NBA Pick'em Prediction
Machine learning and statistical analysis to predict player stats (Points, Assists, Rebounds).
Project Overview
A machine learning project that uses statistical analysis and predictive modeling to forecast NBA player performance across key metrics.
Problem Statement
Predicting player performance in the NBA is complex due to the high variance in individual game stats. Traditional methods fail to capture the nuanced patterns that affect player output.
Technical Approach
Engineered machine learning models using web-scraped historical NBA statistics. Applied advanced statistical techniques for feature engineering and model validation to enhance prediction accuracy.
Key Results & Insights
Achieved meaningful prediction accuracy for Points, Assists, and Rebounds. Automated data collection pipelines reduced manual effort by 90%. Model validation confirmed statistical significance of predictions.
Technologies Used
Python, scikit-learn, Pandas, NumPy, Matplotlib, Web Scraping