Credit Card Fraud Detection

An end-to-end machine learning pipeline for detecting fraudulent credit card transactions. The project trains a Random Forest model on the European credit card dataset and deploys it using FastAPI for real-time prediction.

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Features

• Exploratory Data Analysis (EDA)
• Handling class imbalance using SMOTE
• Random Forest classification model
• Hyperparameter tuning
• Modular ML pipeline
• FastAPI deployment for real-time predictions

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Project Structure

fraud-detection
│
├── models
│   ├── fraud_model.pkl
│   └── scaler.pkl
│
├── notebooks
│   └── 01_data_exploration.ipynb
│
├── src
│   ├── preprocessing.py
│   ├── train.py
│   ├── predict.py
│   ├── tune_model.py
│   ├── evaluate.py
│   └── api.py
│
├── requirements.txt
└── README.md

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Installation

Clone the repository

git clone https://github.com/omkar42785/fraud-detection.git
cd fraud-detection

Install dependencies

pip install -r requirements.txt

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Running the API

Start the FastAPI server:

uvicorn src.api:app --reload

Open the API documentation:

http://127.0.0.1:8000/docs

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Example API Request

POST /predict

{
 "features": [0.1,-1.2,0.5,-0.3,0.7,0.1,-0.6,0.4,-0.2,0.3,-0.5,0.6,-0.1,0.8,-0.7,0.2,-0.3,0.4,-0.6,0.5,-0.2,0.3,-0.4,0.1,0.2,-0.3,0.4,-0.5,100]
}

Example response:

{
 "prediction": 0,
 "fraud_probability": 0.02
}

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Model

Algorithm used:

• Random Forest Classifier

Evaluation metrics:

• ROC-AUC
• Precision
• Recall
• F1 Score

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Dataset

European cardholders credit card fraud dataset.

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Author

Omkar

API Documentation

Prediction Example