Income Tax Fraud Detection using AIML

Financial fraud poses a significant threat to both individuals and institutions, leading to substantial monetary losses and undermining trust in financial systems. Detecting fraudulent transactions promptly and accurately is crucial to mitigate these risks. This project aims to address the challenge of fraud detection by employing advanced machine learning techniques. Specifically, it seeks to develop a robust system capable of classifying transactions as fraudulent or non-fraudulent using a dataset of financial transactions. The project will explore various algorithms, including K-Means Clustering for unsupervised learning and Decision Tree, Logistic Regression, Random Forest, and Naïve Bayes for supervised learning. By evaluating and comparing the performance of these models, the project aims to identify the most effective approach for fraud detection and enhance the overall security and efficiency of financial transaction systems