Training a machine learning model on multiple CSV files?
If all of the files contain the same features, you can concatenate them. If some features are preprocessed differently (for example, they have different ranges in different files), you should make them consistent before concatenating. Then use the obtained big data frame/array for model training.
Also, consider shuffling the rows.
I want to train a machine learning model on multiple csv files that are all unique. Each file is a collection of time series data from basketball games. I want to train a model to look at each game and be able to predict outcomes. Should I simply tell sci kit learn or another package to iterate through the files in the folder of interest and run regressions on each? Thank you in advance.