Fastag Fraud Detection System

[Harshit-code-tech]

Fastag Fraud Detection System

Welcome to the Fastag Fraud Detection System project! This project tackles the challenge of identifying fraudulent activities within the Fastag toll payment system in India. Our goal is to develop a robust system that can analyze real-world Fastag transaction data and accurately classify transactions as either legitimate or fraudulent.

Project Overview

Data Exploration and Analysis

• Understand the data distribution.
• Identify potential patterns indicative of fraud (e.g., unusual toll amounts, frequent lane changes).
• Explore relationships between various features like vehicle type, toll booth location, and time of transaction.

Feature Engineering

• Create new features to enhance the model's ability to detect fraud.
• Extract time-based features (e.g., hour of the day).
• Calculate distances between toll booths for potential route inconsistencies.
• Create categorical features based on vehicle dimensions.

Machine Learning Model Development and Evaluation

• Train and evaluate various machine learning models suited for classification tasks.
• Potential models include Support Vector Machines (SVM), Random Forest, Gradient Boosting, and XGBoost.
• Model selection will be based on accuracy, precision, recall, and ROC AUC curve to ensure optimal fraud detection performance.

Model Deployment

• Develop a user-friendly web application for real-time fraud prediction.
• Users can input transaction details (vehicle type, toll amount, etc.).
• The application will leverage the trained model to predict the likelihood of fraud for that specific transaction.

Documentation

• Update the project documentation to reflect the new enhancements.
• Include a detailed README file explaining the changes made, how to run the enhanced model, and the results obtained.
• Ensure the code is well-commented for better understanding and maintenance.

Benefits

• Reduced Revenue Loss: Effectively identify and prevent fraudulent transactions to minimize revenue loss.
• Enhanced Security and Trust: Foster a more secure and reliable Fastag ecosystem, increasing user trust.
• Improved Efficiency: Accurate toll collection through fraud detection leads to better overall efficiency of the Fastag system.

Requirements

• Strong knowledge of Python and machine learning libraries (pandas, scikit-learn, streamlit, etc.).
• Experience with data preprocessing, feature engineering, and model evaluation.
• Familiarity with web application development using Streamlit.
• Good documentation practices.

Expected Outcome

• Develop a detailed understanding of Fastag transaction data and identify key features indicative of fraud.
• Train and evaluate various machine learning models to find the best model for fraud detection.
• Deploy a user-friendly web application that accurately predicts the likelihood of fraud in Fastag transactions, providing a valuable tool for enhancing the security and efficiency of the Fastag system.

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[Harshit-code-tech]

I am a contributor on Social Summer Of Code Season 3 please assign me this issue

[Harshit-code-tech]

https://www.kaggle.com/datasets/krishujeniya/fastag-frauds-records
this is dataset url

[abhisheks008]

Thanks for creating a new issue. Assigned this issue to you under SSOC tag.
@Harshit-code-tech

[github-actions]

Hello @Harshit-code-tech! Your issue #679 has been closed. Thank you for your contribution!