Aniket-Thopte/Demand-Forecasting-Public-Bike-Rental-Predictive-Modeling-
Developed multiple predictive models with 90% accuracy for forecasting the daily-hourly bike rental count using Python & Machine Learning techniques like Regression, Clustering, Ensemble, Neural Network to achieve maximum accuracy
This project helps bike-sharing operators predict how many bikes will be rented each hour, every day. It takes historical rental data and relevant factors like weather, outputting highly accurate forecasts. Operations managers or city planners running public bike rental systems can use this to optimize bike distribution and staffing.
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Use this if you manage a public bike rental service and need to anticipate hourly demand to reduce costs and improve service.
Not ideal if you are looking to predict demand for a different type of rental service, such as cars or scooters, without significant adaptation.
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May 07, 2021
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