- We first explore the data given to us which is available in
original_data
directory. This is covered in the1_exploration.ipynb
notebook file. - As part of the same file, we check for missing values and possible discrepancies in the data. To fill in the missing values, we have downloaded data from the internet and saved it in the
downloaded_data
directory. It contains the information for all the five leagues from the 2008-09 season to 2017-18 season. The script to download these CSVs is0_downloadMoreData.py
. Once done, we use theimputed_data
directory to save the imputed train and test dataframes for use in the next steps. - Finally, now that there are no null values, we perform feature-engineering on the train and test dataframes keeping in mind the models we might use. This is covered in
2_featureEngineering.ipynb
- Further feature-engineering steps which are specific to the model we will be using is covered in the three
3_modelBuilding_*.ipynb
files. - The predictons for each of these three models are saved in
predictions
directory.
More information about steps taken in each steps can be found in the Ipython notebook files.