AI System for Customer Churn Prediction using Machine Learning

Project Overview

This project focuses on predicting customer churn in subscription-based businesses using Machine Learning techniques

The goal is to identify customers who are likely to leave, enabling companies to take proactive retention actions and improve customer lifetime value

Problem

Customer churn represents a major challenge for subscription-based businesses, as losing customers directly impacts revenue and increases acquisition costs

Early prediction helps businesses retain customers and improve long-term profitability

Approach

Data preprocessing (handling missing values, encoding categorical variables, binary mapping)

Exploratory Data Analysis (EDA) to understand churn patterns and customer behavior

Feature engineering and selection to improve model performance

Training a Random Forest Classifier

Hyperparameter tuning using GridSearchCV

Evaluation on unseen test data

Results

Accuracy: 93%

High precision, recall, and F1-score

Strong generalization on unseen data

Clear identification of key churn drivers

Business Impact

This model helps businesses identify at-risk customers early and apply targeted retention strategies, reducing churn and increasing customer lifetime value

Tools & Technologies

Python – Pandas – NumPy – Scikit-learn – Machine Learning-XGboost