HackML: Fraud Detection with Data Validation

A machine learning project for detecting fraudulent transactions using advanced data validation and model training pipelines.

Project Overview

This project implements a comprehensive fraud detection system that includes:

• Data Validation Layer: Automated validation, cleaning, and quality checks for financial transaction data
• Model Training: Baseline machine learning models for fraud classification
• Reporting: Detailed validation reports and model performance metrics
• Testing: Comprehensive test suite for data validation components

Prerequisites

• Python 3.8+
• Required packages listed in requirements.txt

Installation

1. Clone the repository:

git clone https://github.com/dshak1/hackML.git
cd hackML

2. Install dependencies:

pip install -r requirements.txt

Quick Start

1. Prepare Data

Place your fraud detection datasets in the fraud/ directory:

• fraud/train.csv - Training data with target column
• fraud/test.csv - Test data without target column

2. Validate Data

Run data validation to check data quality and generate reports:

python scripts/validate_data.py \
  --train fraud/train.csv \
  --test fraud/test.csv \
  --out_dir runs \
  --mode warn

3. Train Model

Train a baseline fraud detection model:

python scripts/train_model.py \
  --train fraud/train.csv \
  --test fraud/test.csv \
  --out_dir runs

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run the full test suite
6. Submit a pull request