I am trying to solve the following classification problem: one has a large (~2-10 GB) .csv file containing a data frame of various types. In particular, there are columns containing numeric floats as well as date/times, text strings and discrete parameters from a small set. There is also a binary parameter classifying each entry which one wants to predict in the future using the machine learning approach.Now, what would be the most appropriate tool for this job? After some research I settled on scikit-learn as the main option, but if there is other software radically better suited for the task, I'd be happy to switch.Secondly, is the random forest approach the best way to go here? I realise there are probably many factors influencing the answer to this question, but from playing around with a few tutorials and looking at the scikit-learn flowchart I am currently leaning towards random forests.Assuming one uses random forests, how exactly one should prepare these data in order for the model to make sense of it? From what I understand, scikit-learn takes only numerical floats as inputs for the model. While I could discretise date/time and other discrete fields with something like OneHotEncoder, it is not immediately clear what to do with text fields since the processing, I suppose, is very different. (I have looked at the bag-of-words approach, but being someone new to the field I fell it a bit hard to grasp right away.) Is there a meaningful way of transforming text fields for use in the model?Finally, what is the best way of dealing with large datasets for this task? While for the start their size could be limited to something fitting inside runtime memory, ideally there should be a way to extend the training set to be very large.Apologies for the vagueness of some questions. Since I am quite new to data science in general, I would appreciate if you could include actual working examples in your answers. Thanks!
Applying random forest model to a dataframe with multiple types of data
scikit learn;random forest