Predictive Customer Churn Analysis & Machine Learning Model

Developed an end-to-end machine learning solution designed to analyze customer behavior patterns and accurately predict potential churn for subscription-based services.

Key Features & Work Carried Out:

Data Preprocessing & EDA: Cleaned raw data, handled missing values, encoded categorical features, and applied feature scaling using Pandas and NumPy.

Model Building: Evaluated multiple classification algorithms (Logistic Regression, Random Forest, XGBoost) to identify optimal performance.

Hyperparameter Tuning: Fine-tuned model parameters using GridSearchCV, optimizing for ROC-AUC and Recall metrics.

Evaluation & Insights: Generated feature importance visualizations and confusion matrices to provide actionable business retention strategies.

Results: Achieved 89% predictive accuracy in identifying at-risk customers before churn occurs