clv-prediction

Real-time predictive modeling pipeline for time-series data analysis and automated forecasting.

End-to-end AI-powered ecommerce analytics platform combining Machine Learning, Deep Learning, NLP, Recommendation Systems, Time Series Forecasting, and a RAG chatbot with FastAPI, Streamlit, Docker, and MLflow.

End-to-end retail analytics project transforming raw transaction data into structured insights, customer segmentation, and machine learning–based Customer Lifetime Value (CLV) predictions, delivered through an interactive Power BI dashboard.

Customer value intelligence and retention analytics platform with CLV prediction, RFM segmentation, monitoring, reporting, and executive dashboards.

A data science project that builds a predictive model to estimate Customer Lifetime Value (CLV) using customer transaction data, enabling businesses to improve customer retention and targeted marketing.

Dashboard built in streamlit for customer behaviour analysis, covering RFM, CLV and more

End-to-end e-commerce customer marketing and campaign ROI analytics system built with SQL, Python, Excel and Power BI

Scalable e-commerce customer analytics pipeline using Apache Spark for CSC1142 assignment.

Predictive analytics project using R to estimate customer lifetime value (CLV) and churn risk from RFM segmentation results. Supports data-driven retention and marketing strategies.

A React dashboard that retrieves US stock market data and generates CLV-based BUY, HOLD, and TRIM signals for a customizable stock universe.

Customer segmentation and 90-day CLV prediction using RFM analysis, K-Means clustering, and machine learning on e-commerce transaction data.

RFM customer segmentation & 3-month CLV prediction on the UCI Online Retail dataset — Excel cleaning, Python (K-Means + Linear Regression), Power BI dashboard.

An end-to-end data science pipeline predicting 90-day Customer Lifetime Value and churn risk using BG/NBD and Gamma-Gamma models, deployed to an interactive Tableau dashboard.

End-to-end Customer Lifetime Value (CLV) and RFM segmentation pipeline using BG-NBD & Gamma-Gamma models, with AI-generated customer insights and interactive dashboards built on synthetic fintech data.

An end-to-end data analytics and machine learning case study combining analytics, segmentation, churn prediction, and CLV forecasting on 397K+ retail transactions and 4,338 customers.

Machine learning project to predict Customer Lifetime Value (CLV) and segment customers into high, medium, and low value groups. Includes automated feature engineering and regression models.

Advanced E-Commerce Customer Lifetime Value (CLV) Prediction using BG/NBD and Pareto/NBD Models with RFM Analysis and K-Means Segmentation

Bayesian Customer Lifetime Value Survival & Real-Time Price Elasticity Co-Optimizer. Contextual Thompson Sampling over pricing frontiers coupled with Weibull/Pareto-NBD churn hazard models to maximize enterprise Net Present Value (NPV).