A polyp is an abnormal tissue growth in the large intestine that is typically benign but can develop into malignant colorectal cancer. Colonoscopy enables endoscopists to identify and assess these polyps for potential removal. However, the accuracy of this procedure depends heavily on the clinician’s expertise, making it prone to human error and variability. Our goal is to build a deep-learning model that detects colorectal polyps in images from colonoscopies to minimize missed lesions and improve patient outcomes.

Fraud detection is a critical area where deep learning has been effectively applied to identify and prevent unauthorized transactions, money laundering, and other financial crimes. Traditional rule-based systems and statistical models often struggle to detect sophisticated fraud patterns, particularly when dealing with large volumes of data and rapidly evolving fraud techniques. In contrast, deep learning models, such as CNNs, RNNs, and autoencoders, have proven highly effective in analyzing complex, high-dimensional transaction data and detecting subtle, non-linear patterns indicative of fraudulent activity.
In this project, we build a User ID-based fraud detection model using autoencoders, trained on unlabelled real-world credit card transaction data, capable of detecting fraud with a precision of up to 35% and a recall of up to 72%, performing significantly better than traditional ML/statistical baseline models..