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I have a dataset which contains 4000k rows and 6 columns. The goal is to predict travel time demand of a taxi. I have read many articles regarding how to approach the problem. So, every writer tell his own way. The thing which I have concluded from all my readings is that I have to use multiple algorithms and check the accuracy of each one. Then I can ensemble them by averaging or any other approach.

Which algorithms will be best for my problem accuracy-wise? Some links to code will be helpful for me.

I currently only have training set of data. After I work on it, it will be evaluated on any testing set by my professor. So, what should I do now? Either split data I have into my own testing and training set or separately generate dummy data as a testing set?

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  • $\begingroup$ Do you have the output of your input features? Can you say what your features are? $\endgroup$ – Media Jun 14 '19 at 11:40
  • $\begingroup$ Yes I have travel time as output feature. Where input features are Day, where the value indicates the sequential order and not a particular day of the month. Timestamp, start time of 15-minute intervals, in the following format: <hour>:<minute>, where hour ranges from 0 to 23 and minute is either one of (0, 15, 30, 45). Demand, aggregated demand normalised to be in the range [0,1] $\endgroup$ – Saeed Ahmad Jun 14 '19 at 19:12

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