This challenge is about the bankruptcy prediction. A classification study with unbalanced classes. The 5 initial arff files are converted to 5 csv files in order to conceive the dataframe. It includes 64 features, adding to this the target and one feature about the year that I have added. The target, named "class", is a numeric feature. 0 means no bankruptcy while 1 means the bankruptcy. Due to the unbalanced target classes, the choice of the metrics is mainly based on the recall (the true positive) and the auc.

The final notebook is named final_version.py. It contains all the study from the data preprocessing step to the fitting models step

I have implemented the flask API by using the logistic regression model with the most important features resulting of a seven feature model. The API is located in the Test folder with the requirements.txt. The model imported for the flask application is the file named LR.