Currently Rental bikes are introduced in many urbancities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern
The goal is to create a prediction model using Supervised Regression so that it may be used to foretell bike count required at each hour for the stable supply of rental bikes.
Pandas for data manipulation, aggregation
Matplotlib and Seaborn for visualisation and behaviour with respect to the target variable
NumPy for computationally efficient operations
Scikit Learn for model training, model optimization, and metrics calculation
