IPL-Cricket-ML-Analysis

Machine Learning-based data analysis on IPL dataset

🏏 IPL Cricket Data Analysis using Machine Learning

This project performs in-depth analysis of IPL (Indian Premier League) data using Python, Pandas, and visualization libraries. It focuses on team strategies, player performance, and match statistics. The analysis helps in extracting insights and patterns using real match data.

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📁 Datasets Used

• matches.csv: Contains match-level information (season, venue, winner, toss, etc.)
• deliveries.csv: Ball-by-ball delivery details (batsman, bowler, runs, dismissals, etc.)

Both datasets are sourced from Kaggle IPL Dataset.

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🛠 Technologies Used

• Python (Jupyter / Google Colab)
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn (if ML model is applied)

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🔍 Features Covered

• Total matches per season and winners
• Most successful teams and players
• Toss analysis: Does winning toss impact match result?
• Run rate comparisons
• Player strike rate and economy analysis
• Top scorers, wicket-takers, consistent performers
• (Optional) Match outcome prediction using machine learning

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📊 Sample Visualizations

Matches per season

![Matches per Season]https://github.com/rejothomas1/IPL-Cricket-ML-Analysis/raw/main/matches_per_season.png

Top Batsman

![Top Batsmen]https://github.com/rejothomas1/IPL-Cricket-ML-Analysis/raw/main/top_run_scorers.png

Toss vs Win

![Toss vs Win]https://github.com/rejothomas1/IPL-Cricket-ML-Analysis/raw/main/toss_vs_win.png

Top Bowler

![Top bowler]https://github.com/rejothomas1/IPL-Cricket-ML-Analysis/raw/main/top_wicket_takers.png

Feature importance matrix

![feature importance]https://github.com/rejothomas1/IPL-Cricket-ML-Analysis/raw/main/feature_importance_rf.png

Confusion matrix

![confusion matrix]https://github.com/rejothomas1/IPL-Cricket-ML-Analysis/raw/main/confusion_matrix_winner_prediction.png

📌 How to Run

1. Clone this repo or open ipl_analysis.ipynb in Google Colab
2. Upload matches.csv and deliveries.csv into your session
3. Run all cells to view insights and visualizations

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📂 Files Included

File	Description
ipl_analysis.ipynb	Main analysis notebook
matches.csv	IPL match-level dataset
deliveries.csv	Ball-by-ball delivery-level dataset
README.md	Project documentation
screenshots/	(Optional) Visualization screenshots

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📄 License

This project is licensed under the MIT License – feel free to use and modify.

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🙋‍♂ Author

Rejo Thomas
BCA Student | Machine Learning Enthusiast
GitHub Profile