Skip to content

Latest commit

 

History

History
162 lines (98 loc) · 4.6 KB

README.md

File metadata and controls

162 lines (98 loc) · 4.6 KB
Raw

Machine Learning Project: Exploratory Data Analysis (EDA) Basketball App

Welcome to the EDA Basketball App machine learning project repository! This project focuses on performing exploratory data analysis on basketball data and implementing machine learning techniques for insights and predictions related to the sport.

3

📋 Contents

📖 Introduction

This repository contains a machine learning project focused on exploring basketball-related data to uncover trends, patterns, and predictive insights using statistical analysis.

🎯 Why This Project

The primary motivation behind creating this project is to analyze basketball data to gain insights into player performance, team strategies, and game outcomes, which can inform coaching decisions and enhance fan engagement.

📊 Dataset

The dataset used for this project contains comprehensive basketball statistics, including player performance metrics, team statistics, game results, and other relevant data points.

🌟 Features

  • Data Exploration: Exploring and visualizing basketball data to understand distributions, correlations, and trends.
  • Statistical Analysis: Performing statistical tests and analysis to uncover significant patterns and relationships in the data.
  • Visualization: Creating interactive visualizations and dashboards to present insights and predictions effectively.

🧠 Models Implemented

Several machine learning models and techniques were explored and implemented, including:

  • Time Series Analysis for tracking team performance trends over seasons.

Each model's performance was evaluated based on relevant sports analytics metrics and benchmarks.

🚀 Setup and Installation

To run this project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/Md-Emon-Hasan/3-Eda-Basketball-ML-App.git

  2. Navigate to the project directory:

    cd 3-Eda-Basketball-ML-App

  3. Install the required dependencies:

    pip install -r requirements.txt

  4. Explore the Jupyter notebooks or run the Python scripts to interact with the data and models.

🌐 Demo

Explore the live demo of the project here

🤝 Contributing

Contributions to enhance or expand the project are welcome! Here's how you can contribute:

  1. Fork the repository.

  2. Create a new branch:

    git checkout -b feature/new-feature

  3. Make your changes:

    • Implement new features, improve data visualization, or enhance model accuracy.

  4. Commit your changes:

    git commit -am 'Add a new feature or update'

  5. Push to the branch:

    git push origin feature/new-feature

  6. Submit a pull request.

🛠️ Challenges Faced

During the development of this project, the following challenges were encountered:

  • Handling and cleaning large-scale sports datasets for analysis.
  • Integrating diverse data sources to enrich the analysis and predictions.
  • Interpreting and presenting complex statistical insights in an understandable manner.

📚 Lessons Learned

Key lessons learned from this project include:

  • Importance of domain knowledge in sports analytics and data interpretation.
  • Application of statistical techniques and machine learning models in sports forecasting.
  • Visualization strategies for effectively communicating insights to stakeholders.

📄 License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

📬 Contact

Feel free to reach out for any questions or feedback regarding the project!

Feel free to customize this template further to better fit your project's specific details and style preferences.