naishasinha/Fantasy-Basketball

# NBA Fantasy Basketball Assistant *** 🏀 _A full-stack AI application for fantasy basketball enthusiasts._ 🏀 ![9ed13d1846a5f262edaea59c29483c02](https://github.com/naishasinha/NBA-Fantasy-Assistant/assets/117387359/b33e4381-c769-42e4-b8f5-d794aebb213b) *** ## Overview

The NBA Fantasy Basketball Assistant is a full-stack AI application designed to provide in-depth analysis and actionable insights for fantasy basketball enthusiasts. Leveraging detailed NBA game statistics, this tool aims to enhance users' decision-making by predicting team performances, analyzing matchups, and offering personalized recommendations.

## Core Features

### 📊 Team Performance Analysis → **Historical Data Insights**: _Analyze historical data over the last nine seasons to predict future team performances._
→ **Current Form**: _Calculate rolling averages of key statistics over recent games to determine current form and identify trends._ ### 🏆 Matchup Analyzer → **Historical Matchup Performance**: _Analyze historical matchups between teams to predict outcomes and evaluate matchup strengths and weaknesses._
→ **Performance Trends Against Specific Opponents**: _Highlight performance trends against specific types of opponents (e.g., teams with strong defense or high-scoring offenses)._ ### 📝 Customizable Recommendations → **User Criteria-Based Recommendations**: _Allow users to prioritize specific statistics and receive tailored recommendations._

*** ## Project Details ### Development Progress: This project is designed for continuous enhancement and improvement. Therefore, a chart has been added to track the development and completion progress of each project element, highlighting the future updates I plan to implement for improvement.
| Component | Progress | | -------- | ------- | | App Interface (Full-Stack) | ✅ Completed UI/UX Design Implementation
✅ Client successfully displays and interacts with server mock data
✅ Seamlessly implements dynamic internal page changes | | Machine Learning Prediction Model | ✅ Initial model complete with 63% accuracy
⚠️ Working to [improve current accuracy](https://github.com/naishasinha/Fantasy-Basketball/issues/1) | | Integration of ML Results and Pandas Manipulation Data with Server | ⚠️ In Progress | | 📅 **Current Task** 📅 | | -------- | | Develop a JSON file that generates up-to-date performance and matchup analysis based on the mock data format. |
### Noteworthy Description Files:

ML-Model	ML-Model README.md file: Provides details and explanations behind the web scraping and predictor model training process
Client	Client README.md file: Provides details on navigating and utilizing the React App

### Full Directory Tree:

``` nba-fantasy-assistant/ ├── ML-model/ │ ├── README.md │ ├── Create_NBADataset.ipynb # Code for creating CSVs (Jupyter Notebook) │ ├── Retrieve_NBAData.ipynb # Web Scraping Code (Jupyter Notebook) │ ├── NBA_PredictionModel1.ipynb # Base ML Model │ ├── nba_data/ │ │ ├── .ipynb_checkpoints/ │ │ ├── scores/ # All individual box scores across seasons │ │ ├── standings/ # All standings (by month) across seasons │ │ ├── ... # Other miscellaneous csv files ├── client/ │ ├── public/ │ ├── src/ │ │ ├── components/ │ │ │ ├── Form.css │ │ │ ├── Header.css │ │ │ ├── Modal.css │ │ │ ├── Modal.js │ │ │ ├── NavButton.css │ │ │ ├── NavButton.js │ │ ├── images/ # All images used for app │ │ ├── pages/ │ │ │ ├── Home.js # Main (Home) Page │ │ │ ├── Home.css │ │ │ ├── TeamPerformanceAnalysis.js │ │ │ ├── MatchupAnalyzer.js │ │ │ ├── FantasyRecommendations.js │ │ ├── App.js │ │ ├── index.js │ │ ├── index.css │ ├── package.json │ ├── README.md # React App Explanation ├── server/ │ ├── index.js │ ├── package.json ├── README.md # Main Project File README ├── LICENSE ```

### Tech Stack:

**Other tools for ML Model:** `NumPy` `Pandas` `Scikit-learn` `BeautifulSoup`
### Running the Application: Main Directory Command: `cd NBA-Fantasy-Assistant`

#### Front-End 1. Navigate to the `client` directory: ``` cd client ``` 2. Install dependencies: ``` npm install ``` 3. Start the React App: ``` npm start ``` #### Back-End 1. Navigate to the `server` directory: ``` cd server ``` 2. Install dependencies: ``` npm install ``` 2. Start the server: ``` node index.js ```

naishasinha / Fantasy-Basketball

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