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README.md

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-license: mit
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+license: mit
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+# 🏀 NBA Player Performance Predictor (Assignment #2)
+
+## 🎥 Project Presentation
+## Attached video in HG
+
+---
+
+## 📌 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.