FinTech ML Labs is a hands-on Python project series designed to help you learn and build real-world machine learning solutions used in financial systems. This includes credit scoring, fraud detection, risk forecasting, and more.
Each project is practical, beginner-friendly, and includes all necessary code, datasets, and documentation.
- How ML is used in credit scoring, fraud detection, and trading
- How to work with public financial datasets
- How to build, evaluate, and deploy ML models
- How companies like Stripe, PayPal, and Klarna apply ML in production
.github/workflows # GitHub Actions for CI/CD
api/ # Backend API components
dashboard/ # Dashboard-related files and slides
notebooks/ # Jupyter notebooks and datasets
scripts/ # Utility or helper scripts
tests/ # Unit and integration test files
.gitignore # Git ignored files config
README.md # Project overview and setup guide
requirements.txt # Required Python packages
git clone https://github.com/epythonlab2/fintech-ml-labs.git
cd fintech-ml-labspython -m venv venv
# macOS/Linux
source venv/bin/activate
# Windows
.�env\Scripts�ctivatepip install -r requirements.txtNavigate to the notebooks/ directory and launch Jupyter:
jupyter notebookOpen credit_scoring_model.ipynb to explore the machine learning pipeline.
Instructions for setting up the API or dashboard will be provided in later episodes.
📺 Watch the full tutorial series on YouTube:
Build FinTech ML Projects with Python (Intro Episode)
Build FinTech ML Projects with Python (Credit Scoring Model)
Each video includes:
- Python walkthrough
- Code explanation
- Dataset links
- GitHub repository access
This project is licensed under the MIT License.
See the LICENSE file for details.
Created by Asibeh – AI developer and educator focused on ML in finance.