IPL Match Win Predictor

IPL Match Win Predictor is a Streamlit web app that predicts the winning probability of an IPL team during a run chase. The app uses a trained machine learning pipeline saved in pipe.pkl and takes live match inputs such as batting team, bowling team, city, target, score, overs, and wickets.

Features

• Predicts win probability for the batting and bowling teams.
• Simple Streamlit user interface.
• Uses match situation inputs from the second innings.
• Loads a pre-trained machine learning model from pipe.pkl.
• Includes IPL teams and host cities supported by the trained model.

Project Structure

IPL-Match-Win-Predictor/
|-- app.py
|-- pipe.pkl
|-- matches.csv
|-- deliveries.csv
|-- ipl-win-predictor.ipynb
|-- requirements.txt
`-- README.md

Requirements

• Python 3.10 or newer
• pip
• Virtual environment support through venv

Python 3.13.5 was used during local setup.

Setup

Open PowerShell in the project folder and run:

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -r requirements.txt

If PowerShell blocks activation scripts, run this command in the same terminal:

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

Then activate the virtual environment again:

.\.venv\Scripts\Activate.ps1

Run The App

Start the Streamlit app with:

streamlit run app.py

Open the local URL shown in the terminal. It is usually:

http://localhost:8501

Keep the terminal open while using the app. To stop the server, press Ctrl+C.

How To Use

1. Select the batting team.
2. Select the bowling team.
3. Select the host city.
4. Enter the target score.
5. Enter the current score.
6. Enter completed overs.
7. Enter wickets lost.
8. Click Predict Probability.

The app will show the predicted winning probability for both teams.

Model And Data

• pipe.pkl contains the trained prediction pipeline.
• matches.csv and deliveries.csv contain IPL match and ball-by-ball data.
• ipl-win-predictor.ipynb contains the notebook used for data exploration, feature engineering, and model training.

Troubleshooting

If dependencies are missing, reinstall them:

python -m pip install -r requirements.txt

If streamlit is not recognized, make sure the virtual environment is active:

.\.venv\Scripts\Activate.ps1

If the model fails to load, verify that pipe.pkl is present in the same folder as app.py.