000</td>\n",
" <td>55.000000</td>\n",
" <td>89.850000</td>\n",
" <td>3794.737500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>72.000000</td>\n",
" <td>118.750000</td>\n",
" <td>8684.800000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SeniorCitizen Tenure MonthlyCharges TotalCharges\n",
"count 7043.000000 7043.000000 7043.000000 7032.000000 \n",
"mean 0.162147 32.371149 64.761692 2283.300441 \n",
"std 0.368612 24.559481 30.090047 2266.771362 \n",
"min 0.000000 0.000000 18.250000 18.800000 \n",
"25% 0.000000 9.000000 35.500000 401.450000 \n",
"50% 0.000000 29.000000 70.350000 1397.475000 \n",
"75% 0.000000 55.000000 89.850000 3794.737500 \n",
"max 1.000000 72.000000 118.750000 8684.800000 "
]
},
"execution_count": 448,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datset_churn.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observations:\n",
"1. We have 4 numeric variables\n",
"2. Tenure can vary from 0 months to 72 months. This is how long customer is with Telco\n",
"3. Total Charges = Monthly Charges * Tenure\n",
"4. Looking at the count column, all columns have count as 7043 . TotalCharges have count of 7032, a differece of 11 records. These are missing records"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assess missing values in dataset"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Assess missing values in dataset\n",
" Total Percent\n",
"TotalCharges 11 0.001562\n",
"Churn 0 0.000000\n",
"OnlineSecurity 0 0.000000\n",
"Gender 0 0.000000\n",
"SeniorCitizen 0 0.000000\n",
"Partner 0 0.000000\n",
"Dependents 0 0.000000\n",
"Tenure 0 0.000000\n",
"PhoneService 0 0.000000\n",
"MultipleLines 0 0.000000\n",
"InternetService 0 0.000000\n",
"OnlineBackup 0 0.000000\n",
"DeviceProtection 0 0.000000\n",
"TechSupport 0 0.000000\n",
"StreamingTV 0 0.000000\n",
"StreamingMovies 0 0.000000\n",
"Contract 0 0.000000\n",
"PaperlessBilling 0 0.000000\n",
"PaymentMethod 0 0.000000\n",
"MonthlyCharges 0 0.000000\n",
"CustomerID 0 0.000000\n"
]
}
],
"source": [
"print(\"Assess missing values in dataset\")\n",
"total = datset_churn.isnull().sum().sort_values(ascending=False)\n",
"percent = (datset_churn.isnull().sum()/datset_churn.isnull().count()).sort_values(ascending=False)\n",
"missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
"print(missing_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Observation - TotalCharges have 11 missing values. We will replace them with multiplying Tenure * MonthlyCharges value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Replacing missing value"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing Value Rows--> [488, 753, 936, 1082, 1340, 3331, 3826, 4380, 5218, 6670, 6754] \n",
"Missing data in rows--> 11\n",
"Assess missing values in dataset\n",
" Total Percent\n",
"Churn 0 0.0\n",
"OnlineSecurity 0 0.0\n",
"Gender 0 0.0\n",
"SeniorCitizen 0 0.0\n",
"Partner 0 0.0\n",
"Dependents 0 0.0\n",
"Tenure 0 0.0\n",
"PhoneService 0 0.0\n",
"MultipleLines 0 0.0\n",
"InternetService 0 0.0\n",
"OnlineBackup 0 0.0\n",
"TotalCharges 0 0.0\n",
"DeviceProtection 0 0.0\n",
"TechSupport 0 0.0\n",
"StreamingTV 0 0.0\n",
"StreamingMovies 0 0.0\n",
"Contract 0 0.0\n",
"PaperlessBilling 0 0.0\n",
"PaymentMethod 0 0.0\n",
"MonthlyCharges 0 0.0\n",
"CustomerID 0 0.0\n"
]
}
],
"source": [
"#Identifying the rows containing missing data\n",
"missing_value_row = list(datset_churn[datset_churn['TotalCharges'].isnull()].index)\n",
"print('Missing Value Rows-->', missing_value_row , '\\nMissing data in rows-->', len(missing_value_row))\n",
"\n",
"#Replacing missing data with Tenure X MonthlyCharges\n",
"for missing_row in missing_value_row :\n",
" datset_churn['TotalCharges'][missing_row] = datset_churn['Tenure'][missing_row] * datset_churn['MonthlyCharges'][missing_row]\n",
"\n",
"# Displaying Missing Value statistics\n",
"print(\"Assess missing values in dataset\")\n",
"total = datset_churn.isnull().sum().sort_values(ascending=False)\n",
"percent = (datset_churn.isnull().sum()/datset_churn.isnull().count()).sort_values(ascending=False)\n",
"missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
"print(missing_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Missing values in MonthlyCharges are gone!!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Univariate Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vizualizing the Categorical variables with bar chart"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# Getting the list of all columns\n",
"columns_hist = list(datset_churn.columns)\n",
"\n",
"#Removing the Numerical Variables\n",
"columns_hist.remove('CustomerID')\n",
"columns_hist.remove('SeniorCitizen')\n",
"columns_hist.remove('Tenure')\n",
"columns_hist.remove('MonthlyCharges')\n",
"columns_hist.remove('TotalCharges')\n",
"\n",
"#Creating Column into 4X4 matrix to display 16 bar charts in 4X4 form:\n",
"columns_hist_nparray = np.array(columns_hist)\n",
"columns_hist_nparray = np.reshape(columns_hist_nparray, (4,4)) # reshaping the columns into 4X4 matrix"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Univariate Analysis of each categorical Variables\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x1440 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting the bar chart\n",
"rows = 4 ; columns = 4\n",
"f, axes = plt.subplots(rows, columns, figsize=(20, 20))\n",
"print('Univariate Analysis of each categorical Variables')\n",
"for row in range(rows):\n",
" for column in range(columns):\n",
" sns.countplot(datset_churn[columns_hist_nparray[row][column]], palette = \"Set1\", ax = axes[row, column])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observations :\n",
"1. We have almost equal genders in our dataset\n",
"2. Almost 50% have partners\n",
"3. Around 30% have dependants\n",
"4. 85% of the customers have phone service\n",
"5. Around 40% customers have multiple lines\n",
"6. People prefer Fiber Optics over DSL for Internet\n",
"7. Around 30% have taken online security.Majority of Customer don't have Online security or backup\n",
"8. Close 35% prefer device protection\n",
"9. Majority of Customer don't have Tech Support\n",
"10. Around 37% have registered for Streaming TV & MOvie\n",
"11. Contract - Majority of customers are subscribed for Month to Month contract (55%)\n",
"12. Majority of customers have opted Paperless billing\n",
"13. Majority of customers pay eletronic check. 43 % prefer Automatic payment (Bank Transfer and Credit Card)\n",
"14. Target Variable - \"Churn\" - We have unbalanced distribution (Yes - Approx 1800 ; No - Approx 5000). So Churn positive is 25% (Approx)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vizualizing the Numeric variables\n",
"#### Histogram to see data distribution of Quantitative Variables(SeniorCitizen, tenure, MonthlyCharges, TotalCharges)\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x23a7e04fa90>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(2, 3, figsize=(20,10))\n",
"#Charting the histogram\n",
"datset_churn[\"Tenure\"].plot.hist(color='DarkBlue', alpha=0.7, bins=50, title='Tenure',ax=axes[0, 0])\n",
"datset_churn[\"MonthlyCharges\"].plot.hist(color='DarkBlue', alpha=0.7, bins=50, title='MonthlyCharges',ax=axes[0, 1])\n",
"datset_churn[\"TotalCharges\"].plot.hist(color='DarkBlue', alpha=0.7, bins=50, title='TotalCharges',ax=axes[0, 2])\n",
"\n",
"#Charting the density plot\n",
"sns.distplot( datset_churn[\"Tenure\"] , kde=True, rug=False, color=\"skyblue\", ax=axes[1, 0])\n",
"sns.distplot( datset_churn[\"MonthlyCharges\"] , kde=True, rug=False, color=\"olive\", ax=axes[1, 1])\n",
"sns.distplot( datset_churn[\"TotalCharges\"] , kde=True, rug=False, color=\"gold\", ax=axes[1, 2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observations:\n",
"1. Tenure:\n",
"\n",
" 1.1. Not a normal distribution. Bi-Modal distribution (having 2 peaks) which means data is concentrated across two different groups\n",
" \n",
" 1.2 We have major chunk of customers in 0-1 month period. Lot of them might be cutomers who tried the service and left or liked the service and continued\n",
" \n",
" 1.3. Between 10 months to 65 months, we can see flat distribution of data.\n",
" \n",
" 1.4. We have lot customers in 69-72 months range. They are the loyal customers\n",
" \n",
" \n",
" 2. Monthly Charges - \n",
" \n",
" 2.1. Not a normal distribution.Close to Bi-Modal distribution\n",
" \n",
" 2.2. Majority of customers are paying $18 to $20 dollars. Must be the service charge for basic service. Majority of customers are subscribed to basic package.\n",
" \n",
" 2.3. Between $70-$100 dollars, we have quite a number of customers. They might be the ones subscribed for multiple services.\n",
" \n",
" \n",
" 3. Total Charges - \n",
" \n",
" 3.1. Data is positively skewed.\n",
" \n",
" 3.2. Majority of the population have spent close to $1,100 dollars\n",
" \n",
" 3.3. Cutomers have spent upto $9,000 dollars \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Barchart for the Gender (0 is No , 1 is Yes)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x23a7d8fb630>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(datset_churn['SeniorCitizen'], palette = \"Set1\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"16% of customers are senior citizens"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Let's Check for Outliers using Box Plot for Tenure, MonthlyCharges, TotalCharges"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x23a7e316400>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(1, 3, figsize=(15,5))\n",
"sns.boxplot(x=datset_churn_copy[\"tenure\"], orient=\"v\", color=\"olive\",ax=axes[0])\n",
"sns.boxplot(x=datset_churn_copy[\"MonthlyCharges\"], orient=\"v\", color=\"gold\",ax=axes[1])\n",
"sns.boxplot(x=datset_churn_copy[\"TotalCharges\"] , orient=\"v\", color=\"skyblue\",ax=axes[2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observation - Seems like we dont have outliers !"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bivariate Analysis\n",
"Correlating the features with Target column (Churn)\n",
"Let us start by understanding correlations between numerical features and our solution goal (Churn).\n",
"\n",
"A histogram chart is useful for analyzing continous numerical variables like tenure , Monthly Charges and Total Charges where banding or ranges will help identify useful patterns. The histogram can indicate distribution of samples using automatically defined bins or equally ranged bands. This helps us answer questions relating to specific bands."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Converting the categorical variable to numerical variable\n",
"datset_churn['Churn_Num'] = datset_churn['Churn'].map( {'Yes': 1, 'No': 0} ).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Churn</th>\n",
" <th>Churn_Num</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Churn Churn_Num\n",
"0 No 0\n",
"1 No 0\n",
"2 Yes 1\n",
"3 No 0\n",
"4 Yes 1"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Validating the mappaing\n",
"datset_churn[['Churn','Churn_Num']].head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x23a7d68dba8>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting Tenure Column with Churn\n",
"# Churn_num indicates customer who left the company. 0 indicates customer who stayed.\n",
"fighist = sns.FacetGrid(datset_churn, col='Churn_Num')\n",
"fighist.map(plt.hist, 'Tenure', bins=20) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observations:\n",
"\n",
"1. Customer who left the Telco are mostly customers within 1st month (600+) and churn steady declines with time.\n",
"2. If customer can be retained between 10-20 months, there are high chances, customer will stay very long. Churn decreases over time\n",
"3. Customer at 72 month tenure, mostly stayed (Churn=0).\n",
"\n",
"#### Decisions:\n",
"\n",
"1. We should definitely use 'Tenure' column in our model training.\n",
"2. We should band 'Tenure'"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x23a7e874550>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting MonthlyCharges Column with Churn\n",
"# Churn_num indicates customer who left the company. 0 indicates customer who stayed.\n",
"fighist = sns.FacetGrid(datset_churn, col='Churn_Num')\n",
"fighist.map(plt.hist, 'MonthlyCharges', bins=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observation :\n",
"1. Majority of customers are in 18 to 20 range and they didn't leave\n",
"2. Customer Leaving are mostly in the bannd of 75-100 who have opted for multiple services.\n",
"\n",
"#### Decisions :\n",
"1. We will use 'MonthlyCharges' column in our model training.\n",
"2. We should band 'MonthlyCharges'"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x23a7e5565f8>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting TotalCharges Column with Churn\n",
"# Churn_num indicates customer who left the company. 0 indicates customer who stayed.\n",
"fighist = sns.FacetGrid(datset_churn, col='Churn_Num')\n",
"fighist.map(plt.hist, 'TotalCharges', bins=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observation:\n",
"It's difficult to conclude anything using this column. Total charges are Tenure * MonthlyCharges . Tenur might me high and Monthly charges may be low and vice-versa. Data is positively skewed.\n",
"\n",
"#### Decision\n",
"We will not use this column\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now we will use the Categorical variables and their relationships with Churn\n",
"\n",
"#### We will use Seaborn Categorical Plot"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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J+iTrN23aNK/FScuJfUmau4UO0++207e0n3e09o3AXkPrrQJu20r7o1TVaVW1pqrWrFy5ct4Ll5YL+5I0dwsdphcDMyNy1wEXDbW/uo3qPRS4t50G/gxweJLd2sCjw1ubJEmLxopxbTjJXwPPB/ZIspHBqNx3AecnOQ74DnB0W/1TwEuADcC/Aa8FqKq7krwTuKqt946q2nxQkyRJEzW2MK2qV25h0QtmWbeAE7awnTOBM+exNEmS5tViGYAkSdLUMkwlSepkmEqS1MkwlSSpk2EqSVInw1SSpE6GqSRJnQxTSZI6GaaSJHUyTCVJ6mSYSpLUyTCVJKmTYSpJUifDVJKkToapJEmdDFNJkjoZppIkdVox6QIm6Wd/7+xJlzB1vnrKqyddgiQtOh6ZSpLUyTCVJKmTYSpJUifDVJKkToapJEmdDFNJkjoZppIkdTJMJUnqZJhKktTJMJUkqZNhKklSJ8NUkqROhqkkSZ0MU0mSOi3rW7BJmh/eznDuvJ3h0uKRqSRJnQxTSZI6GaaSJHUyTCVJ6jQ1YZpkbZKbkmxI8uZJ1yNJ0oypCNMkOwIfAF4M7A+8Msn+k61KkqSBqQhT4BBgQ1V9q6r+HTgXOGLCNUmSBECqatI1bFOSo4C1VfUbbf5VwHOq6nVD6xwPHN9mnwHctOCFzq89gDsnXcQyNu3//ndW1drteeIS60vT/ndcCqb9bzBSX5qWizZklrZHvAuoqtOA0xamnPFLsr6q1ky6juVqOf/7L6W+tJz/jovFcvkbTMtp3o3AXkPzq4DbJlSLJEmPMC1hehWwOsm+SXYCjgUunnBNkiQBU3Kat6ruT/I64DPAjsCZVXX9hMsatyVxmm2K+e+/NPh3nLxl8TeYigFIkiQtZtNymleSpEXLMJUkqZNhOmFJKsl7hubfmORtEyxpycvAF5O8eKjtmCR/O8m6tP3sR5NhX3qYYTp59wEvS7LHpAtZLmowUOC/AX+WZJckPwacDJww2crUwX40Afalhxmmk3c/g9Fu/2PzBUl+IsllSa5pP/de+PKWpqq6Dvgb4E3AScDZVfXNJOuSXJnk6iQfTLJDkhVJzklybZLrkpw42eo1C/vRhNiXBqbiqzHLwAeAa5L8yWbtf8HgP+ZZSX4deB9w5IJXt3S9Hfga8O/AmiQ/DfwK8HPt61inMfhO8zeBParqmQBJdp1Uwdoq+9HkLPu+ZJguAlX1vSRnAycCPxha9J+Al7Xpc4DNXyTUoaq+n+Q84F+r6r4kLwSeDaxPAvBY4FYG329+RpL3Ap8CLplUzdoy+9Hk2JcM08Xkzxm8s/vwVtbxS8Hz78H2gME1oM+sqj/afKUkBzK4BeCJwMt5+ELwWlzsR5OzrPuSn5kuElV1F3A+cNxQ8z8wODUC8KvAFxe6rmXm74BjZgaxJHlSkr2TrGRwgZOPMfhM6OBJFqktsx8tGsuuL3lkuri8B3jd0PyJwJlJfg/YBLx2IlUtE1V1bZK3A3+XZAfgRwxGKj4AnJHB+apiMNBCi5f9aMKWY1/ycoKSJHXyNK8kSZ0MU0mSOhmmkiR1MkwlSepkmEqS1MkwXYKSPNCuh3ldko8leVySfZJcN+b97pDkfW2/1ya5Ksm+87Tt05PsPx/bkkZlX9Ko/J7p0vSDqjoIIMlHGXy/68IF2O8rgKcBB1bVg0lWAd8f9clJdqyqB2ZbVlW/MU81SnNhX9JIPDJd+r4A7Nemd0zyoSTXJ7kkyWMBkhyU5CvtrhqfSLJba788ybvbnR++keQ/t/Ydk5zS3i1fk+S32vafCtxeVQ8CVNXGqrq7PefwJF9O8rX2Dv/xrf2WJG9N8kXg95NcOVN4OwK4ZqiWNW16bdvOPya5rLX9WJIzW01fT3LEWP9VtRzZl7RFhukSlmQFg2tgXtuaVgMfqKoDgHsYXBcT4GzgTVV1YFv3pKHNrKiqQ4A3DLUfB9xbVc9mcDHr32ynoM4HfrmdFntPkme1OvYA/hB4YVUdDKwHfndoHz+squdV1f8GdkryH1v7K9o2h3+nlcCHgJdX1c8AR7dF/wv4bKvpF4BTMri3otTNvmRf2hZP8y5Nj01ydZv+AnAGg1NG366qmfavAvskeSKwa1V9vrWfBXxsaFsXDq/fpg8HDkxyVJt/IrC6qi5J8gzgsPa4LMnRDO4YsT/wpQzuILET8OWhfZw3NH0+cAzwLgYvAK/Y7Hc7FPj7qvo2PHQt1pmaXprkjW1+F2Bv4MZZ/4Wk0diX7EsjMUyXpoc+55nROt59Q00PMOiY2zLznAd4+P9LgNdX1Wc2X7mq7gM+DXw6yXcZ3DfyEuDSqnrlFvYx/FnQecDHklw42FzdvNm6M9f03FwYvMO+aYTfSRqVfUkj8TTvMldV9wJ3z3yGA7wK+PxWngKDexL+dpLHACR5evuc5eAkT2ttOwAHAv8MfAV4bpL92rLHJXn6Fur5JoMXmz/ike+yZ3wZ+Pl2Kowkuw/V9Pq0V7qZ02LSQrEvLW8emQpgHfBXSR4HfItt31XjdAanqb7WOtwmBu+anwx8KMnObb0rgb+oqh8meQ3w10PL/hD4xha2fx5wCvCorwJU1aYkxwMXtheZO4BfBN7J4F6W17SabgF+aRu/hzTf7EvLlHeNkSSpk6d5JUnqZJhKktTJMJUkqZNhKklSJ8NUkqROhqkkSZ0MU0mSOv1/oqkuMcPLYk4AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"col_list = columns_hist\n",
"col_list.remove('Churn')\n",
"for col in col_list:\n",
" if col == 'PaymentMethod':\n",
" aspect_ratio = 2.0\n",
" else:\n",
" aspect_ratio = 0.8\n",
" \n",
" plot_cat_data = sns.catplot(x=col, col='Churn_Num', data = datset_churn, kind='count', height=4, aspect=aspect_ratio)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observations (Churn_Num - 1 is \"Yes\" ; 0 is \"No\") :\n",
"1. 'Gender' : Difficult to determine Churn using this field. Counts are almost same in either category\n",
"2. 'Partner' : Customer with partner have lower chance of leaving\n",
"3. 'Dependents' :Customer with dependants have lower chance of leaving. We will merge Partner & Dependant Columns as 1 column\n",
"4. 'PhoneService' & 'MultipleLines' : We will merge these columns into PhoneLines - Single & Multiple and determine\n",
"5. 'InternetService' : Customer with Fiber Optic Interner Service have higher chances of leaving\n",
"6. 'OnlineSecurity' & 'OnlineBackup' : We will merge these columns for better visibility\n",
"7. 'DeviceProtection' : Customers without device protection have likely higher chances of leaving\n",
"8. 'TechSupport' - Customer not opting for TechSupport have higher chances of leaving \n",
"9. 'StreamingTV', 'StreamingMovies' - We will merge these columns into streaming and check again\n",
"10. 'Contract' - Month to Month customers have likely higher chances to leave\n",
"11. 'PaperlessBilling' - Customers with paperless billing have higher chances of leaving\n",
"12. 'PaymentMethod' - People paying with electronic check have higher chances of leaving"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating new feature from existing set of columns using the above observations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating bands for numerical variables - Tenure & Monthly Charges"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tenure</th>\n",
" <th>TenureRange</th>\n",
" <th>TenureCat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>(-0.072, 14.4]</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>34</td>\n",
" <td>(28.8, 43.2]</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>(-0.072, 14.4]</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>45</td>\n",
" <td>(43.2, 57.6]</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>(-0.072, 14.4]</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>8</td>\n",
" <td>(-0.072, 14.4]</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>22</td>\n",
" <td>(14.4, 28.8]</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10</td>\n",
" <td>(-0.072, 14.4]</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>28</td>\n",
" <td>(14.4, 28.8]</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>62</td>\n",
" <td>(57.6, 72.0]</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tenure TenureRange TenureCat\n",
"0 1 (-0.072, 14.4] 0.0\n",
"1 34 (28.8, 43.2] 3.0\n",
"2 2 (-0.072, 14.4] 0.0\n",
"3 45 (43.2, 57.6] 3.0\n",
"4 2 (-0.072, 14.4] 0.0\n",
"5 8 (-0.072, 14.4] 0.0\n",
"6 22 (14.4, 28.8] 2.0\n",
"7 10 (-0.072, 14.4] 1.0\n",
"8 28 (14.4, 28.8] 2.0\n",
"9 62 (57.6, 72.0] 5.0"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating tenure band and co-relation with Churn\n",
"datset_churn['TenureRange'] = pd.cut(datset_churn['Tenure'], 5)\n",
"datset_churn[['TenureRange', 'Churn_Num']].groupby(['TenureRange'], as_index=False).mean().sort_values(by='TenureRange', ascending=True)\n",
"\n",
"# Replacing Age band with ordinals based on these bands\n",
"datset_churn.loc[ datset_churn['Tenure'] <= 8, 'TenureCat'] = 0\n",
"datset_churn.loc[(datset_churn['Tenure'] > 8) & (datset_churn['Tenure'] <= 15), 'TenureCat'] = 1\n",
"datset_churn.loc[(datset_churn['Tenure'] > 15) & (datset_churn['Tenure'] <= 30), 'TenureCat'] = 2\n",
"datset_churn.loc[(datset_churn['Tenure'] > 30) & (datset_churn['Tenure'] <= 45 ), 'TenureCat'] = 3\n",
"datset_churn.loc[(datset_churn['Tenure'] > 45) & (datset_churn['Tenure'] <= 60 ), 'TenureCat'] = 4\n",
"datset_churn.loc[ datset_churn['Tenure'] > 60, 'TenureCat'] = 5\n",
"\n",
"datset_churn[['Tenure','TenureRange','TenureCat']].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MonthlyCharges</th>\n",
" <th>MonthlyChargesRange</th>\n",
" <th>MonthlyChargesCat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>29.85</td>\n",
" <td>(18.15, 38.35]</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>56.95</td>\n",
" <td>(38.35, 58.45]</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>53.85</td>\n",
" <td>(38.35, 58.45]</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>42.30</td>\n",
" <td>(38.35, 58.45]</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>70.70</td>\n",
" <td>(58.45, 78.55]</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>99.65</td>\n",
" <td>(98.65, 118.75]</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>89.10</td>\n",
" <td>(78.55, 98.65]</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>29.75</td>\n",
" <td>(18.15, 38.35]</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>104.80</td>\n",
" <td>(98.65, 118.75]</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>56.15</td>\n",
" <td>(38.35, 58.45]</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MonthlyCharges MonthlyChargesRange MonthlyChargesCat\n",
"0 29.85 (18.15, 38.35] 1.0\n",
"1 56.95 (38.35, 58.45] 2.0\n",
"2 53.85 (38.35, 58.45] 2.0\n",
"3 42.30 (38.35, 58.45] 2.0\n",
"4 70.70 (58.45, 78.55] 3.0\n",
"5 99.65 (98.65, 118.75] 4.0\n",
"6 89.10 (78.55, 98.65] 4.0\n",
"7 29.75 (18.15, 38.35] 1.0\n",
"8 104.80 (98.65, 118.75] 5.0\n",
"9 56.15 (38.35, 58.45] 2.0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Creating MonthlyCharges Band and co-relation with Churn\n",
"datset_churn['MonthlyChargesRange'] = pd.cut(datset_churn['MonthlyCharges'], 5)\n",
"datset_churn[['MonthlyChargesRange', 'Churn_Num']].groupby(['MonthlyChargesRange'], as_index=False).mean().sort_values(by='MonthlyChargesRange', ascending=True)\n",
"\n",
"# Replacing Age band with ordinals based on these bands\n",
"datset_churn.loc[ datset_churn['MonthlyCharges'] <= 20, 'MonthlyChargesCat'] = 0\n",
"datset_churn.loc[(datset_churn['MonthlyCharges'] > 20) & (datset_churn['MonthlyCharges'] <= 40), 'MonthlyChargesCat'] = 1\n",
"datset_churn.loc[(datset_churn['MonthlyCharges'] > 40) & (datset_churn['MonthlyCharges'] <= 60), 'MonthlyChargesCat'] = 2\n",
"datset_churn.loc[(datset_churn['MonthlyCharges'] > 60) & (datset_churn['MonthlyCharges'] <= 80 ), 'MonthlyChargesCat'] = 3\n",
"datset_churn.loc[(datset_churn['MonthlyCharges'] > 80) & (datset_churn['MonthlyCharges'] <= 100 ), 'MonthlyChargesCat'] = 4\n",
"datset_churn.loc[ datset_churn['MonthlyCharges'] > 100, 'MonthlyChargesCat'] = 5\n",
"\n",
"#Checking the categories\n",
"datset_churn[['MonthlyCharges','MonthlyChargesRange','MonthlyChargesCat']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating new derived columns for Categorical variables"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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1MkwlSWo08P1MJUmL0/fe/cJRtzB2nvMnG+d1fe6ZSpLUyDCVJKmRYSpJUiPDVJKkRoapJEmNhhamSdYleTDJHX21fZPckOSe7nmfrp4kH06yOcntSY7sW2ZNN/89SdYMq19JkuZqmHumnwRW71A7B7ixqlYCN3bTACcAK7vHWuAi6IUvcC7wUuAo4NypAJYkabEYWphW1d8CD+9QPgm4rHt9GXByX/3y6vk6sHeSA4DjgRuq6uGqegS4gacGtCRJI7XQn5nuX1X3A3TPz+7qK4D7+uab7Go7qz9FkrVJNiTZsHXr1nlvXFouHEvS7C2WE5AyTa1mqD+1WHVxVa2qqlUTExPz2py0nDiWpNlb6DB9oDt8S/f8YFefBA7qm+9AYMsMdUmSFo2FDtP1wNQZuWuAa/vqp3dn9R4NfL87DHw9cFySfboTj47rapIkLRpDu9B9kr8EjgH2SzJJ76zc9wJXJTkT+B5wajf7dcCJwGbgx8AZAFX1cJL3ALd08727qnY8qUmSpJEaWphW1et38tarppm3gLN2sp51wLp5bE2SpHm1WE5AkiRpbBmmkiQ1MkwlSWpkmEqS1MgwlSSpkWEqSVIjw1SSpEaGqSRJjQxTSZIaGaaSJDUyTCVJamSYSpLUaGgXupe0fLzk7ZePuoWxc+v7Tx91C5pH7plKktTIMJUkqZFhKklSI8NUkqRGhqkkSY0MU0mSGhmmkiQ1MkwlSWpkmEqS1MgwlSSpkWEqSVIjw1SSpEaGqSRJjUYSpknuTbIxyW1JNnS1fZPckOSe7nmfrp4kH06yOcntSY4cRc+SJO3MKPdMX1FVR1TVqm76HODGqloJ3NhNA5wArOwea4GLFrxTSZJmsJgO854EXNa9vgw4ua9+efV8Hdg7yQGjaFCSpOmMKkwL+EKSW5Os7Wr7V9X9AN3zs7v6CuC+vmUnu9qTJFmbZEOSDVu3bh1i69LS5liSZm9UYfryqjqS3iHcs5L8mxnmzTS1ekqh6uKqWlVVqyYmJuarT2nZcSxJszeSMK2qLd3zg8A1wFHAA1OHb7vnB7vZJ4GD+hY/ENiycN1KkjSzBQ/TJD+X5FlTr4HjgDuA9cCabrY1wLXd6/XA6d1ZvUcD3586HCxJ0mKwxwi2uT9wTZKp7f+Pqvp8kluAq5KcCXwPOLWb/zrgRGAz8GPgjIVvWZKknVvwMK2q7wAvmqb+T8CrpqkXcNYCtCZJ0pwspj+NkSRpLBmmkiQ1MkwlSWpkmEqS1MgwlSSpkWEqSVIjw1SSpEaGqSRJjQxTSZIaGaaSJDUyTCVJamSYSpLUyDCVJKmRYSpJUiPDVJKkRoapJEmNDFNJkhoZppIkNTJMJUlqZJhKktTIMJUkqZFhKklSI8NUkqRGhqkkSY0MU0mSGhmmkiQ1GpswTbI6yd1JNic5Z9T9SJI0ZSzCNMnuwMeAE4DDgNcnOWy0XUmS1DMWYQocBWyuqu9U1b8AVwInjbgnSZIASFWNuoddSnIKsLqq/kM3/UbgpVV1dt88a4G13eTzgbsXvNH5tR/w0KibWMbG/d//oapaPZcFl9hYGvfv41Iw7t+DgcbSHgvRyTzINLUn/RZQVRcDFy9MO8OXZENVrRp1H8vVcv73X0pjaTl/HxeL5fI9GJfDvJPAQX3TBwJbRtSLJElPMi5heguwMskhSfYETgPWj7gnSZKAMTnMW1Xbk5wNXA/sDqyrqk0jbmvYlsRhtjHmv//S4Pdx9JbF92AsTkCSJGkxG5fDvJIkLVqGqSRJjQzTEUjPTUlO6Ku9LsnnR9nXcpSkklzQN/22JOeNsCXNgmNp8VjuY8kwHYHqfVD9+8AHkzw9yc8B5wNnjbazZWkb8Nok+426Ec2eY2lRWdZjyTAdkaq6A/hr4I+Bc4HLq+rbSdYkuTnJbUk+nmS3JHskuSLJxiR3JHnzaLtfUrbTO9vwD3d8I8mvJLkxye3d83MWvj3timNp0VjWY2ks/jRmCXsX8E3gX4BVSX4VeA3wsu7PgS6m9ze13wb2q6oXAiTZe1QNL1EfA25P8qc71D9K7wfzZUl+D/gwcPKCd6dBOJYWh2U7lgzTEaqqHyX5NPDPVbUtyauBXwc2JAF4BnAfvb+vfX6SDwHXAV8YVc9LUVX9IMnlwJuBn/S99a+B13avrwB2/AGhRcKxtDgs57FkmI7e490DetcgXldV/23HmZL8Gr1b0L0Z+C2euBC55sef0duzuXSGefyj7MXNsbQ4LMux5Gemi8sXgddNfYCf5BeTPCfJBL0LbPwVvc+Ejhxlk0tRVT0MXAWc2Vf+Gr1DgwC/A9y00H1pzhxLI7Jcx5J7potIVW1M8i7gi0l2A35G70zFx4BL0jteVfROtND8uwA4u2/6zcC6JG8HtgJnjKQrzZpjaeSW3VjycoKSJDXyMK8kSY0MU0mSGhmmkiQ1MkwlSWpkmEqS1Mgw1bSSPNZd03TqcfA8rPP3k5zevf5kklNa1yktdo6l5cG/M9XO/KSqjpjPFVbVn8/n+qQx4VhaBtwz1cCSHJzkq0m+2T1e1tWPSfI3Sa5K8g9J3pvkd7o7dmxM8txuvvOSvG2Hdb4qyTV908cm+ezCfmXSwnIsLT2GqXbmGX2HpaYG6IPAsVV1JPDv6d35YcqLgLcALwTeCBxaVUcBnwD+YIbtfAl4QXeZN+hdGWWma3pK48axtAx4mFc7M92hqacBH01yBL3Lsh3a994tVXU/QJJv88TdODYCr9jZRqqqklwBvCHJpfTuLnH6PH0N0mLgWFoGDFPNxh8CD9D7zXk34Kd9723re/143/Tj7Pr/2aX0bu78U+Cvqmr7vHQrLV6OpSXGMNVs/AIwWVWPJ1kD7D4fK62qLUm2AP8VOHY+1iktco6lJcbPTDUbHwfWJPk6vcNSP5rHdX8KuK+q7pzHdUqLlWNpifGuMVoUknwU+FZVXTLqXqRx5lgaDcNUI5fkVnq/mR9bVdt2Nb+k6TmWRscwlSSpkZ+ZSpLUyDCVJKmRYSpJUiPDVJKkRoapJEmN/h8VVDwCjmF7SAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 460.8x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Creating a new column for family. If a customer has dependant or Partner, I am considering it as family .\n",
"list_family = []\n",
"for rows in range(len(datset_churn['Partner'])):\n",
" if ((datset_churn['Partner'][rows] == 'No') and (datset_churn['Dependents'][rows] == 'No')):\n",
" list_family.append('No')\n",
" else:\n",
" list_family.append('Yes')\n",
"datset_churn['Family'] = list_family\n",
"#print(datset_churn[['Partner', 'Dependents', 'Family' ]].head(10))\n",
"\n",
"#Creating a new column for Online Services (Online Security & Online Backup) . If a customer has Online Security or Online Backup services\n",
"#then , I am considering it as \"Yes\" else \"No\"\n",
"list_online_services = []\n",
"for rows_os in range(len(datset_churn['OnlineSecurity'])):\n",
" if ((datset_churn['OnlineSecurity'][rows_os] == 'No') and (datset_churn['OnlineBackup'][rows_os] == 'No')):\n",
" list_online_services.append('No')\n",
" else:\n",
" list_online_services.append('Yes')\n",
"datset_churn['OnlineServices'] = list_online_services\n",
"\n",
"#print(datset_churn[['OnlineSecurity', 'OnlineBackup', 'OnlineServices' ]].head(10))\n",
" \n",
"#Creating a new column for Streaming Services (StreamingTV & StreamingMovies) . If a customer has StreamingTV or StreamingMovies\n",
"#then , I am considering it as \"Yes\" else \"No\"\n",
"list_streaming_services = []\n",
"for rows_stv in range(len(datset_churn['StreamingTV'])):\n",
" if ((datset_churn['StreamingTV'][rows_stv] == 'No') and (datset_churn['StreamingMovies'][rows_stv] == 'No')):\n",
" list_streaming_services.append('No')\n",
" else:\n",
" list_streaming_services.append('Yes')\n",
"datset_churn['StreamingServices'] = list_streaming_services\n",
"\n",
"#print(datset_churn[['StreamingTV', 'StreamingMovies', 'StreamingServices' ]].head(10))\n",
"\n",
"plot_cat_data = sns.catplot(x='Family', col='Churn_Num', data = datset_churn, kind='count', height=4, aspect=0.8)\n",
"plot_cat_data = sns.catplot(x='OnlineServices', col='Churn_Num', data = datset_churn, kind='count', height=4, aspect=0.8)\n",
"plot_cat_data = sns.catplot(x='StreamingServices', col='Churn_Num', data = datset_churn, kind='count', height=4, aspect=0.8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observation\n",
" - Customers with family are less likely to Churn\n",
" - Customers not opted for online services (online backup or security) have slightly higher chances of churn\n",
" - Customer opted for Streaming Services seems to have slightly higher chances of churn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparing Columns for Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Converting the Object/Categorical Variable to Numerical Variable"
]
},
{
"cell_type": "code",
"execution_count": 464,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7043 entries, 0 to 7042\n",
"Data columns (total 29 columns):\n",
"CustomerID 7043 non-null object\n",
"Gender 7043 non-null object\n",
"SeniorCitizen 7043 non-null int64\n",
"Partner 7043 non-null object\n",
"Dependents 7043 non-null object\n",
"Tenure 7043 non-null int64\n",
"PhoneService 7043 non-null object\n",
"MultipleLines 7043 non-null object\n",
"InternetService 7043 non-null object\n",
"OnlineSecurity 7043 non-null object\n",
"OnlineBackup 7043 non-null object\n",
"DeviceProtection 7043 non-null object\n",
"TechSupport 7043 non-null object\n",
"StreamingTV 7043 non-null object\n",
"StreamingMovies 7043 non-null object\n",
"Contract 7043 non-null object\n",
"PaperlessBilling 7043 non-null object\n",
"PaymentMethod 7043 non-null object\n",
"MonthlyCharges 7043 non-null float64\n",
"TotalCharges 7043 non-null float64\n",
"Churn 7043 non-null object\n",
"Churn_Num 7043 non-null int32\n",
"TenureRange 7043 non-null category\n",
"TenureCat 7043 non-null float64\n",
"MonthlyChargesRange 7043 non-null category\n",
"MonthlyChargesCat 7043 non-null float64\n",
"Family 7043 non-null object\n",
"OnlineServices 7043 non-null object\n",
"StreamingServices 7043 non-null object\n",
"dtypes: category(2), float64(4), int32(1), int64(2), object(20)\n",
"memory usage: 1.4+ MB\n"
]
}
],
"source": [
"datset_churn.info()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gender</th>\n",
" <th>Gender_Num</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Female</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Gender Gender_Num\n",
"0 Female 1\n",
"1 Male 0"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Converting Gender column to numeric value\n",
"#datset_churn['Gender'].unique() # Print unique values in the column\n",
"datset_churn['Gender_Num'] = datset_churn['Gender'].map( {'Female': 1, 'Male': 0} ).astype(int) #Map Categorical to Numerical Values\n",
"datset_churn[['Gender','Gender_Num']].head(2) # Test the mapping"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Family</th>\n",
" <th>Family_Num</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Family Family_Num\n",
"0 Yes 1\n",
"1 No 0"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# For Partner & Dependant , we created Family Column . Converting Family column to numeric value\n",
"#datset_churn['Family'].unique() # Print unique values in the column\n",
"datset_churn['Family_Num'] = datset_churn['Family'].map( {'Yes': 1, 'No': 0} ).astype(int) #Map Categorical to Numerical Values\n",
"datset_churn[['Family','Family_Num']].head(2) # Test the mapping"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"datset_churn['PhoneService_Num'] = datset_churn['PhoneService'].map( {'Yes': 1, 'No': 0} ).astype(int)\n",
"datset_churn['MultipleLines_Num'] = datset_churn['MultipleLines'].map( {'No': 0, 'Yes': 1, 'No phone service':2} ).astype(int)\n",
"datset_churn['InternetService_Num'] = datset_churn['InternetService'].map( {'DSL': 0, 'Fiber optic': 1, 'No':2} ).astype(int)\n",
"datset_churn['OnlineServices_Num'] = datset_churn['OnlineServices'].map( {'Yes': 1, 'No': 0} ).astype(int)\n",
"\n",
"datset_churn['DeviceProtection_Num'] = datset_churn['DeviceProtection'].map( {'No': 0, 'Yes': 1, 'No internet service':2} ).astype(int)\n",
"datset_churn['StreamingServices_Num'] = datset_churn['StreamingServices'].map( {'Yes': 1, 'No': 0} ).astype(int)\n",
"datset_churn['TechSupport_Num'] = datset_churn['TechSupport'].map( {'No': 0, 'Yes': 1, 'No internet service':2} ).astype(int)\n",
"datset_churn['Contract_Num'] = datset_churn['Contract'].map( {'Month-to-month': 0, 'One year': 1, 'Two year': 2} ).astype(int)\n",
"datset_churn['PaperlessBilling_Num'] = datset_churn['PaperlessBilling'].map( {'Yes': 1, 'No': 0} ).astype(int)\n",
"datset_churn['PaymentMethod_Num'] = datset_churn['PaymentMethod'].map( {'Electronic check': 0, 'Mailed check': 1, 'Bank transfer (automatic)': 2 , 'Credit card (automatic)' : 3} ).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7043 entries, 0 to 7042\n",
"Data columns (total 41 columns):\n",
"CustomerID 7043 non-null object\n",
"Gender 7043 non-null object\n",
"SeniorCitizen 7043 non-null int64\n",
"Partner 7043 non-null object\n",
"Dependents 7043 non-null object\n",
"Tenure 7043 non-null int64\n",
"PhoneService 7043 non-null object\n",
"MultipleLines 7043 non-null object\n",
"InternetService 7043 non-null object\n",
"OnlineSecurity 7043 non-null object\n",
"OnlineBackup 7043 non-null object\n",
"DeviceProtection 7043 non-null object\n",
"TechSupport 7043 non-null object\n",
"StreamingTV 7043 non-null object\n",
"StreamingMovies 7043 non-null object\n",
"Contract 7043 non-null object\n",
"PaperlessBilling 7043 non-null object\n",
"PaymentMethod 7043 non-null object\n",
"MonthlyCharges 7043 non-null float64\n",
"TotalCharges 7043 non-null float64\n",
"Churn 7043 non-null object\n",
"Churn_Num 7043 non-null int32\n",
"TenureRange 7043 non-null category\n",
"TenureCat 7043 non-null float64\n",
"MonthlyChargesRange 7043 non-null category\n",
"MonthlyChargesCat 7043 non-null float64\n",
"Family 7043 non-null object\n",
"OnlineServices 7043 non-null object\n",
"StreamingServices 7043 non-null object\n",
"Gender_Num 7043 non-null int32\n",
"Family_Num 7043 non-null int32\n",
"PhoneService_Num 7043 non-null int32\n",
"MultipleLines_Num 7043 non-null int32\n",
"InternetService_Num 7043 non-null int32\n",
"OnlineServices_Num 7043 non-null int32\n",
"DeviceProtection_Num 7043 non-null int32\n",
"StreamingServices_Num 7043 non-null int32\n",
"TechSupport_Num 7043 non-null int32\n",
"Contract_Num 7043 non-null int32\n",
"PaperlessBilling_Num 7043 non-null int32\n",
"PaymentMethod_Num 7043 non-null int32\n",
"dtypes: category(2), float64(4), int32(13), int64(2), object(20)\n",
"memory usage: 1.8+ MB\n"
]
}
],
"source": [
"datset_churn.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now we will delete the non-required rows and prepare the dataset for classification"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"# Take a copy of dataset\n",
"datset_churn_copy = datset_churn.copy()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7043 entries, 0 to 7042\n",
"Data columns (total 17 columns):\n",
"CustomerID 7043 non-null object\n",
"Gender 7043 non-null int32\n",
"SeniorCitizen 7043 non-null int64\n",
"Family 7043 non-null int32\n",
"TenureCat 7043 non-null float64\n",
"PhoneService 7043 non-null int32\n",
"MultipleLines 7043 non-null int32\n",
"InternetService 7043 non-null int32\n",
"OnlineServices 7043 non-null int32\n",
"DeviceProtection 7043 non-null int32\n",
"TechSupport 7043 non-null int32\n",
"StreamingServices 7043 non-null int32\n",
"Contract 7043 non-null int32\n",
"PaperlessBilling 7043 non-null int32\n",
"PaymentMethod 7043 non-null int32\n",
"MonthlyChargesCat 7043 non-null float64\n",
"Churn 7043 non-null int32\n",
"dtypes: float64(2), int32(13), int64(1), object(1)\n",
"memory usage: 577.8+ KB\n"
]
}
],
"source": [
"#Dropping the Categorical columns and keeping their equivalent numeric column\n",
"columns_to_drop = ['Gender', 'Partner', 'Dependents', 'Tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'TotalCharges', 'Churn', 'Family', 'OnlineServices', 'StreamingServices']\n",
"datset_churn = datset_churn.drop(columns_to_drop, axis=1)\n",
"\n",
"#Re-arranging the columns as per origial dataset\n",
"datset_churn = datset_churn[['CustomerID', 'Gender_Num', 'SeniorCitizen', 'Family_Num', 'TenureCat', 'PhoneService_Num', 'MultipleLines_Num', 'InternetService_Num', 'OnlineServices_Num', 'DeviceProtection_Num', 'TechSupport_Num', 'StreamingServices_Num', 'Contract_Num', 'PaperlessBilling_Num', 'PaymentMethod_Num', 'MonthlyChargesCat', 'Churn_Num']]\n",
"datset_churn = datset_churn.rename(columns={'Gender_Num' : 'Gender', \n",
" 'Family_Num' : 'Family',\n",
" 'PhoneService_Num' : 'PhoneService',\n",
" 'MultipleLines_Num': 'MultipleLines', \n",
" 'InternetService_Num' : 'InternetService', \n",
" 'OnlineServices_Num' : 'OnlineServices', \n",
" 'DeviceProtection_Num' : 'DeviceProtection',\n",
" 'TechSupport_Num' : 'TechSupport', \n",
" 'StreamingServices_Num' : 'StreamingServices', \n",
" 'Contract_Num' : 'Contract', \n",
" 'PaperlessBilling_Num' : 'PaperlessBilling', \n",
" 'PaymentMethod_Num' : 'PaymentMethod', \n",
" 'MonthlyCharges' : 'MonthlyCharges', \n",
" 'Churn_Num' : 'Churn' })\n",
"datset_churn.info()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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",
" .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>CustomerID</th>\n",
" <th>Gender</th>\n",
" <th>SeniorCitizen</th>\n",
" <th>Family</th>\n",
" <th>TenureCat</th>\n",
" <th>PhoneService</th>\n",
" <th>MultipleLines</th>\n",
" <th>InternetService</th>\n",
" <th>OnlineServices</th>\n",
" <th>DeviceProtection</th>\n",
" <th>TechSupport</th>\n",
" <th>StreamingServices</th>\n",
" <th>Contract</th>\n",
" <th>PaperlessBilling</th>\n",
" <th>PaymentMethod</th>\n",
" <th>MonthlyChargesCat</th>\n",
" <th>Churn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7590-VHVEG</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
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" <td>1.0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>5575-GNVDE</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3668-QPYBK</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7795-CFOCW</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9237-HQITU</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>9305-CDSKC</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1452-KIOVK</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>6713-OKOMC</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7892-POOKP</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>6388-TABGU</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CustomerID Gender SeniorCitizen Family TenureCat PhoneService \\\n",
"0 7590-VHVEG 1 0 1 0.0 0 \n",
"1 5575-GNVDE 0 0 0 3.0 1 \n",
"2 3668-QPYBK 0 0 0 0.0 1 \n",
"3 7795-CFOCW 0 0 0 3.0 0 \n",
"4 9237-HQITU 1 0 0 0.0 1 \n",
"5 9305-CDSKC 1 0 0 0.0 1 \n",
"6 1452-KIOVK 0 0 1 2.0 1 \n",
"7 6713-OKOMC 1 0 0 1.0 0 \n",
"8 7892-POOKP 1 0 1 2.0 1 \n",
"9 6388-TABGU 0 0 1 5.0 1 \n",
"\n",
" MultipleLines InternetService OnlineServices DeviceProtection \\\n",
"0 2 0 1 0 \n",
"1 0 0 1 1 \n",
"2 0 0 1 0 \n",
"3 2 0 1 1 \n",
"4 0 1 0 0 \n",
"5 1 1 0 1 \n",
"6 1 1 1 0 \n",
"7 2 0 1 0 \n",
"8 1 1 0 1 \n",
"9 0 0 1 0 \n",
"\n",
" TechSupport StreamingServices Contract PaperlessBilling PaymentMethod \\\n",
"0 0 0 0 1 0 \n",
"1 0 0 1 0 1 \n",
"2 0 0 0 1 1 \n",
"3 1 0 1 0 2 \n",
"4 0 0 0 1 0 \n",
"5 0 1 0 1 0 \n",
"6 0 1 0 1 3 \n",
"7 0 0 0 0 1 \n",
"8 1 1 0 1 0 \n",
"9 0 0 1 0 2 \n",
"\n",
" MonthlyChargesCat Churn \n",
"0 1.0 0 \n",
"1 2.0 0 \n",
"2 2.0 1 \n",
"3 2.0 0 \n",
"4 3.0 1 \n",
"5 4.0 1 \n",
"6 4.0 0 \n",
"7 1.0 0 \n",
"8 5.0 1 \n",
"9 2.0 0 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datset_churn.head(10) # Taking a quick look into the new data"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 5634 samples in the training set and 1409 samples in the test set\n"
]
}
],
"source": [
"X = datset_churn.iloc[:,1:16].values # Feature Variable\n",
"y = datset_churn.iloc[:,16].values # Target Variable\n",
"\n",
"#Dividing data into test & train splitting 70% data for training anf 30% for test\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
"print('There are {} samples in the training set and {} samples in the test set'.format(X_train.shape[0], X_test.shape[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification\n",
"### We will run all classifiers to have an initial look at the performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Defining function for Confusion Matrix , Precision, Recall and F1 Score"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"#Creating function for Confusion Matrix , Precsion, Recall and F1 Score\n",
"def plot_confusion_matrix(classifier, y_test, y_pred_test):\n",
" cm = confusion_matrix(y_test, y_pred_test)\n",
" \n",
" print(\"\\n\",classifier,\"\\n\")\n",
" plt.clf()\n",
" plt.imshow(cm, interpolation='nearest', cmap='RdBu')\n",
" classNames = ['Churn-No','Churn-Yes']\n",
" plt.ylabel('True label')\n",
" plt.xlabel('Predicted label')\n",
" tick_marks = np.arange(len(classNames))\n",
" plt.xticks(tick_marks, classNames, rotation=45)\n",
" plt.yticks(tick_marks, classNames)\n",
" s = [['TN','FP'], ['FN', 'TP']]\n",
" \n",
" for i in range(2):\n",
" for j in range(2):\n",
" plt.text(j,i, str(s[i][j])+\" = \"+str(cm[i][j]), \n",
" horizontalalignment='center', color='White')\n",
" \n",
" plt.show()\n",
" \n",
" tn, fp, fn, tp = cm.ravel()\n",
"\n",
" recall = tp / (tp + fn)\n",
" precision = tp / (tp + fp)\n",
" F1 = 2*recall*precision/(recall+precision)\n",
"\n",
" print('Recall={0:0.3f}'.format(recall),'\\nPrecision={0:0.3f}'.format(precision))\n",
" print('F1={0:0.3f}'.format(F1))\n",
" return;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Defining function for Precision Recall Curve"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import average_precision_score, precision_recall_curve\n",
"def plot_prec_rec_curve(classifier, y_test, y_pred_score):\n",
" precision, recall, _ = precision_recall_curve(y_test, y_pred_score)\n",
" average_precision = average_precision_score(y_test, y_pred_score)\n",
"\n",
" print('Average precision-recall score: {0:0.3f}'.format(\n",
" average_precision))\n",
"\n",
" plt.plot(recall, precision, label='area = %0.3f' % average_precision, color=\"green\")\n",
" plt.xlim([0.0, 1.0])\n",
" plt.ylim([0.0, 1.05])\n",
" plt.xlabel('Recall')\n",
" plt.ylabel('Precision')\n",
" plt.title('Precision Recall Curve')\n",
" plt.legend(loc=\"best\")\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Master Classification Engine"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" LogisticRegression \n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.532 \n",
"Precision=0.683\n",
"F1=0.599\n",
"Average precision-recall score: 0.663\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" KNeighborsClassifier \n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.504 \n",
"Precision=0.557\n",
"F1=0.529\n",
"Average precision-recall score: 0.516\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" GaussianNB \n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.764 \n",
"Precision=0.488\n",
"F1=0.595\n",
"Average precision-recall score: 0.647\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" SVC \n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.514 \n",
"Precision=0.723\n",
"F1=0.601\n",
"Average precision-recall score: 0.651\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DecisionTreeClassifier \n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.475 \n",
"Precision=0.500\n",
"F1=0.487\n",
"Average precision-recall score: 0.382\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" RandomForestClassifier \n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.444 \n",
"Precision=0.562\n",
"F1=0.496\n",
"Average precision-recall score: 0.526\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" SGDClassifier \n",
"\n"
]
},
{
"data": {
"image/png": 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VHH/1t6rDrEX2VVLKveddztp3V9C1X2+umvMUi6a/SOmnm5n9p8f5w7nfBWDCg79i1DfP5sUpD7TYvtu7pDsrN3wGwG4dC0m6s7GkHIDBvTpXz+vXpZhuHQqq5zXFZ+WVbN0WfZEU5uXQr2sxKz6Jt9+1mE8+20ZJeRSeVYm3bksZcbbSq1Mh3YsL2NCMGlqTupmB+3D+Yko/3cIBx45qsW2ufec91r67AoBPP1rLlrWf0LlXDwAWPPOv6uVWzJ5P9wF9W2y/u5KSikryc5v351mzRWW2/Z7eBfF2S8oT1ctVLZqssU6Ota/7gKtlFoiC4iKunjsNgE/e+4App11YPe+ZG3/NKTdeweIZL9W7/tgrJjLy66fuNP2dF1/lkUuvr3e9gSMOIbcgn3XLVu4wPScvj8O/Ma7BdaV+HQvyKCnfuXver2txdRjVtLGknM1lFTtN71SYR89OheTl5PDhphIACvJySLrTv2sx+bk5lJRXsm7rtup1+nYpomNBHuWJJGu3lLXgu8qsjIaZmfUFbgVGANuAFcDjwCnuflIr7HsWcLi7r42n3QksdfdbMrnvbKirm1nl3ZdeA2DfUSPqXf+5W+7kuVvuTGufXfr2Yvwff8F9/3UFXmtg5Zw7fsI7L86u3rc0zgz26tERgNKKBJtKdw6njz4tTWubW7dFXc3i/Fx6diysDrTi/DxWbthKRSIKta5F+Xwah+HHm6MA6925iM5F+XWGZFuUsTCzqF07FbjP3c+Opw0FTm7mdvPcvdERZXf/2Mx+DvwMGG9mI4CRwMXN2X979czk2zn+6m+RqGcwPt2WWVHnTnzr6Xt44pqf896rc3eYd+KPL6VTr93404UX7rSe1M+d6jGz+qTbMqtSWpEgPy+HXDMqk862ygQViegLaOu2Soryc6HW+lvKKujRsUBhBhwDVLj7lKoJ7j7PzLoBY8zsr8BBwBzgXHd3M1sBDHf39WY2HLjF3Ueb2XVAf2AgsN7MngVOAToAg4Cp7n5lHTXcAbxiZkcThdol7l5pZvnAzcCRQCFwq7vfY2Z7AA8BHYFc4Jvu/moL/7tkxeLnZnLKT75H1/6965yfTsssNz+fi6b+jlfuf4w3/jpth3lHTTiLIV/+IreOOWen1po0Xzots/xcqw6swrwcDEi4k6hIkGNGrhkJdzoURL+i1l6nU2Ee5ZXJFn8PmZLJMKsKqrocChwIrAZeBo4C6h/QiQwDRrl7qZmNB4bG29kGLDGzX7v7BzVXcPekmU0CZgCPuvvL8axJwIfuPtLMioBX44A8D3jM3X9uZrlAce0izGwiMBGgE7mNlNy2PDP5di5+4vfN3s6wr57I4C+OpONu3fmP8WcAcN/4K/hw/iLOmTKZDStXceWsqQDMfezvTPvJr5q9T0lf58J8uhTnRwP87jsE4bqtZdWHiJRVbu/S9utSHA38G2yrSLJmS3rd2myyTH17mtl3gL3d/bJa00cDV7v72Pj1b4GX3f2BRlpm7u7Xx+uMB45y9wvi188Ak929zkA0s38D33L3N+LXTwH7AlWjm12B8UABUWvuj8Dj7v5mQ++xlxX66fRL/R9Fsu6yjxv8SKUN2r9v1znuPryx5TJ5aMZCotZUXbbVeJ5gewuxskZNRbXWqT2YsNM2zGycmc2LHzXffDJ+VDHgQncfGj/2dvcX3P05ou7xGuAhMzuroTcoIm1HJsPsH0ChmV1QNSEehD+6gXVWsD0AT093h+4+tUZAvd7AotOBi80sL67rADMrMrOBwEfu/jvgfqJurIi0AxkLM4/6r+OAsWa2zMwWAtcRjZPV53rgNjObSdTaypQ7gPeBuWa2ALidaMB/LDDfzOYCxwO/yWANItKCMjZmtivQmFn7ozGz9qctjJmJiLQahZmIBEFhJiJBUJiJSBAUZiISBIWZiARBYSYiQVCYiUgQFGYiEgSFmYgEQWEmIkFQmIlIEBRmIhIEhZmIBEFhJiJBUJiJSBAUZiISBIWZiARBYSYiQVCYiUgQFGYiEgSFmYgEQWEmIkFQmIlIEBRmIhIEhZmIBEFhJiJBUJiJSBAUZiISBIWZiARBYSYiQVCYiUgQFGYiEgSFmYgEQWEmIkFQmIlIEBRmIhIEhZmIBEFhJiJBUJiJSBAUZiISBIWZiARBYSYiQVCYiUgQFGYiEgSFmYgEQWEmIkFQmIlIEBRmIhIEc/ds19Bumdk6YGW268iQnsD6bBchKQv589rL3Xs1tpDCTOpkZq+7+/Bs1yGp0eelbqaIBEJhJiJBUJhJfe7MdgGSll3+89KYmYgEQS0zEQmCwkxEgqAwE5EgKMykWczMsl2DpC/Ez01hJk1mZubxL0hmdpSZDTWz/WrOz151Up9an9tZZjbCzHKzXVdz5WW7AGm/avxBXA58FVgNlJrZLHe/3fVTeZtU43O7BLgQ+Iq7J7JbVfMpzCRtZtYZKHH3hJntCZwBjAFygYOA75rZu+7+92zWKfUzsyHABOAEd//QzI4HioE33H1FVotrIoWZpMXMPgdcDdxuZm/Ek4uIjlncbGZvAcuAPbJVo+ysZtcy9gHwPPBzM9sE7AOsA3YD7spCic2mMTNJi7svBbYSdU8Odvf3gVlE4dbB3bcApcBg0LhZW2BmOTW6lqPMrKoV/QLwGvBLdx8LvAXsV/+W2jadASApMbNeQIW7b4pfTwb2Bn4GbAa+BRwLPAJ8AzjF3ZdkqVypg5l9FzgdWArsD1zk7m/F884FLgPOdffF2auy6dQyk0aZ2VBgFTDNzC4ys4Pc/WrgXaJxlx7ufjlwM9EfyskKsuwzs4PN7Oj4+WHAaHf/AvAmUAIsjOcNJRrzHN9egwzUMpMUmFl/4EbgCOAd4H1gIPAwcBGwAJji7vOyVaPsyMwKgNuBOe7+OzPrC5wPDAAGASe5e4WZnQY8BeS5e0n2Km4+tcykUe6+mijM7iEaD7sOuA/oBnQFJgK3xX9AkkVVY5TuXg48BxwWz+oAfB44APhaHGTnAdcC3dt7kIFaZpIGMxtE1BLbDbjC3TeY2R5EY2er3f3drBYoVYdcLAHyiQbzbyJqhSXM7DiicbF3iI5kGEUUbAuzVW9LUphJo2odMb4PcAGwO/ADd/8oq8VJNTMrJjrc4n2iL5z7gBuA0919brzMMKA/0Bv4p7svz1K5LU5hJjuofTySmeUAuHsy/tbPB7YQfcMXEv0ilsxKsbKTONAKgBPi//4A+BPwW3ffkM3aMk1hJtVqtcCGAJvi8TLMbCTwJNEhF6+a2V5AqbuvzV7FUlMdB8YSD/z/AXgduM3dP8lKca1AYSY7MbNLgfFEg8bXA/8AvgKscPfpWSxNUhAfJJs0s9x4rGx3ouP/pgH/G+o5szqdSXZgZicAXwKGAf9JdKR/DvBE1fhYXS0AaX2NDAkcDOSY2QJ3X2VmpwP5IX9uOjRjF1fzdCMzO5BorKWnuyfdfRrRjTLOBE41s66w/aoLkj21hwTMrH/8mSXjIYFngY5xyyzH3T929w+yW3VmKcx2cTX+IM4HJgHTgTVmdqWZ5bn700RjLqOzV6XUVuNzu5RogP+fZnZOPEZ2KNHR/P+Ol90lfqBRN1MwsyOBU4Ez3L08bq2NBS4zs1+6+9/MbIa7f5bdSqUmDQnsSC2zXVz8TX4R0SV7+seTnwX+DgwhOoEconP5JIs0JNAwhdkuLD4ifCvwC6LzK08zswHuXkZ08OVD8WOX+qNoqzQk0DAdmrGLMrMORCHWm+iSPZ8Hvk50JYzH4uuUSRsTDwn8kO1DAqcQDQm8T3Rdskoz67grDgmoZbaLik8svgFYDtwNzCcaSB4KnBTCDS5CoyGBhqlltouJL8LX291/Eb/uQ3QZ7E7AJcDBwPvu/nH2qpTa4iGBfwP7At8D5gKPxNfvLwKOBubvyp+bWmaBqzqQsoblRDccuRjA3dcQjb0cTtRNmb0r/0G0RfGQwGnA/URXvLiD6Jr9Z5jZnu5e5u7Td/XPTWEWsPhn+WT8/Agz2yc+9uhEokC7JF60A/Ao8JMslSoN0JBAatTNDFR8oviF7n5p/OvX94mu1f8QMIXom/1eom/6I4Hj4puVSBuhIYH0KMwCZGbDia4wehbwHtAROBf4D+AcYCXRJZULgL7A1tBPdWkPqk4Qr/H6SKIvn5vc/Y542olEN5GZ6e4XZafStkndzMCY2UnA74BK4LdAOXCQuyfc/SWiP46BwBVAV3dfrCDLPg0JNJ9OZwpIfCeeW4FvuPuseNoCYC8zu83dL3X3F80sHziJqNspWVY1JADsMCRgZlVDAuOAe83sKLYPCazKWsFtlLqZATGzy4GEu99mZgXxQZW5RNeC/y7RxRavjJctdvfSbNYrGhJoSepmBqDGOXt7A73i5xVx1yUBLAZmAsPM7KZ4flkrlym1aEigZambGYAa501OBa4ys2HuPsfMcuJB5YSZ9SO6VdyMWutIFmhIoOWpmxkQM+tINN7SAXjY3efE078GfAc4S+dctg0aEmh56mYGJD65+C7iK2GY2S1mdiPRjV4nKsiyT0MCmaOWWYAsut3YMOBY4COi+yPqgNg2xMy+BFxFdO/ROfFpZxYPCVwJrAZm6IDY1CnMRLJAQwItT2EmkiXxLeAmAGOA14i6k2cAZ7r7W9msrT1SmIlkkYYEWo7CTESCoF8zRSQICjMRCYLCTESCoDATkSAozEQkCAozySgzS5jZPDNbYGZ/iW/O0dRtjTazp+Lnp5jZDxtYtlvVTVvS3Md1ZnZFqtNrLXOvmZ2Rxr4GxieXSwtQmEmmlbr7UHc/iOgSNztc6tkiaf9/6O5PuPtNDSzSDUg7zKT9UphJa5oJ7Bu3SBab2R3AG8AeZnacmc0yszfiFlwnADP7TzN728xeIrrdGvH08WZ2e/y8j5lNNbP58eNI4CZgUNwqvDle7vtm9pqZvWlm19fY1tVmtsTMZhBdtaJBZnZBvJ35ZvZordbmsWY208yWxtcrw8xyzezmGvu+sLn/kLIzhZm0CjPLA44Hqk7T2Q+4390PBT4DrgGOdffDgNeByy26ue1dwMnAF4iutFqXXwEvuPshRFdtXQj8EFgWtwq/b9FNdAcDI4lu0TbMzL5oZsOAs4FDicJyRApv5zF3HxHvbzHRKUlVBhLdkPdEYEr8HiYAn7r7iHj7F5jZ3insR9KgizNKphWb2bz4+Uyi+z72B1a6+yvx9COAIcDL8RVyCoBZwP7Ae+7+DoCZPQBMrGMfXwLOA4gvo/OpmXWvtcxx8WNu/LoTUbh1BqbG96bEzJ5I4T0dFF9aqVu8nek15j0S35jkHTNbHr+H44DP1xhP6xrvW6cttSCFmWRaqbsPrTkhDqzPak4CnnP3r9VabijQUufbGfBTd/9drX18twn7uBc41d3nm9l4YHSNebW35fG+v+3uNUMPMxuY5n6lAepmSlvwCnCUme0LYGYdzOxzwNvA3mY2KF7ua/Ws/zwwKV4318y6AFuIWl1VpgPn1xiL293MegMvAuPMrNjMOhN1aRvTGfgovqT112vNOzO+XPkgohstL4n3PSleHjP7XHwJIGlBaplJ1rn7uriF82czK4wnX+PuS81sIvC0ma0HXgIOqmMTlwJ3mtkEIAFMcvdZZvZyfOjDM/G42QHArLhluBU4193fMLOHgXlEd0KamULJ/wO8Gi//FjuG5hLgBaAPcJG7l5nZ74nG0t6IrzS7Djg1tX8dSZWumiEiQVA3U0SCoDATkSAozEQkCAozEQmCwkxEgqAwE5EgKMxEJAgKMxEJwv8Dal8OCb7C1rMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.943 \n",
"Precision=0.374\n",
"F1=0.535\n",
"Average precision-recall score: 0.549\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" AdaBoostClassifier \n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.545 \n",
"Precision=0.698\n",
"F1=0.612\n",
"Average precision-recall score: 0.674\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Making a list of all classifiers\n",
"classifier_model = [LogisticRegression(),KNeighborsClassifier(),GaussianNB(),SVC(),DecisionTreeClassifier(),RandomForestClassifier(), SGDClassifier(), AdaBoostClassifier()]\n",
"\n",
"# Creating empty list to store the performance details\n",
"classifier_model_list= []\n",
"classifier_accuracy_test = []\n",
"classifier_accuracy_train = []\n",
"f1score = []\n",
"precisionscore = []\n",
"recallscore = []\n",
"avg_pre_rec_score = []\n",
"cv_score = []\n",
"\n",
"for classifier_list in classifier_model:\n",
" classifier = classifier_list\n",
" \n",
" # Fitting the training set into classification model\n",
" classifier.fit(X_train,y_train)\n",
" \n",
" # Predicting the output on test datset\n",
" y_pred_test = classifier.predict(X_test) \n",
" score_test = accuracy_score(y_test, y_pred_test)\n",
" \n",
" # Predicting the output on training datset\n",
" y_pred_train = classifier.predict(X_train) \n",
" score_train = accuracy_score(y_train, y_pred_train)\n",
" \n",
" # Cross Validation Score on training test\n",
" scores = cross_val_score(classifier, X_train,y_train, cv=10)\n",
" cv_score.append(scores.mean())\n",
" \n",
" #Keeping the model and accuracy score into a list\n",
" classifier_model_list.append(classifier_list.__class__.__name__)\n",
" classifier_accuracy_test.append(round(score_test,4))\n",
" classifier_accuracy_train.append(round(score_train,4))\n",
" \n",
" #Precision, Recall and F1 score\n",
" f1score.append(f1_score(y_test, y_pred_test))\n",
" precisionscore.append(precision_score(y_test, y_pred_test))\n",
" recallscore.append(recall_score(y_test, y_pred_test))\n",
" \n",
" #Calculating Average Precision Recall Score\n",
" try:\n",
" y_pred_score = classifier.decision_function(X_test)\n",
" except:\n",
" y_pred_score = classifier.predict_proba(X_test)[:,1]\n",
" \n",
" from sklearn.metrics import average_precision_score\n",
" average_precision = average_precision_score(y_test, y_pred_score)\n",
" avg_pre_rec_score.append(average_precision)\n",
" \n",
" \n",
" #Confusion Matrix\n",
" plot_confusion_matrix(classifier_list.__class__.__name__, y_test, y_pred_test)\n",
" plot_prec_rec_curve(classifier_list.__class__.__name__, y_test, y_pred_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CLASSIFICATION MODEL PERFORMANCE EVALUATION"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"*------------------------------ CLASSIFICATION MODEL PERFORMANCE EVALUATION ---------------------*\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>Cross Val Score</th>\n",
" <th>Test Accuracy</th>\n",
" <th>Average_Accuracy</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>Avg Precision Recall</th>\n",
" <th>F1 Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RandomForestClassifier</td>\n",
" <td>0.764294</td>\n",
" <td>0.9290</td>\n",
" <td>0.846647</td>\n",
" <td>0.562500</td>\n",
" <td>0.444156</td>\n",
" <td>0.525998</td>\n",
" <td>0.496372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>DecisionTreeClassifier</td>\n",
" <td>0.730566</td>\n",
" <td>0.9395</td>\n",
" <td>0.835033</td>\n",
" <td>0.500000</td>\n",
" <td>0.475325</td>\n",
" <td>0.382063</td>\n",
" <td>0.487350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SVC</td>\n",
" <td>0.798731</td>\n",
" <td>0.8076</td>\n",
" <td>0.803166</td>\n",
" <td>0.722628</td>\n",
" <td>0.514286</td>\n",
" <td>0.650592</td>\n",
" <td>0.600910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LogisticRegression</td>\n",
" <td>0.802106</td>\n",
" <td>0.8039</td>\n",
" <td>0.803003</td>\n",
" <td>0.683333</td>\n",
" <td>0.532468</td>\n",
" <td>0.663086</td>\n",
" <td>0.598540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>AdaBoostClassifier</td>\n",
" <td>0.799797</td>\n",
" <td>0.8026</td>\n",
" <td>0.801198</td>\n",
" <td>0.697674</td>\n",
" <td>0.545455</td>\n",
" <td>0.674440</td>\n",
" <td>0.612245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>KNeighborsClassifier</td>\n",
" <td>0.762881</td>\n",
" <td>0.8387</td>\n",
" <td>0.800790</td>\n",
" <td>0.557471</td>\n",
" <td>0.503896</td>\n",
" <td>0.516306</td>\n",
" <td>0.529332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GaussianNB</td>\n",
" <td>0.736608</td>\n",
" <td>0.7368</td>\n",
" <td>0.736704</td>\n",
" <td>0.487562</td>\n",
" <td>0.763636</td>\n",
" <td>0.647102</td>\n",
" <td>0.595142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>SGDClassifier</td>\n",
" <td>0.766244</td>\n",
" <td>0.5424</td>\n",
" <td>0.654322</td>\n",
" <td>0.373841</td>\n",
" <td>0.942857</td>\n",
" <td>0.549295</td>\n",
" <td>0.535398</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Cross Val Score Test Accuracy Average_Accuracy \\\n",
"5 RandomForestClassifier 0.764294 0.9290 0.846647 \n",
"4 DecisionTreeClassifier 0.730566 0.9395 0.835033 \n",
"3 SVC 0.798731 0.8076 0.803166 \n",
"0 LogisticRegression 0.802106 0.8039 0.803003 \n",
"7 AdaBoostClassifier 0.799797 0.8026 0.801198 \n",
"1 KNeighborsClassifier 0.762881 0.8387 0.800790 \n",
"2 GaussianNB 0.736608 0.7368 0.736704 \n",
"6 SGDClassifier 0.766244 0.5424 0.654322 \n",
"\n",
" Precision Recall Avg Precision Recall F1 Score \n",
"5 0.562500 0.444156 0.525998 0.496372 \n",
"4 0.500000 0.475325 0.382063 0.487350 \n",
"3 0.722628 0.514286 0.650592 0.600910 \n",
"0 0.683333 0.532468 0.663086 0.598540 \n",
"7 0.697674 0.545455 0.674440 0.612245 \n",
"1 0.557471 0.503896 0.516306 0.529332 \n",
"2 0.487562 0.763636 0.647102 0.595142 \n",
"6 0.373841 0.942857 0.549295 0.535398 "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Creating pandas dataframe with Model and corresponding accuracy\n",
"#accuracy_df = pd.DataFrame({'Model':classifier_model_list , 'Test Accuracy':classifier_accuracy_test, 'Train Accuracy' :classifier_accuracy_train , 'Precision':precisionscore, 'Recall':recallscore ,'F1 Score':f1score},index=None)\n",
"accuracy_df = pd.DataFrame({'Model':classifier_model_list , 'Cross Val Score':cv_score, 'Test Accuracy' :classifier_accuracy_train , 'Precision':precisionscore, 'Recall':recallscore ,'Avg Precision Recall':avg_pre_rec_score ,'F1 Score':f1score})\n",
"\n",
"# Calculating Average Accuracy = (Test + Train)/2\n",
"accuracy_df['Average_Accuracy'] = (accuracy_df['Cross Val Score'] + accuracy_df['Test Accuracy'] )/ 2\n",
"\n",
"#Arranging the Columns\n",
"print(\"\\n*------------------------------ CLASSIFICATION MODEL PERFORMANCE EVALUATION ---------------------*\\n\")\n",
"accuracy_df = accuracy_df[['Model','Cross Val Score', 'Test Accuracy', 'Average_Accuracy','Precision', 'Recall','Avg Precision Recall','F1 Score']] # This will arrange the columns in the order we want\n",
"\n",
"#Sorting the Columns based on Average Accuracy\n",
"accuracy_df.sort_values('Average_Accuracy', axis=0, ascending=False, inplace=True) # Sorting the data with highest accuracy in the top\n",
"accuracy_df\n",
"#accuracy_df.transpose()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observations\n",
"\n",
"1. Since our dataset class is imbalanced. Churn \"Yes\" is almost 3 times as \"No', Accuracy is not the right measure and we have to consider Precision, Recall and F1 Score for further evaluation and improvement of model\n",
" \n",
" 1.1 Precision: A measure of a classifiers exactness.A low precision can also indicate a large number of False Positives.\n",
" \n",
" 1.2 Recall: A measure of a classifiers completeness.A low recall indicates many False Negatives.\n",
" \n",
" 1.3 F1 Score (or F-score): A weighted average or Harmonic Mean of precision and recall.\n",
"\n",
"2. Logistic Regression (AUC = 0.66) and Adaboost model (AUC = 0.67) looks promising. Let's try to improve the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Improving our Model: Model Tuning\n",
"### Grid Search for Logistic Regression Classifier and running with optimized hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unoptimized model\n",
"------\n",
"Accuracy score on testing data: 0.8048\n",
"F-score on testing data: 0.6467\n",
"\n",
"Optimized Model\n",
"------\n",
"Final accuracy score on the testing data: 0.8041\n",
"Final F-score on the testing data: 0.6451\n",
"LogisticRegression(C=2.7825594022071245, class_weight=None, dual=False,\n",
" fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
" multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
" solver='liblinear', tol=0.0001, verbose=0, warm_start=False)\n"
]
}
],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.metrics import make_scorer\n",
"from sklearn.metrics import fbeta_score, accuracy_score\n",
"from sklearn.linear_model import LogisticRegression # Logistic Regression Classifier\n",
"\n",
"#Logistic Regression Classifier\n",
"clf = LogisticRegression()\n",
"\n",
"#Hyperparameters\n",
"parameters = {'C':np.logspace(0, 4, 10), \n",
" 'penalty' : ['l1', 'l2']\n",
" }\n",
"\n",
"# Make an fbeta_score scoring object\n",
"scorer = make_scorer(fbeta_score,beta=0.5)\n",
"\n",
"# Perform grid search on the classifier using 'scorer' as the scoring method\n",
"grid_obj = GridSearchCV(clf, parameters,scorer)\n",
"\n",
"# Fit the grid search object to the training data and find the optimal parameters\n",
"grid_fit = grid_obj.fit(X_train,y_train)\n",
"\n",
"# Get the estimator\n",
"best_clf = grid_fit.best_estimator_\n",
"\n",
"# View best hyperparameters\n",
"#print(grid_srchfit.best_params_)\n",
"\n",
"# Make predictions using the unoptimized and model\n",
"predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
"best_predictions = best_clf.predict(X_test)\n",
"\n",
"# Report the before-and-afterscores\n",
"print (\"Unoptimized model\\n------\")\n",
"print (\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
"print (\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
"print (\"\\nOptimized Model\\n------\")\n",
"print (\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
"print (\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
"print (best_clf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Grid Search for Adaboost Classifier and running with optimized hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unoptimized model\n",
"------\n",
"Accuracy score on testing data: 0.7388\n",
"F-score on testing data: 0.5185\n",
"\n",
"Optimized Model\n",
"------\n",
"Final accuracy score on the testing data: 0.8041\n",
"Final F-score on the testing data: 0.6452\n",
"AdaBoostClassifier(algorithm='SAMME.R',\n",
" base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best'),\n",
" learning_rate=0.5, n_estimators=120, random_state=None)\n"
]
}
],
"source": [
"# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.metrics import make_scorer\n",
"from sklearn.metrics import fbeta_score, accuracy_score\n",
"\n",
"# TODO: Initialize the classifier\n",
"clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())\n",
"\n",
"# TODO: Create the parameters list you wish to tune\n",
"parameters = {'n_estimators':[50, 120], \n",
" 'learning_rate':[0.1, 0.5, 1.],\n",
" 'base_estimator__min_samples_split' : np.arange(2, 8, 2),\n",
" 'base_estimator__max_depth' : np.arange(1, 4, 1)\n",
" }\n",
"\n",
"# TODO: Make an fbeta_score scoring object\n",
"scorer = make_scorer(fbeta_score,beta=0.5)\n",
"\n",
"# TODO: Perform grid search on the classifier using 'scorer' as the scoring method\n",
"grid_obj = GridSearchCV(clf, parameters,scorer)\n",
"\n",
"# TODO: Fit the grid search object to the training data and find the optimal parameters\n",
"grid_fit = grid_obj.fit(X_train,y_train)\n",
"\n",
"# Get the estimator\n",
"best_clf = grid_fit.best_estimator_\n",
"\n",
"# Make predictions using the unoptimized and model\n",
"predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
"best_predictions = best_clf.predict(X_test)\n",
"\n",
"# Report the before-and-afterscores\n",
"print (\"Unoptimized model\\n------\")\n",
"print (\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
"print (\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
"print (\"\\nOptimized Model\\n------\")\n",
"print (\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
"print (\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))\n",
"print (best_clf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Observations:\n",
"Both Logistic Regression & Adaboost Classifier gives us final F score of 0.65 and accuracy of 0.80 post grid search."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Importance\n",
"### In this section , we will run scikit learn feature importances to evaluate which columns are being give higher weights"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Features Adaboost_Score\n",
"11 Contract 0.18\n",
"14 MonthlyChargesCat 0.18\n",
"3 TenureCat 0.14\n",
"13 PaymentMethod 0.12\n",
"6 InternetService 0.08\n",
"9 TechSupport 0.06\n",
"5 MultipleLines 0.04\n",
"8 DeviceProtection 0.04\n",
"10 StreamingServices 0.04\n",
"12 PaperlessBilling 0.04\n",
"1 SeniorCitizen 0.02\n",
"2 Family 0.02\n",
"4 PhoneService 0.02\n",
"7 OnlineServices 0.02\n",
"0 Gender 0.00\n"
]
}
],
"source": [
"# Feature Importance for Adaboost\n",
"from sklearn.feature_selection import RFE\n",
"features = list(datset_churn.columns[1:16])\n",
"\n",
"# Feature Importance for AdaBoostClassifier\n",
"adboost_cls = AdaBoostClassifier()\n",
"adboost_cls .fit(X_train, y_train)\n",
"feature_imp_adboost = np.round(adboost_cls.feature_importances_, 5)\n",
"\n",
"feature_imp_df = pd.DataFrame({'Features' :features, 'Adaboost_Score': feature_imp_adboost})\n",
"feature_imp_df.sort_values('Adaboost_Score', axis=0, ascending=False, inplace=True)\n",
"print(feature_imp_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Observation:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Overall - Contract, Monthly Charges, Tenure and Payment Method and Internet Service are leading columns contributing to churn. They consitute 60% weight from Mean_Feature_Importance\n",
"\n",
"2. Gender has no impact on Churn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now we will create the dataset with top 5 columns and run Adaboost classifier to see if there is any improvement in performance"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 5634 samples in the training set and 1409 samples in the test set\n"
]
}
],
"source": [
"dataset_churn_new = datset_churn[['Contract', 'MonthlyChargesCat', 'TenureCat', 'PaymentMethod', 'Churn']]\n",
"X_new = dataset_churn_new.iloc[:,:-1].values # Feature Variable\n",
"y_new = dataset_churn_new.iloc[:,-1].values # Target Variable\n",
"\n",
"#Dividing data into test & train splitting 70% data for training anf 30% for test\n",
"X_train_new, X_test_new, y_train_new, y_test_new = train_test_split(X_new , y_new, test_size=0.20)\n",
"print('There are {} samples in the training set and {} samples in the test set'.format(X_train_new.shape[0], X_test_new.shape[0]))"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Adaboost Classifier \n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.463 \n",
"Precision=0.649\n",
"F1=0.540\n",
"Average precision-recall score: 0.617\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Adaboost Classifier , filled the hyperparameter from the Grid Search\n",
"classifier = AdaBoostClassifier(algorithm='SAMME.R',\n",
" base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best'),\n",
" learning_rate=0.5, n_estimators=120, random_state=None)\n",
" \n",
"# Fitting the training set into classification model\n",
"classifier.fit(X_train_new, y_train_new)\n",
" \n",
"# Predicting the output on test datset\n",
"y_pred_new = classifier.predict(X_test_new) \n",
"\n",
"try:\n",
" y_pred_new_score = classifier.decision_function(X_test_new)\n",
"except:\n",
" y_pred_new_score = classifier.predict_proba(X_test_new)[:,1]\n",
" \n",
"#Confusion Matrix and Precision Recall Curve\n",
"plot_confusion_matrix('Adaboost Classifier', y_test_new, y_pred_new)\n",
"plot_prec_rec_curve('Adaboost Classifier', y_test_new, y_pred_new_score)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Logistic Regression \n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall=0.509 \n",
"Precision=0.648\n",
"F1=0.570\n",
"Average precision-recall score: 0.608\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Logistic Regression , filled the hyperparameter from the Grid Search\n",
"classifier_logreg = LogisticRegression(C=2.7825594022071245, class_weight=None, dual=False,\n",
" fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
" multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
" solver='liblinear', tol=0.0001, verbose=0, warm_start=False)\n",
" \n",
"# Fitting the training set into classification model\n",
"classifier_logreg.fit(X_train_new, y_train_new)\n",
" \n",
"# Predicting the output on test datset\n",
"y_pred_new = classifier_logreg.predict(X_test_new) \n",
"\n",
"try:\n",
" y_pred_new_score = classifier_logreg.decision_function(X_test_new)\n",
"except:\n",
" y_pred_new_score = classifier_logreg.predict_proba(X_test_new)[:,1]\n",
" \n",
"#Confusion Matrix and Precision Recall Curve\n",
"plot_confusion_matrix('Logistic Regression', y_test_new, y_pred_new)\n",
"plot_prec_rec_curve('Logistic Regression', y_test_new, y_pred_new_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"Adaboost classifier performed well with Precision Recall Curve - 0.62\n",
"Contract, Monthly Charges, Tenure and Payment Method and Internet Service are leading columns contributing to churn\n",
"\n",
"Model can be further improved using the strategies discussed in next paragragh.\n",
"\n",
"### Handling Imbalaced Dataset :\n",
"\n",
"1. Increasing the number of instances of the minority class (This case Churn = 'Yes') . We need more data with Churn Class as \"Yes\".\n",
"2. Decreasing the number of instances of majority class\n",
"3. Random Under-Sampling\n",
"4. Random Over-Sampling\n",
"5. Cluster-Based Over Sampling\n",
"6. Synthetic Minority Over-sampling Technique(SMOTE)\n",
"\n",
"Detailed explanation - https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hope you enjoyed the kernel. Thank You!\n",
"Jagannath Banerjee | [email protected] | https://www.linkedin.com/in/jagannath-banerjee/ | Aug 2018"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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