Predict the outcome of an Indian Premier League (IPL) match using machine learning and data analysis. This project processes historical IPL match data, extracts key features, and applies ML models to predict match results based on various in-game parameters.
✅ Data Preprocessing & Feature Engineering from IPL datasets
✅ Handling Missing Values and normalizing team names for consistency
✅ Crucial Match Metrics Calculation (e.g., Current Run Rate (CRR), Required Run Rate (RRR), wickets left, balls left, etc.)
✅ Machine Learning Models Applied: Logistic Regression
✅ Performance Evaluation using accuracy scores and probability predictions
✅ Interactive Match Progression Analysis for insights
The project utilizes historical IPL match data from CSV files, including:
📌 Match details: Teams, location, winner, scores, etc.
📌 Ball-by-ball delivery data
- Encoding categorical features (teams, venues)
- Handling missing data
🏏 Runs left, balls left, wickets, total runs, CRR, RRR
🎯 Logistic Regression model using sklearn.pipeline for data transformation and classification
📊 Achieves ~80% accuracy in predicting match outcomes
🔢 Determines winning probability at different match stages