Fraud Detection model based on anonymized credit card transaction. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The datasets contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

This project leverages computer vision and machine learning techniques to automate the process of detecting cracks in concrete structures. The primary goal is to provide an efficient and accurate method for damage surveillance in buildings, which is crucial for maintaining structural integrity and safety. The project was developed as an entry for the "PS-1, Concrete Crack Detection". The model has achieved an impressive F1 score of 1, indicating its high accuracy in distinguishing between cracked and non-cracked surfaces.

Brain Tumor Detection Using Machine Learning Source Code project implements a U-Net model to segment brain tumors in MRI images. It focuses on identifying and classifying three types of brain tumors: meningioma, glioma, and pituitary tumors. The project uses the dataset from publicly available brain tumor segmentation data and applies deep learning techniques to accurately delineate tumor regions from MRI scans.