Project Title: IPL Score Prediction Using Machine Learning
Description: Our project focuses on developing a machine learning model to predict the scores in Indian Premier League (IPL) matches. The goal is to analyze historical data and forecast future scores based on match situations such as overs, wickets, and player performance. By leveraging past IPL data, we aim to provide accurate and insightful predictions that can be beneficial for sports analysts, cricket enthusiasts, and fantasy league players.
Technologies Used:
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Programming Language: Python
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Libraries:
Pandas and NumPy for data manipulation and analysis.
Matplotlib and Seaborn for data visualization.
Scikit-learn for building and evaluating machine learning models.
- Machine Learning Algorithms:
Linear Regression for continuous score prediction.
Random Forest and Gradient Boosting for improving model accuracy.
- Data Handling:
CSV files for storing and processing past IPL data.
Dataset: We used a CSV dataset containing historical IPL scores, including details like runs scored, overs played, wickets fallen, and other match-specific data. The dataset was thoroughly cleaned, preprocessed, and split into training and testing sets to ensure robust model performance.
This project showcases the application of machine learning in sports analytics and highlights the power of data-driven decision-making in cricket.