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📊 An R package for interactively exploring UCSC Xena https://xenabrowser.net/datapages/; Book: https://lishensuo.github.io/UCSCXenaShiny_Book; App online: https://shiny.hiplot.cn/ucsc-xena-shiny/, https://shiny.zhoulab.ac.cn/UCSCXenaShiny
This repository contains working notebooks and data files for the Platinum Jamat Khana's data wrangling online classes.
This project involves the application of dimensionality reduction techniques i.e., Principal Component Analysis (PCA), on a cancer patients dataset. The goal is to simplify the dataset by reducing its dimensionality, making it easier to visualize and analyze, while retaining essential information.
This is one of the ways in which KNN algo is implemented on cancer dataset
Automating the prognosis of cancer in new patients and also survival prediction of existing cancer patients to see whether they fall into relapse or non-relapse and provide appropriate treatment. We have introduced a new idea , where an e-commerce application using micro services approach has been developed to track the purchases of the users and data analysis has been done to get the required data which can be used for our prediction. Along with these, clinical data and genomic data have been trained and tested using ensemble learning algorithms for survival prediction.
SVC model predictions on cancer dataset of sklearn and also used GridSearchCV to find the best parameters for the SVC model