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?