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license: mit
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# 🏀 NBA Player Performance Predictor (Assignment #2)
## 🎥 Project Presentation
[## Click here to Watch the Video ](https://youtu.be/MlvFNjFf5ZI)
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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. |