This repository contains a small pre-task for potential ML team members for UBC Launch Pad.
The dataset bundled in this repository contains information about credit card bill payments, courtesy of the UCI Machine Learning Repository. Your task is to train a model on this data to predict whether or not a customer will default on their next bill payment.
Most of the work should be done in model.py. It contains a
barebones model class; your job is to implement the fit and predict methods,
in whatever way you want (feel free to import any libraries you wish). You can
look at main.py to see how these methods will be called. Don't
worry about getting "good" results (this dataset is very tough to predict on)
— treat this as an exploratory task!
To run this code, you'll need Python and three libraries: NumPy, SciPy,
and scikit-learn. After invoking python main.py from your shell of
choice, you should see the model accuracy printed: approximately 50% if you
haven't changed anything, since the provided model predicts completely randomly.
Here are the things you should do:
- Fork this repo, so we can see your code!
- Install the required libraries using
pip install -r requirements.txt(if needed). - Ensure you see the model's accuracy/precision/recall scores printed when running
python main.py. - Replace the placeholder code in
model.pywith your own model. - Fill in the "write-up" section below in your forked copy of the README.
Good luck, and have fun with this! 🚀
Give a brief summary of the approach you took, and why! Include your model's accuracy/precision/recall scores as well!
X_train and X_test contain data of the following form:
| Column(s) | Data |
|---|---|
| 0 | Amount of credit given, in dollars |
| 1 | Gender (1 = male, 2 = female) |
| 2 | Education (1 = graduate school; 2 = university; 3 = high school; 4 = others) |
| 3 | Marital status (1 = married; 2 = single; 3 = others) |
| 4 | Age, in years |
| 5–10 | History of past payments over 6 months (-1 = on-time; 1 = one month late; …) |
| 11–16 | Amount of previous bill over 6 months, in dollars |
| 17–22 | Amount of previous payment over 6 months, in dollars |
y_train and y_test contain a 1 if the customer defaulted on their next
payment, and a 0 otherwise.