Customer Churn Prediction — Machine Learning Project

Built a predictive machine learning model to analyze customer churn patterns using structured data, focusing on feature engineering, exploratory analysis, and predictive modeling to support business retention strategies.

PROJECTS

Chandan N

2/27/20261 min read

Problem
Customer churn directly impacts revenue, but identifying high-risk customers requires structured data analysis and predictive modeling.

Objective
Develop a predictive analytics workflow to:

Identify churn-driving factors
Analyze customer behavior trends
Build predictive churn model
Support data-driven retention decisions

What I Did?

Performed exploratory data analysis (EDA) on customer datasets
Cleaned and prepared data for modeling
Built and optimized Random Forest predictive model
Evaluated model performance and feature importance

Key Analyses

Customer behavior trend analysis
Feature importance evaluation
Churn prediction modeling
Data visualization for business insights

Tools & Technologies
Python, Pandas, NumPy, Machine Learning, Data Visualization

Outcome
Delivered a predictive churn model achieving ~50% recall, demonstrating practical ML workflow from data analysis to business insight generation.