Fraud Detection Project

Overview

This project is a fraud detection system that leverages machine learning models to identify fraudulent transactions. It includes data preprocessing, synthetic data generation, model training, and a web-based interface for results visualization.

Features

• Preprocessing and cleaning raw transaction data
• Generating synthetic data to enhance training datasets
• Building and training a Random Forest model
• Visualizing predictions and insights through a web dashboard

Requirements

To run this project, you need:

• Python (>=3.9)
• Libraries specified in req.txt

Install Dependencies

bash pip install -r req.txt

Usage

1. Clone the Repository bash git clone cd CODE/fraud_detection_project

2. Prepare the Data Place your data files in the data/ folder.

3. Run the Web Application bash python app.py

4. Optional: Retrain the Model If you need to retrain the model: bash python src/model/train_model.py

Project Structure

fraud_detection_project/ ├── app.py
├── main.py
├── synthetic_data.py

data/
├── final_dataset.csv ├── new_transaction_data.csv ├── predictions.csv ├── transactions.csv └── transaction_history_unlocked.pdf

src/
├── generate_synData.py
├── pdf_to_csv.py
├── predict.py
├── visualize_predictions.py

src/model/
├── best_random_forest_model.joblib ├── train_model.py
├── evaluate_model.py
├── preprocessed_data.joblib
└── preprocessor.joblib

src/preprocess/
└── preprocess_data.py

static/
├── css/
└── images/

templates/
├── analysis.html ├── dashboard.html ├── index.html └── visualize.html

req.txt

How It Works

1. Data Preprocessing
   The preprocess_data.py script cleans and prepares raw transaction data for training.

2. Synthetic Data Generation
   generate_synData.py creates synthetic datasets to augment training data.

3. Model Training
   train_model.py trains a Random Forest model to detect fraudulent transactions.

4. Prediction and Visualization
   predict.py runs predictions, and the results are displayed in a user-friendly web interface.

Libraries and Tools

This project leverages the following libraries and tools:

• Python: Programming language used for implementation.
• Scikit-learn: For machine learning model development and evaluation.
• Pandas: For data preprocessing and manipulation.
• Joblib: For saving and loading model and preprocessor artifacts.
• Flask: To build the web application and API endpoints.
• Matplotlib/Seaborn: For data visualization and exploratory analysis.

Data Sources

• Synthetic Dataset: Created using generate_synData.py to supplement training data.
• Raw Transaction Data: Processed using scripts in the preprocess/ and data/ directories.

Acknowledgments

• OpenAI: For providing tools and guidance during the project.
• Community Contributors: Special thanks to open-source community contributors for shared knowledge and resources.

License

This project is licensed under the MIT License. See the LICENSE file for full details.

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