Predicting Customer Lifetime Value using ML

In this mini project, we will develop a machine learning model to predict Customer Lifetime Value (CLV), a key metric for businesses to gauge the total revenue a customer is expected to generate throughout their relationship with the brand. The project begins with data collection, using historical customer transaction data, demographic information, and engagement metrics. We will preprocess the data by handling missing values, encoding categorical variables, and normalizing numerical features. Next, we will explore various machine learning algorithms, such as linear regression, decision trees, and ensemble methods like random forests and gradient boosting, to identify the best-performing model. The model will be trained and validated using techniques like cross-validation to ensure robustness. Finally, we will evaluate the model’s performance using metrics such as Mean Absolute Error (MAE) and R-squared, and visualize the results to provide actionable insights for marketing strategies and customer retention initiatives. This project not only enhances understanding of CLV prediction but also demonstrates the practical application of machine learning in driving business decisions.