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README.md

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-license: mit
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+# 🏀 NBA Draft Data Analysis (1989–2021)
+
+##  Overview
+In this project, I analyzed NBA draft data from 1989 to 2021.  
+The goal was to understand what makes a player a **Top 10 draft pick**.
+
+This project included:
+- Data Cleaning
+- Outlier Detection
+- Descriptive Statistics
+- Visualizations
+- Research Questions and Insights
+
+Dataset source: [Kaggle – NBA Draft Basketball Player Data](https://www.kaggle.com/datasets/mattop/nba-draft-basketball-player-data-19892021)
+
+---
+
+##  Objective
+The main goal was to identify what characteristics separate Top 10 NBA draft picks from other drafted players.
+
+**Target variable:**  
+`top10` → 1 = drafted in Top 10, 0 = otherwise.
+
+---
+
+##  Step 1: Data Cleaning
+- Removed irrelevant columns like `player`, `team`, and `college`.
+- Checked for missing values and replaced numeric NaNs with the column mean.
+- Removed duplicate rows.
+
+ Final dataset is clean and ready for analysis.
+
+---
+
+##  Step 2: Outlier Detection
+- Used the **IQR (Interquartile Range)** method to remove extreme outliers.  
+- This helped to remove unrealistic values that could distort averages, like superstars with very high point-per-game stats.
+
+---
+
+##  Step 3: Descriptive Statistics
+- Calculated mean, median, and standard deviation for all numeric columns.
+- Found positive correlations between:
+  - `points_per_game` and `win_shares`
+  - `assists` and `total_rebounds`
+
+---
+
+##  Step 4: Research Questions and Visualizations
+
+### **Question 1:**  
+Do Top 10 draft picks score more points per game?
+
+**Answer:**  
+Yes — Top 10 picks have a higher median points per game, suggesting that scoring ability is an important factor for early draft selection.
+
+---
+
+### **Question 2:**  
+Do Top 10 picks play more games in their career?
+
+**Answer:**  
+Yes — Top 10 picks usually play more total games, showing they tend to have longer and more consistent careers.
+
+---
+
+### **Question 3:**  
+Do Top 10 picks contribute more to team wins?
+
+**Answer:**  
+Yes — Their `win_shares` values are generally higher, meaning they help their teams win more often.
+
+---
+
+##  Key Insights
+- Top 10 players **score more**, **play more games**, and **contribute more** to their teams.  
+- These factors show that high draft selections usually reflect player quality and potential.  
+- Teams generally make good choices with early draft picks.
+
+---
+
+##  Tools Used
+- Python (Pandas, NumPy)
+- Matplotlib, Seaborn
+- Google Colab / Jupyter Notebook
+- Hugging Face Datasets
+
+---
+
+##  Files Included
+- `nbaplayersdraft.csv` – dataset file  
+- `nba_draft_EDA.ipynb` – notebook with analysis and code  
+- `README.md` – this summary file  
+
+---
+
+##  Video Presentation
+A short 2–3 minute overview of the process and results.  
+ *(Add your video link here after uploading it)*