This PR submits my solutions for the Data Science Club's Task 1 (Data-Driven Insights & Visualization) and Task 2 (Real-World ML Pipeline) for technical team selection.

• Task 1: Analyzed NBA scoring leaders (1992-93 to 2025) with data cleaning, 3 visualizations (PPG trend, top players, durability), 'Multiple Winner' feature, and 3 insights (offensive evolution, player retention, health monitoring).
• Task 2: Built a scikit-learn pipeline for bike rental prediction with preprocessing, 'temp_hum_interaction' feature, RandomForestRegressor, and evaluation (MSE ~3220, R² ~0.93).
• Challenges Resolved:
  • Task 1: HTTP 403 scraping errors (fixed with custom headers), column mismatches (dynamic mapping), year filtering (ensured min Year=1992).
  • Task 2: Seamless handling of numerical/categorical features in pipeline.
• Main Repo: https://github.com/Dheeraj1771/DSC-Tasks-Submission
• Folder: Project_1_Sports_Transport_ML

Instructions to run are in Project_1_Sports_Transport_ML/README.md. Thanks for reviewing!