data visualization, customer segmentation, CLV and next purchase prediction

The second iteration of Cuana, an E2E customer analytics solution for churn/CLV prediction, segmentation & lead scoring

A project to predict and cluster Customer Lifetime Value (CLV) using SQL, Python, and Machine Learning

Customer analytics project with segmentation and CLV prediction

E-Commerce Customer Segmentation & CLV Analysis - RFM Segmentation, Customer Lifetime Value Prediction

Customer Lifetime Value (CLV) prediction and customer segmentation using RFM analysis and machine learning.

E-commerce customer analytics — RFM segmentation, churn prediction, SHAP, CLV forecast, Plotly Dash · Python · SQL

End-to-end Customer Intelligence System using RFM Segmentation, Churn Prediction, and CLV Modeling with an interactive Streamlit dashboard.

Customer Lifetime Value (CLV) Prediction Model Internship Project – Predicting Customer Lifetime Value using Random Forest. Includes segmentation, visualization, and performance metrics. Internship project – Elevate Labs, MSME.

End-to-end ML system for telecom customer churn prediction with XGBoost, SHAP explainability, customer segmentation, CLV estimation, survival analysis, and an interactive Streamlit dashboard (ChurnShield AI)

End-to-end Customer Lifetime Value (CLV) prediction system using RFM features, BG/NBD, Gamma–Gamma, and XGBoost models on the UCI Online Retail dataset, with customer segmentation for marketing optimization.

An end-to-end customer analytics project using the Online Retail II dataset. This work features RFM segmentation, churn prediction with XGBoost, Customer Lifetime Value (CLV) forecasting with BG/NBD & Gamma-Gamma models, and statistical A/B testing.

A data exploration and analysis project focused on customer shopping behavior. This project uses data analysis techniques such as customer segmentation, predictive modeling, and customer lifetime value (CLV) prediction. The analysis is performed using Python, with libraries like Pandas, Matplotlib, Seaborn, and Scikit-learn.

📊 Titanic Lifetime Value Prediction – ML & Analytics A machine learning pipeline to analyze and predict Customer Lifetime Value (CLV) using XGBoost Regression. Includes data cleaning, feature engineering, EDA, and segmentation to uncover key customer insights. 🚀 Tech: Pandas, Seaborn, XGBoost, Sklearn

End-to-end Customer Lifetime Value (CLV) Prediction & Retention Analytics System built with Python, XGBoost, and Streamlit — includes RFM segmentation, cohort analysis, persona insights, model monitoring, drift detection, logs analytics, and automated executive summary reporting.

End-to-end MLOps pipeline for e-commerce customer analytics. It uses the Online Retail II dataset to run RFM segmentation, churn prediction, and CLV modeling on Spark. Airflow orchestrates the workflow, MLflow tracks experiments and models, DVC versions data, and Streamlit provides an interactive UI—services are containerized with Docker.

Customer Insights, RFM Segmentation & CLV Prediction

End-to-end customer segmentation, SQL exploration, and 4-week CLV forecasting pipeline using RFM, K-Means, BG/NBD + Gamma-Gamma models, with Tableau dashboard integration.

Customer segmentation and lifetime value prediction project using SQL, Python, and Power BI.

No description available

Self project on Customer segmentation and Customer Lifetime Value prediction

Machine Learning project for customer segmentation and Customer Lifetime Value (CLV) prediction using RFM analysis, K-Means clustering, and Random Forest regression.

Customer Segmentation, Churn Prediction & CLV using ML

No description available

This project segments customers and predicts 6-month CLV using UCI Online Retail II data to deliver business insights, identify high-value customers, and forecast revenue.

segmenting customers and clv prediction

No description available

Customer Segmentation & CLV Prediction using RFM, KMeans, DBSCAN, and XGBoost with PCA visualizations

End-to-end data science project to predict 6-month customer lifetime value using RFM features, machine learning, and interactive dashboards (Power BI & Streamlit).

Customer segmentation and CLV prediction using RFM & clustering