What is CLV or LTV? CLV or LTV is a metric that helps you measure the customer's lifetime value to a business. In this kernel, I am sharing the customer lifetime value prediction using BG-NBD, Pareto, NBD & Gamma Model on top of RFM in Python.

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Machine Learning Web application created using Python Flask for Customer Lifetime Value Prediction

Customer Lifetime Value(CLV) prediction using Python, RFM analysis and Random Forest Algorithm.

End-to-end Customer Lifetime Value (CLV) prediction using RFM feature engineering and regression modeling in Python.

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.

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Customer Lifetime Value Prediction using Python

Customer Lifetime Value (CLTV) prediction model using Python (Lifetimes)

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Customer Lifetime Value Prediction Model project using Python

Customer Lifetime Value (LTV) Prediction using Python and ML

Customer Lifetime Value (CLV) Prediction using MySQL, Python, Pandas, and Scikit-Learn

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

End-to-end customer lifetime value prediction and revenue optimization system using SQL, Python, ML, Power BI, and Streamlit

Customer churn prediction and survival analysis using Python, Machine Learning, and an interactive web app to estimate churn risk and customer lifetime value.

End-to-end Customer Lifetime Value prediction and segmentation project using the Olist e-commerce dataset (Python, Scikit-Learn, XGBoost).

Built a Customer Lifetime Value prediction model using MySQL and Python, performed ETL and feature engineering, trained regression models, and evaluated customer profitability to support data-driven marketing decisions.

Identifying R$5.3M in At-Risk Revenue: Full-Cycle E-Commerce Customer Churn Prediction & Lifetime Value Analysis using Python, SQL, Scikit-Learn, XGBoost, and RFM Segmentation

Advanced Customer Behavior Analytics Intelligence Platform powered by Google Analytics 4. Real-time customer segmentation, lifetime value optimization, churn prediction, and journey analytics using advanced SQL and Python visualizations for senior Data Analyst positions.

Customer Lifetime Value (CLV) prediction using Python: Estimates future customer revenue from real-world retail transaction data using BG/NBD and Gamma-Gamma models. Includes CLV segmentation, visualizations, and business insights to support data-driven marketing and retention strategies.

Customer Lifetime Value (CLV) Prediction project using Python and machine learning. The model analyzes customer purchase behavior, performs data preprocessing and feature engineering, and predicts future revenue value. Helps businesses identify high-value customers, improve retention strategies, and optimize marketing decisions.

End-to-end Customer Lifetime Value (CLV) Prediction for the Retail & E-Commerce sector using transactional and behavioral data. Built with Python, MS SQL, Power BI, Azure, and ML models.

End-to-end financial analytics pipeline built in Python. Covers revenue forecasting with ARIMA and Prophet, churn prediction using machine learning, cohort retention analysis, RFM segmentation, and customer lifetime value analysis on SaaS business data.

An end-to-end Python project performing RFM-based customer segmentation and lifetime value prediction using synthetic retail data. Features automated ETL, K-Means clustering, churn modeling, and an interactive Streamlit dashboard for business insights.

Customer segmentation engine using RFM analysis, K-Means clustering, and probabilistic CLV prediction (BG/NBD + Gamma-Gamma). Identifies high-value customers, predicts churn risk, and recommends marketing actions per segment. Built with Python, scikit-learn, lifetimes, Plotly, and Streamlit. UCI Online Retail Dataset.