Developed a web-based application to predict customer churn using an Artificial Neural Network (ANN) built with TensorFlow. The app, deployed on Render using Streamlit, enables real-time predictions through an interactive user interface, allowing businesses to identify at-risk customers and optimize retention strategies. Key features include data preprocessing, model training, and dynamic visualizations. Technologies used: Python, TensorFlow, Keras, Streamlit, Pandas, NumPy, Scikit-learn, and Render for cloud deployment. Achieved [e.g., 85% model accuracy, if applicable]. Explore the live app: https://customer-churn-modelling-using-ann.onrender.com