bike-sharing-prediction-with-RNN

We have created a model to predict how many bicycles will be rented in the following days. The features used like weather, temperature, working day are explained in the following sections in detail. We have used SimpleRNN method in Keras library. It is one of the sequential models. The others are LSTM and GRU. Sequential models have 3 dimension (sample size, time steps, features). Preparing 3D input is another challenge. Instead of trying to create a 3D array, we use TimeSeriesGenerator class which brings some other advantages like setting the batch size. We skipped feature engineering and visualization parts because main purpose was to practice a sequential neural network. It is possible to have better achivements by applying these methods and then create a predictive model. Data is 2 years daily data. Number of samples is 731. We have splitted it into 631, 50, 50 as train, test and hold-out data respectively. We have measured the performance of the model with ( 1 - (mean average error) / (mean) ) and we have reached values around %80. There are so many further studies: More feature engineering for better accuracy and trying other sequential models.