Batsmen Average Prediction

This project implements a batsmen average prediction system using web scraping, data preprocessing, data visualization, machine learning, and a Flask-based website for visualization.

Overview

The project follows these major steps:

1. Data Collection: Web scraping gathers cricket data from various online sources.
2. Data Preprocessing: The collected data is cleaned and preprocessed to ensure data quality and consistency.
3. Data Visualization: Visualizations are created to explore the data and identify correlations between the batting average and other features.
4. Machine Learning: Machine learning models are trained on the preprocessed data to predict batting averages.
5. Web Interface: A Flask-based web application is developed with a simple HTML template to showcase the prediction results.

Requirements

• Python 3.11.6
• Flask
• Pandas
• Matplotlib
• Scikit-learn
• BeautifulSoup (for web scraping)
• Joblib (for model persistence)
• HTML/CSS (for web interface)

Installation

1. Clone the repository:

   git clone https://github.com/your_username/batsmen-average-prediction.git

2. Install the required Python libraries:

   pip install -r requirements.txt

3. Run the Flask application:

   python app.py

4. Access the web interface at http://localhost:5000 in your web browser.

Usage

1. Navigate to the web interface and input the required data or upload a dataset.
2. Explore the visualizations to understand the data correlations.
3. Use the prediction feature to predict batsmen averages based on selected features.

Files Included

• app.py: Flask application for the web interface.
• crick_analysis.ipynb: Data preprocessing and visualization scripts.
• webscrap_site.ipynb: Webscraping the website to collect data script.
• templates/: HTML templates for the web interface.
• requirements.txt: List of required Python libraries.

Contributing

Contributions are welcome! Feel free to submit a pull request if you have any suggestions, bug fixes, or improvements.