Customer Churn Prediction Using MLFlow and Streamlit

This session will guide you through every stage of the machine learning process, from data preprocessing and feature engineering to model training, pipeline construction, and deployment. The session will begin by preparing and transforming data to ensure it’s ready for model training. Next, dive into building scalable ML pipelines within MLFlow, where you’ll learn how to track experiments, monitor model performance, and manage version control. These features enable a streamlined and reproducible workflow, empowering both beginner and experienced practitioners to understand and implement best practices in ML pipeline management. After setting up the pipeline, Priyanka will focus on training high-performance classification models to predict customer churn. To make these predictions accessible and understandable, she will build a user-friendly interface with Streamlit, allowing participants to visualize churn predictions in real-time.