This project addresses the rising fraud in digital transactions, such as UPI and card payments, where fraudsters exploit system vulnerabilities. The goal is to create a highly accurate detection system using advanced data processing and machine learning to enhance transaction security. Nowadays, we perform numerous transactions using UPI, credit/debit cards,and ATMs for various purposes like purchasing goods and services. However, with the increasing number of transactions, fraud cases are also rising. Theprimary reason for this increase is the presence of loopholes in the transaction systems, which fraudsters exploit to track payment histories and commit fraud.
Understanding Fraudulent Transactions: Imagine you have a bank account where you perform various transactions like sending money to friends, receiving your allowance, or buying things online. Sometimes, however, there might be un-authorized or fake transactions that you didn't approve. These are called fraudulent transactions.
Aim : The aim of this project is to develop a highly accurate fraud detection system using advanced data processing and machine learning techniques to enhance the security and integrity of financial transactions.
Objectives of this Project:
- Develop a Robust Fraud Detection Model: o Utilize the dataset to create a highly accurate model for detecting fraudulent transactions.
- Data Cleaning and Preprocessing: o Ensure the dataset is free from missing or inconsistent data for effective analysis and model training.
- Data Visualization and Exploration: o Use visualization techniques to understand transaction patterns and identify potential fraud indicators.
- Feature Engineering: o Transform and engineer features to improve model performance.
- Model Selection and Evaluation: o Evaluate various machine learning models to identify the one with the highest accuracy, with the Decision Tree Classifier selected for its superior performance.
- Model Training and Testing:o Split the data into training and testing sets, train the model, and evaluate its performance to ensure reliability.
- Prediction and Analysis: o Use the trained model to predict fraud on new transactions and analyze its predictions for further improvement