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https://github.com/md-emon-hasan/3-eda-basketball-ml-app

A ML application focused on EDA and basketball analytics, showcasing data visualization and insights using Python and relevant libraries.
https://github.com/md-emon-hasan/3-eda-basketball-ml-app

basketball-analysis csv data-visualization eda exploratory-data-analysis exploratory-data-analysis-eda ml-app

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A ML application focused on EDA and basketball analytics, showcasing data visualization and insights using Python and relevant libraries.

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README 

        
# 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](https://github.com/user-attachments/assets/5aad1622-d636-49c0-b53e-c00688306d30)



## 📋 Contents



- [Introduction](#introduction)

- [Why This Project](#why-this-project)

- [Dataset](#dataset)

- [Features](#features)

- [Models Implemented](#models-implemented)

- [Setup and Installation](#setup-and-installation)

- [Demo](#demo)

- [Contributing](#contributing)

- [Challenges Faced](#challenges-faced)

- [Lessons Learned](#lessons-learned)

- [License](#license)

- [Contact](#contact)



---



## 📖 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:



   ```bash

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

   ```



2. Navigate to the project directory:



   ```bash

   cd 3-Eda-Basketball-ML-App

   ```



3. Install the required dependencies:



   ```bash

   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](https://three-eda-basketball-ml-apps.onrender.com/)



---



## 🤝 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:**



   ```bash

   git checkout -b feature/new-feature

   ```



3. **Make your changes:**



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



4. **Commit your changes:**



   ```bash

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

   ```



5. **Push to the branch:**



   ```bash

   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](LICENSE) file for more details.



---



## 📬 Contact



- **Email:** [[email protected]](mailto:[email protected])

- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)

- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)

- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)

- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)



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



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