This project predicts the NBA Rookie of the Year (ROTY) for each season based on player statistics. A Random Forest model is trained using historical player data, and a Streamlit dashboard is used to visualize predictions. see a live demo here
Data Processing: Reads player statistics from multiple CSV files and calculates per-game metrics.
Machine Learning Model: Uses a Random Forest Classifier to predict the likelihood of a player winning ROTY.
Yearly Predictions: Ensures that every year has a predicted ROTY based on model probability.
Interactive Dashboard: Built with Streamlit to display predictions and trends.
This Python script scrapes NBA rookie data from Tankathon and retrieves player statistics from the NBA API. The data is saved in a CSV file for further analysis.
This script scrapes NBA rookie information from Tankathon for a given draft year and combines it with career stats from the NBA API. The final dataset, which includes rookies' draft positions and their statistics, is saved as a CSV file.
Ensure you have the following Python libraries installed:
pip install requests beautifulsoup4 pandas numpy nba_api streamlitThis project is a Streamlit-based web application that visualizes NBA rookie data. It loads and displays a dataset of NBA rookies along with their career stats and enhances the presentation by associating team logos with each player.