IPL Match Winner Prediction Project 🏏

Overview

This project predicts the winning probabilities of the teams playing in an IPL (Indian Premier League) match using key match parameters. It leverages Logistic Regression, achieving an accuracy of 81.2%.

Features

The model takes the following inputs to generate predictions:

1. Batting Team: The team currently batting.
2. Bowling Team: The team currently bowling.
3. Host City: The city where the match is being played.
4. Target Score: The total runs the batting team needs to win.
5. Current Score: Runs scored by the batting team so far.
6. Overs Completed: Number of overs bowled in the innings.
7. Wickets Out: Number of wickets lost by the batting team.

Dataset

The dataset used for this project was sourced from Kaggle:
IPL 2008 to 2021 All Match Dataset.

It contains detailed match data spanning IPL seasons from 2008 to 2021. The dataset was cleaned and preprocessed before being used to train the model.

Approach

1. Data Preprocessing:

   • Cleaned and prepared the dataset.
   • Applied feature engineering to extract useful attributes like runs left, balls left, and current/required run rates.
   • One-hot encoded categorical features (Batting Team, Bowling Team, and Host City).

2. Model Training:

   • Utilized Logistic Regression for classification.
   • Split the data into training and testing sets.
   • Evaluated the model's performance using metrics like accuracy.

3. Prediction:

   • The model predicts probabilities for each team:
     • Batting Team Winning Probability
     • Bowling Team Winning Probability

Results

• Model Accuracy: 81.2% on the test dataset.
• The model effectively predicts match outcomes based on the given inputs, providing insights into match scenarios.

Website 🌐

Explore the live version of this project and predict match outcomes directly on the website:
IPL Match Winner Prediction

Repository Contents

This repository includes:

1. Jupyter Notebook: Contains the entire codebase for data processing, model training, and evaluation.
2. CSV Files: Datasets used to train and test the model.

How to Use

Open the project in GitHub Codespaces for a seamless development experience:

• Navigate to your repository on GitHub.
• Click the Code button and select Codespaces.
• Use the pre-configured environment to run the Jupyter Notebook and analyze the results.