ChurnAI is a machine learning application designed to predict customer churn. It helps businesses analyze customer behavior and predict the likelihood of customers leaving, allowing them to take proactive measures. The project is built using Python, Streamlit for deployment, and various machine learning techniques to provide real-time predictions and visualizations.

• Accurate Churn Prediction: Uses Random Forest, providing over 85% accuracy in predicting customer churn.
• Data Analysis: Processes and analyzes large customer datasets, including feature scaling, encoding, and data balancing.
• Real-time Deployment: Hosted on Streamlit for live prediction, allowing businesses to input new data and get immediate insights.
• Visualization: Provides visualizations for better understanding of churn factors and model performance.

Check out the live demo of the project on Streamlit: ChurnAI Live Demo

• Feature Engineering: Adding more customer interaction data for richer predictions.
• Model Selection: Experimenting with other algorithms like XGBoost or Neural Networks to improve accuracy.
• Multi-user Interface: Allowing multiple businesses to use ChurnAI with separate datasets.