1. I attempt to predict defaulters or non-defaulters using Home Credit's Kaggle Dataset.
2. The model must quantify feature importance which appeals to domain knowledge.
3. I use the model to score Probability of Default (PD).
4. Use PD to compute Expected Loss of Principal Due to Default with 0% recovery rate.
5. Build a dashboard to address business questions related to Completed Cash Loans. See link to dashboard.

1. I considered approved cash loans only.
2. Current applicants must have historical cash loans information from Home Credit.
3. Instalments for historical cash loans must be at most 3 years old.

Each notebook represents a step in the analytics process and there are 4 Jupyter Notebooks:

1. columns-selection-for-selected-csv-files.ipynb: this notebook removes unused columns from the original dataset.
2. duplicate-removal.ipynb: this notebook removes duplicates in the dataset.
3. extracting-instalment-payment-features.ipynb: this notebook extracts features for downstream classification and completed cash loan data for analysis.
4. default-prediction.ipynb: this notebook contains classification of defaulters or non-defaulters with logistic regression.

I used Kaggle Notebook to run my code as it offers more computing power. Thus, I needed to upload the Home Credit datasets to Kaggle and store intermediate and final datasets there.

The following are links to the datasets I stored in Kaggle for my work:

1. raw-dataset: this link contains 4 csv files from Home Credit's original dataset.
2. intermediate-dataset: this link contains 3 csv files produced by columns-selection-for-selected-csv-files.ipynb and duplicate-removal.ipynb.
3. cleaned-dataset: this link contains 5 csv files produced by extracting-instalment-payment-features.ipynb.