Sweet Lift Taxi company (fictive company) has collected historical data on taxi orders at airports. To attract more drivers during peak hours, in this project a model should be built to predict the amount of taxi orders for the next hour with a RMSE lower than 48.
Therefore the company supplied a timeseries of rides in the timeframe of 2018-03-01 to 2018-08-31

Within the project the following steps have been carried out:

• Importing and inspecting the data
• Data preprocessing (resampling...)
• In depth analysis of the data
• Feature generation
• Model building, training and evaluation
• Hyperparameter tuning

An Random Forest Regressor with a RMSE of 45.2 has been developed, which exaggerates the requirements of the client