🚨 Financial Fraud Detection Using Machine Learning 🚨

📖 Project Overview

This project is focused on detecting financial fraud using machine learning models. It offers a complete pipeline starting from data collection and model training to deployment through a Flask web application. The objective is to enable users to input transaction data and receive fraud detection predictions in real-time. The machine learning models are trained in Google Colab and deployed locally using Flask.

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📂 Workflow

1. 📅 Dataset Acquisition

• Download the dataset from the following link:
  • Download CSV

2. 📚 Model Training (Google Colab)

• Open the fraud.ipynb file in Google Colab.
• Upload the CSV dataset.
• Execute each cell in the notebook to clean the data, train the machine learning models, and evaluate performance.

3. 🔄 Model Exporting

• Once the models are trained, export and save them as .h5 (Keras/TensorFlow) or .joblib (Scikit-learn).
• Download these files and place them in the models/ directory inside your local project folder.

4. 📝 Project Setup (Local Environment)

• Clone the repository:

  git clone https://github.com/NukaNarendra/FinancialFraudDetectionUsingML
  cd FinancialFraudDetectionUsingML

• Ensure Python 3.10 or below is installed.

5. 📚 Install Required Dependencies

Install the necessary Python libraries using pip:

pip install flask flask_sqlalchemy werkzeug numpy tensorflow pandas joblib

6. 🌐 Run the Flask Application

Start the application with:

python main.py

Visit http://127.0.0.1:5000/ on your browser to access the web interface.

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🛠️ Tech Stack & Tools

• Programming Language: Python
• ML Libraries: TensorFlow, Scikit-learn, Pandas, NumPy, Joblib
• Web Framework: Flask
• Notebook: Google Colab
• Deployment: Localhost via Flask

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📊 Key Features & Results

• ✅ Trains machine learning models to identify fraudulent financial transactions.
• ✅ Flask web interface for interactive prediction and user input.
• ✅ Real-time fraud detection and result display.
• ✅ Easy-to-use modular structure with pre-trained model integration.

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📁 Folder Structure

📂 FinancialFraudDetectionUsingML
├── 📁 instance
├── 📁 static
├── 📁 templates
├── 📁 models             # Folder to store trained model files
├── 📄 README.md
├── 📓 fraud.ipynb        # Google Colab notebook
└── 🐍 main.py            # Flask application

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🔒 Prerequisites

• Python 3.10 or lower
• Google Colab for model training
• Flask & Flask_SQLAlchemy
• TensorFlow, Pandas, NumPy, Joblib

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👥 Contribution

Contributions are highly welcome! Feel free to raise issues, suggest improvements, or submit pull requests to enhance the functionality and performance.

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👤 Author

Venkata Narendra Nuka

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📈 Future Enhancements

• 🔔 Add real-time alerts for detected frauds via email/SMS.
• 🤖 Integrate advanced deep learning models like LSTM or Autoencoders.
• 🔄 Extend deployment to cloud platforms like Heroku or AWS.