Machine Learning - Home Credit Default Risk

Predicting customer credit default with Machine Learning: a model garden exploration.

Project

📰 Intro

Home Credit is a multinational company with a simple business model: lend money. Generally, to customers with little or no credit history.

One of the cornerstones of their business model is the predictive power of the data they possess. They are making some of this data available online aiming to unlock its full potential. In our case, we got a sample of this data to apply the data mining process on it with the intention to extract value.

🎯 Goal

The main goal is to use data to predict if an applicant will have trouble to pay their debt. This objective is cascaded into:

1. Creating a Machine Learning model that is good at distinguishing between classes: in our case, distinguishing a good applicant to a loan from a bad one.
2. This is also a Learning-oriented Project, I intend to experiment on various aspects of Machine Learning - and have some fun.

🗺 Methodology

🗄 Notebooks

• 1.0-framing-the-problem.ipynb
• 2.0-getting-the-data.ipynb
• 3.0-eda-applications.ipynb
• 4.0-preparing-data-and-selecting-models.ipynb
• 5.0-fine-tunning-the-model.ipynb

📦 Folder Structure

├── LICENSE
├── Makefile           <- Makefile with commands like `make data`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── interim        <- Intermediate and useful data.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, figures, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as Markdown, PDF, LaTeX, etc.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip list --format=freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download, generate, and explore data
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │
│   ├── models         <- Scripts to train models and then use trained models to make predictions