felipelodur/Bike-Sharing-Prediction-with-NeuralNet
Developed as coursework for Udacity "Deep Learning Fundamentals" Nanodegree
This project helps urban planners or city operations managers predict the number of daily bike rental riders. By inputting historical ridership data, including factors like weather and day of the week, it estimates future demand. The output is a prediction of how many bikes will be rented each day, aiding in resource allocation and inventory management.
No commits in the last 6 months.
Use this if you need a basic, understandable model to forecast bike rental demand without relying on complex, pre-built deep learning libraries.
Not ideal if you require highly accurate predictions for nuanced periods like holiday seasons, as the model may over-estimate due to limited specific training data.
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Dec 21, 2017
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