I will deliver clv prediction, segmentation and actionable retention insights

I build custom AI-powered business intelligence solutions that combine advanced machine learning with interactive dashboards. My expertise covers Customer Lifetime Value (CLV) prediction, churn prediction, customer segmentation, and model explainability using SHAP/LIME.

With skills in Python, scikit-learn, XGBoost, Random Forest, Pandas, NumPy, Matplotlib, and Streamlit/Dash, I deliver end-to-end solutions that include:

Data preparation & feature engineering (aggregate transactions, clean data, create derived features).

Modeling & evaluation (Linear Regression, Random Forest, SVM, XGBoost with metrics like R², RMSE, precision, recall, F1-score, AUC).

Explainability & insights (feature contribution analysis with SHAP/LIME).

Customer segmentation (KMeans, DBSCAN, Gaussian Mixture Models with actionable profiles).

Interactive dashboards where clients input data and instantly see predictions, feature drivers, and visualizations.

Deliverables can include a Jupyter notebook with full analysis, a short business report with recommendations, and a working Streamlit/Dash app prototype ready to demo or integrate with your existing systems.