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Predict Churn for a Telecom company using Logistic Regression

Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

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What will you learn

  • Understand the customer behavior
  • Understand reasons for churn
  • What are the top factors
  • How to retain customers
  • Apply multiple classification models

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • This data science project will be executed in R programming language.

Project Description

Customer churn refers to a decision made by the customer about ending the business relationship. It is also referred to the loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from the business relationship and the factors affecting the customer decisions. Here we are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Problem Statement
00:02:48
  Read CSV files
00:02:45
  Exploratory Data Analysis
00:01:26
  Importance of Exploratory Data Analysis
00:01:11
  Univariate Features
00:01:43
  Box Plot for Numerical Data
00:06:17
  Observations from Box Plot
00:01:43
  Bar Plot for Categorical Data
00:02:58
  Feature Creation using Dummy Variables
00:09:04
  Modify Dataset to include Dummy Variables
00:06:22
  Outliers
00:01:47
  Prepare Test Dataset
00:02:46
  Install Libraries
00:01:35
  Build Basic Classifier
00:06:51
  Confusion Matrix for Training Dataset
00:01:24
  Confusion Matrix Errors
00:04:37
  Kappa Statistics
00:01:16
  Confusion Matrix for Test Dataset
00:05:03
  Change Probability Threshold
00:03:41
  Reduce Probability Threshold
00:03:41
  Cross Validation
00:08:23
  Debugging
00:00:58
  Decision Tree Model - Method rpart
00:17:43
  Decision Tree Model - Method C5.0
00:04:22
  Variable Importance
00:04:11
  Method C5.0 results
00:03:15
  Logistic Regression Model - Method gml
00:03:16
  Boosting Tree Model - Method bstTree
00:03:14
  Method bstTree results
00:03:12
  Decision Tree Model - Method C5.0Cost
00:01:12
  Method C5.0Cost results
00:01:03
  Decision Tree Model - Method C5.0Rules
00:01:21
  Decision Tree Model - Method treebag
00:02:28
  Method treebag results
00:00:51
  Decision Tree Model - Method xgbTree
00:02:18
  Method xgbTree results
00:00:57
  Comparing Accuracy and Kappa Levels
00:08:36
  Ensemble Methods - Random Forest
00:07:47
  Random Forest results
00:02:13
  Random Search
00:03:33
  Random Search results
00:01:57
  Grid Search
00:02:12
  Grid Search results
00:05:01
  Gradient Boosting Model
00:06:21
  Removing Outliers
00:07:23