At MoneyLion we are constantly working to assess the risk of our applicants more accurately. Being successful in this objective enables us to better price customers and mitigate losses on our portfolio of loans. The following challenge asks you to work with a data set of loan repayment. It is intentionally meant to be open-ended. The point is not to arrive at a predetermined answer or search for the lowest possible standard error. Rather, the hope is that it will force you to ask relevant questions about the data, do some preliminary exploration, perform the necessary manipulations or aggregations, generate visualizations, and reach conclusions or insights. The most important thing to remember is that we are evaluating your thought process and ideas! The more you explain your thinking, in a clear and succinct manner, the better. If you get stuck, describe what additional information or data you might look to collect, and trying a different idea is highly encouraged.
Challenge starts from 13/5/2022 - 15/5/2022 (Approximately 30 hours)
imbalanced-learn
matplotlib
numpy
pandas
scikit-learn
seaborn
tensorflow
pandas-profilinghtml/ - include 2 .html files for jupyter notebook and 1 .html file for report generated model/ - include 3 .joblib files (sklearn model) and 2 .h5 files (tf model) main.ipynb - main file thought_process.ipynb - include features visualization utils.py - include utiliy functions