Build a Customer Churn Prediction Model for Insurance Domain

Build a Customer Churn Prediction Model for Insurance Domain

Machine Learning Project in R -Predict which customers will leave an insurance company in the next 12 months.


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What will you learn

Understanding the problem statement
Importing a training dataset and testing from AWS
Installing necessary libraries and understanding its use
What is churning
Logistic regression, Random Forest, Decision Trees, Neural Network, and SVM
Terminology associated with Decision Tree and Random forest
How to decide to create a split in a decision tree
GINI Index, Chi-Square, Entropy and Variance
Feature engineering
Visualizing variables by density plot
Transforming features into binary variables
Gini coefficient and how to adjust gini weights for better results
Calculating the weighted entropy for all the splitting nodes and selecting the variable with a minimum weighted entropy
Applying Logistic Regression
Using the ROC curve to visualize the efficiency of the model
Applying ensemble model Random Forest Classifier
Applying boosting model Gradient Boosting Classifier
Selecting the best model for final predictions

Project Description

Understanding customer loyalty is an important part of any business. The ability to predict ahead of time when a customer is likely to churn can enable early intervention processes to be put in place, and ultimately a reduction in customer churn.  This machine learning project will find a solution for predicting which existing customers of an insurance company will leave in 12 months time, and when.

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Curriculum For This Mini Project

01h 30m
02h 47m