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