🔍 Fraud Detection API

A machine learning-powered REST API that detects fraudulent credit card transactions in real time.

Built with FastAPI, scikit-learn, and Random Forest — trained on 284,807 real-world transactions.

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🎯 Background

This project was inspired by my experience working at Aviva, a FTSE 100 insurer, where I analysed claims data to identify patterns and anomalies. I wanted to combine that domain knowledge with my machine learning skills to build a production-ready fraud detection system.

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🚀 Live Demo

API deployed at: https://fraud-detection-api-mji8.onrender.com/docs

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📊 Model Performance

Metric	Score
Overall Accuracy	100%
Fraud Precision	84%
Fraud Recall	83%
F1 Score (Fraud)	83%

Trained on the Kaggle Credit Card Fraud Detection dataset — 284,807 transactions, 492 fraud cases.

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🛠️ Tech Stack

• Python 3.11
• FastAPI — REST API framework
• scikit-learn — Random Forest model
• imbalanced-learn — SMOTE for class imbalance
• joblib — model serialisation
• uvicorn — ASGI server
• pandas / numpy — data processing

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📁 Project Structure

fraud-detection-api/ ├── app/ │ ├── init.py │ ├── main.py # API endpoints │ ├── model.py # model loading and prediction │ └── schemas.py # request/response schemas ├── data/ # dataset (not tracked in git) ├── model/ # saved trained model ├── notebooks/ # exploratory analysis ├── train.py # model training script ├── requirements.txt └── README.md

⚙️ Run Locally

1. Clone the repo

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

2. Create virtual environment

python -m venv venv
venv\Scripts\activate  # Windows

3. Install dependencies

pip install -r requirements.txt

4. Train the model

python train.py

5. Run the API

uvicorn app.main:app --reload

6. Open docs http://127.0.0.1:8000/docs

🔌 API Endpoints

Endpoint	Method	Description
/	GET	Welcome message
/health	GET	Health check
/predict	POST	Predict fraud on a transaction

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📬 Example Request

POST /predict
{
  "Time": 406.0,
  "V1": -2.31,
  "V2": 1.95,
  ...
  "Amount": 0.0
}

✅ Example Response

{
  "prediction": 1,
  "probability_fraud": 0.92,
  "message": "⚠️ FRAUD DETECTED — 92.0% confidence"
}

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👩‍💻 Author

Suditi Sharma MSc Computer Science (Data Science) — University of Strathclyde LinkedIn | GitHub