🏏 IPL-Score-Predictor

This is a Flask-based web application that predicts the first innings score for an IPL match based on the venue, batting team, and bowling team.

🎥 Demo

Here’s a quick demo of the IPL-Score-Predictor:

🚀 Features

• User-friendly web interface built with HTML, CSS, and Flask.
• Machine Learning model trained on IPL data to predict match scores.
• Flask backend for handling requests and making predictions.

🛠️ Technologies/ Tools

• Jupyter Notebook / Google Colab
• Python 3+
• Python packages
  • Tensorflow - pip install tensorflow
  • Pandas - pip install pandas
  • Numpy - pip install numpy
  • Scikit-learn - pip install scikit-learn
  • Seaborn - pip install seaborn
  • Flask - pip install flask
• HTML5
• CSS3

📈 Data

Data used are from the IPL tournaments from 2008-2017.

You can download the data set used in this project here:

• IPL data.csv

📖 Setup Instructions

1. Clone the repository:

   git clone https://github.com/LasithaAmarasinghe/IPL-Score-Prediction.git

2. Navigate to the project directory:

   cd IPL-Score-Prediction

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask application:

   python app.py

5. Open 🌍 http://127.0.0.1:5000/ in your browser to access the website.

📝 How This Works

1. User Input

• The user selects:
  • Venue of the match
  • Batting Team
  • Bowling Team
• These inputs are provided through a web form.

2. Data Processing

• The selected inputs are encoded using pre-trained encoders.
• The encoded values are then scaled using a preloaded scaler to ensure the model receives properly formatted data.

3. Prediction

• The processed data is fed into the pre-trained machine learning model that predicts the score.
• The model outputs the estimated score based on historical IPL data.

4. Result Display

• The predicted score is displayed on the webpage.

📋 License

• This project is licensed under the MIT License. See the LICENSE file for details.