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🏏 IPL Winning Prediction 🏆 and Full Data Analysis

Contents in this Project

  1. Data Loading and Summary Checking
  2. Data Cleaning
  3. Feature Extraction
  4. EDA and Data Visualisation
  5. Best Player Clusters since 2008 based on Performance
  6. IPL Match Winning Prediction 🏆

Index of Contents

  1. Introduction
  2. Features
  3. Demo
  4. Deployed Link
  5. Directory Structure
  6. Technology Stack
  7. Installation
  8. Usage
  9. Contribution
  10. License

Introduction

This project aims to analyze IPL match data from 2008-2022 and make predictions based on the analysis. It includes an analysis notebook (Analysis.ipynb) for exploring the data and a prediction notebook (Prediction.ipynb) for developing a prediction model. The web application for predictions is built using Flask, HTML, and CSS.

Features

  • Analyze IPL match data from 2008 to 2022.
  • Develop and train prediction models based on historical data.
  • Deploy a Flask-based web application for interactive predictions.
  • Gain insights into team performance, player statistics, and match outcomes.
  • Make predictions on upcoming IPL matches.

Demo

IPL_Predictions

Deployed Link

The web application is deployed on Render. You can access it here.

Directory Structure

File/Folder Description
dataset Folder containing dataset files
├── balls_by_balls.csv CSV file containing ball-by-ball data
└── matches.csv CSV file containing match data
static Folder containing static files (e.g., CSS)
└── style.css CSS file for styling the web application
templates Folder containing HTML templates
├── index.html HTML template for the main page of the web app
└── result.html HTML template for displaying prediction results
Analysis.ipynb Jupyter notebook for IPL match data analysis
Prediction.ipynb Jupyter notebook for prediction model development
app.py Flask application file
requirements.txt File containing a list of required dependencies

Technology Stack

  • Python 🐍
  • Flask 🌐
  • HTML/CSS 🎨
  • joblib 🧠
  • scikit-learn 📊
  • pandas 🐼

Installation

  1. Clone the repository: 
    git clone https://github.com/neerajcodes888/IPL-Victory-Analysis-with-Predictions.git
cd IPL-Victory-Analysis-with-Predictions

Usage

  1. Run the Flask application: 
    python app.py
  2. Visit http://localhost:5000 in your web browser to access the web app.

Contribution

Contributions are welcome! If you'd like to contribute to this project, feel free to submit a pull request.

License

This project is licensed under the EPL 2.0