vinodbavage31/bike-rental
Predictive model for daily bike rental demand. Utilizes EDA and a Tuned Gradient Boosting Regressor ( R 2 ≈ 0.91 R 2 ≈0.91 ) to forecast demand based on seasonal and weather factors, aiding fleet optimization.
This project helps bike-sharing companies predict how many bikes they'll need each day. By taking historical rental data along with current seasonal and weather information, it forecasts future demand. This allows operations managers to optimize their fleet, schedule maintenance, and plan staffing efficiently.
Use this if you manage a bike-sharing service and need to reliably forecast daily rental demand to improve operational efficiency and resource allocation.
Not ideal if you need real-time, hour-by-hour predictions or if your primary concern is individual bike tracking rather than overall fleet demand.
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Last pushed
Nov 22, 2025
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