Detecting UPI Fraud with Machine Learning: An Advanced Analytical Approach

n this comprehensive project, we delve into the critical issue of UPI (Unified Payments Interface) fraud and explore how machine learning can be leveraged to detect and prevent fraudulent transactions. This project covers:
Introduction to UPI and Fraud Scenarios: An overview of UPI, its significance in the digital payment landscape, and the common types of fraud encountered.
Data Collection and Preprocessing: Methods for gathering relevant transaction data and preparing it for analysis, including cleaning, normalization, and feature selection.
Exploratory Data Analysis (EDA): Techniques to understand the data distribution, identify patterns, and visualize potential indicators of fraud.
Model Selection and Training: Comparing various machine learning algorithms (e.g., Logistic Regression, Decision Trees, Random Forests, and Neural Networks) to find the most effective model for fraud detection.
Model Evaluation and Optimization: Assessing the performance of the selected model using metrics like precision, recall, F1-score, and ROC-AUC, and fine-tuning it for better accuracy.
Implementation and Deployment: Practical steps to implement the trained model in a real-time environment, including integration with UPI systems and setting up alert mechanisms for detected fraud.
Challenges and Future Directions: Discussion of potential challenges in UPI fraud detection and future advancements in the field.
Dataset and Python file:- drive.google.c...
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