A beginner machine learning project where I built a model to predict Olympic medal counts for countries based on features like GDP, population, and past performance.
- Collected and cleaned dataset with Pandas, handling missing values and scaling features.
- Explored patterns with Seaborn visualizations (e.g., GDP vs. medals).
- Trained regression models (Linear Regression, Random Forest) using scikit-learn.
- Compared performance with cross-validation and evaluation metrics (R², MSE).
- Documented results in Jupyter for reproducibility.
- Languages/Libraries: Python, Pandas, NumPy, scikit-learn, Matplotlib/Seaborn
- Concepts: Data preprocessing, feature engineering, regression modeling, model evaluation
- Practices: Experimentation, reproducible ML pipeline, GitHub version control
- How to clean and prepare real-world datasets for ML.
- Differences in model performance (why simple models sometimes outperform complex ones).
- Importance of reproducibility and clear documentation in ML projects.