A machine learning analysis of bike-sharing demand patterns using the UCI dataset, with Gradient Boosting achieving ~90% prediction accuracy.
The Challenge
Bike sharing systems struggle with inventory imbalances. Stations run empty during peak demand while others have idle bikes. Operators need demand forecasting to optimize bike distribution.
The Solution
Analyzed the UCI Bike Sharing Dataset with hourly rental records. Compared four regression models, with Gradient Boosting Regressor achieving the best performance (~0.9 R² score) by capturing complex feature interactions.
Key Features
- Exploratory analysis revealing peak demand at 4-5 PM (~450 bikes)
- Feature engineering from temporal, weather, and user-type variables
- Model comparison: Ridge, Random Forest, Gradient Boosting, KNN
- Gradient Boosting identified as best performer with ~0.9 score
- Hour of day and temperature identified as dominant predictors
Tech Stack
Jupyter Notebook for iterative analysis and visualization
Model training with hyperparameter tuning; Gradient Boosting achieved best results
Data manipulation with one-hot encoding for categorical variables