Credit Classification

Supervised learning project to predict whether a loan will default using tabular customer and loan features. The aim is to build a clear, reproducible baseline credit-risk model that can be extended to more advanced approaches.

1. Overview

This repository contains a small end-to-end workflow for loan default prediction:

• Exploratory data analysis of the loan dataset
• Data cleaning and feature engineering
• Training and evaluation of supervised learning models
• Interpretation of model performance and limitations

The work was originally developed as part of a university assignment, but the structure is organised to resemble a real data science project rather than a single notebook.

2. Data

The project assumes a single CSV file with one row per loan and a binary target variable indicating default vs non-default, together with customer and loan attributes.

The dataset is stored locally and not committed to the repository. A typical local layout is:

• data/raw/dataset.csv – original dataset
• data/processed/ – any cleaned or engineered versions

You can adjust the paths inside the notebook or scripts if your filenames differ.

3. Methodology

The modelling workflow follows a standard supervised learning pipeline for credit risk:

1. Preprocessing and feature engineering

   • Handling missing values
   • Encoding categorical variables
   • Scaling or normalising numerical features where appropriate

2. Model training

   • Baseline models such as logistic regression
   • Optionally, comparison with tree-based methods (for example random forests or gradient boosting)

3. Evaluation

   • Train/validation split or cross-validation
   • Metrics including ROC-AUC, accuracy, precision, recall and confusion matrices
   • Qualitative discussion of where the model performs well or poorly

Most of the experimentation currently lives in notebooks/Loan prediction.ipynb. As the project evolves, more logic can be refactored into reusable modules under src/.

4. Repository structure

.
├── notebooks/          # Jupyter notebooks for exploration and modelling
│   └── Loan prediction.ipynb
├── src/                # Python modules (data prep, training, evaluation)
├── requirements.txt    # Python dependencies
├── .gitignore          # Ignore rules (data, caches, IDE files, etc.)
└── README.md           # Project documentation

The data/, report/ and docs/ folders are expected to exist locally but are not tracked by git, so they do not appear in the GitHub view.

5. Getting started

1. Clone the repository

   git clone [email protected]:abailey81/Credit-Classification.git
   cd Credit-Classification

2. (Optional) Create and activate a virtual environment

   python -m venv .venv
   source .venv/bin/activate    # macOS / Linux
   # .venv\Scripts\activate     # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Add the dataset

   Place your CSV in data/raw/dataset.csv (or update the notebook path accordingly).

5. Run the analysis

   jupyter notebook notebooks/Loan\ prediction.ipynb

   From there you can reproduce the analysis, adjust features, or try alternative models.

6. Reproducibility and next steps

• Dependencies are listed in requirements.txt.
• Data files are kept out of version control to avoid exposing sensitive information.
• Random seeds can be fixed in the notebook to make results more stable between runs.

Planned improvements include refactoring more code into src/, adding configuration files for experiments, logging model outputs, and extending evaluation to include calibration, scorecards and monitoring.