MatanYehudaDataAnalyst/NBAanalysis

🏀 NBA Player Performance Predictor (Assignment #2)

🎥 Project Presentation

## Click here to Watch the Video

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📌 Overview

This project analyzes NBA player data (1950-2017) to predict player performance based solely on physical attributes (Height, Weight, Age).

• Goal 1 (Regression): Predict exact points scored per season.
• Goal 2 (Classification): Classify players as "High Scorers" vs. "Low Scorers."

🔧 Feature Engineering

We engineered several features to improve model performance:

• Clustering: Used K-Means to identify 5 distinct body types (e.g., "Small Guard" vs. "Heavy Center").
• Scaling: Standardized Height and Weight to compare players fairly.
• Encoding: Converted player positions into numeric data.

🏆 Model Results

Part 1: Regression (Predicting Points)

• Winner: Gradient Boosting Regressor
• R² Score: 0.075
• Insight: Physical attributes alone are weak predictors of exact scoring numbers.

Part 2: Classification (High vs. Low Scorer)

• Winner: Support Vector Machine (SVM)
• Recall: 64%
• Insight: The SVM model was the best at identifying "Hidden Gems" (High Recall), minimizing the chance of missing out on talent.

📂 Files Included

• winning_model.pkl: The trained Gradient Boosting Regressor.
• winning_classifier.pkl: The trained SVM Classifier.
• My_Notebook.ipynb: The complete Python code for this analysis.