IPL Win Predictor

• Tech Stack: Flask, Machine Learning, Scikit-learn
• Website URL: Live Link
• Github URL: GitHub Link

• Developed a predictive model using Logistic Regression and Random Forest to forecast match outcomes with 85% accuracy.
• Curated and preprocessed a custom dataset generated via LLM prompt engineering to simulate realistic match scenarios.
• Deployed an interactive Flask UI to visualize real-time win probabilities based on dynamic in-game parameters.
• Utilized Pickle for model serialization, enabling efficient loading and inference within the production environment.