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This is a complete Project that revolves around churn modeling and it contains every aspect from data cleaning down to model deployment. The data of a bank was used in this implementation. An Artificial Neural Network was trained and used to predict the probability that a given customer would leave the bank(With 87% Test accuracy) and for deploy…
The goal is to segment instances of microvascular structures, including capillaries, arterioles, and venules, to in automating the segmentation of microvasculature structures as it will improve researchers' understanding of how the blood vessels are arranged in human tissues.
This project is a Semantic Segmentation for Self Driving Cars made using Python. This project uses U-Net to segment the different regions of the image.
Through segmentation analysis, we aim to uncover meaningful patterns within this data to better understand and target different customer segments. This could involve using techniques such as clustering algorithms like k-means or hierarchical clustering to group customers with similar attributes together.
Project implementation of land cover classification problem. This repository contains the implementation of models in pytorch lightning and their results.
The repo of the ANN's class final project in NCU (Toruń, Poland). It is an implementation of the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation".