This project explores the Capital Bikeshare dataset (Washington D.C., 2011–2012) to predict bike rental demand using supervised machine learning models. I applied a complete ML workflow: data cleaning, exploratory analysis, feature engineering, baseline comparison, model training, and hyperparameter tuning with cross-validation.

Key Skills: Data Cleaning (Pandas), Machine Learning (Scikit-Learn), Hyperparameter Tuning (GridSearchCV), Model Deployment (Streamlit, GitHub).