Project StreamGuard | OTT Customer Churn Prediction & Customer Lifetime Value Using Machine Learning

Welcome to my major project presentation on Project StreamGuard — An End-to-End Machine Learning Pipeline for OTT Customer Churn & Revenue Retention Intelligence. My name is Eshan Maity, and in this video, I demonstrate a machine learning pipeline designed to predict customer churn and estimate Customer Lifetime Value (CLV) for an OTT platform. Project Highlights: Dataset generation and data exploration Data cleaning and missing-value handling IQR-based anomaly handling and outlier clipping Feature engineering and feature selection using RFE Training and comparison of machine learning classification models Model evaluation using accuracy, precision, recall, F1-score, and ROC-AUC Hyperparameter tuning using RandomizedSearchCV Customer Lifetime Value prediction using regression models Churn risk ranking and model saving using Joblib Technologies Used: Python, NumPy, Pandas, Matplotlib, and Scikit-learn. Important Note: This project uses a synthetic dataset containing 800 subscriber profiles. The results demonstrate the machine learning workflow on this generated dataset and do not represent validated performance on real-world OTT customer data. Project Title: Project StreamGuard — An End-to-End Machine Learning Pipeline for OTT Customer Churn & Revenue Retention Intelligence Presented by: Eshan Maity Thank you for watching! #MachineLearning #PythonProject #CustomerChurnPrediction #DataScience #MajorProject #CustomerLifetimeValue #ScikitLearn