This repository proposes several methods to infer relevant associations within a set of features.

Project for IST687 Applied Data Science- School of Information Studies, Syracuse University

Identify the most important variables that contribute to the level of sustainability of schools using supervised machine learning. Use the importance to assign a weight to each characteristic and calculate a sustainability index for each school.

ML-based prediction of NSCLC recurrence with gene expression data

Machine learning model developed in R to predict which water pumps across Tanzania are not working properly

"Heart Attack Risk Prediction" uses machine learning to estimate the likelihood of a heart attack based on user-provided data like physical attributes, symptoms, and medical history. This system enables remote screening, identifying high-risk individuals, and easing medical system burdens by providing early, data-driven health risk assessments.

This repository contains an ML workflow to predict house prices in Ames, Iowa. This project work is carried out under the Machine Learning module of the GeoDSc track of the Copernicus Master in Digital Earth.

Data Mining exercise of epitope prediction and classification of virus based on protein data

Sales Prediction for Rossmann Pharmaceutics database using Machine Learning regression modeling

CART, K-Means, Apriori, Adaboost, RFE; models using Anti-cancer peptides vs Human proteins

Credit Scoring from inputs to interpretation, see Streamlit_app repo to get to the dashboard

Project made for Advanced Methods in Machine Learning subject at MINI PW

A Credit Limit Classification Business Problem. We need to develop a model in 2 days to predict if a bank should concede or not give a higher credit limit to its clients.

Feature selection project for a student competition. Analyzing data for chemists at University of Southampton.

Klasyfikacja podtypów molekularnych ostrej białaczki szpikowej (AML) na podstawie danych RNA-seq przy użyciu uczenia maszynowego. Projekt zawiera pełny analizę (TCGA, Boruta, Random Forest) oraz interaktywną aplikację diagnostyczną wykonaną w R Shiny.

All-relevant feature selection with Boruta, Shiny and R

Bio-informed QSAR framework integrating P. falciparum transcriptomic signatures with molecular descriptors for enhanced antimalarial activity prediction (6.1% improvement, 98.3% feature reduction)

Classification model using R to predict the biodegradability of various chemicals

This web application is motivated by Baymax of the animated movie Big Hero 6. It detects Valvular Heart disorder i.e. damage or defect in one of the four heart valves. On the Machine Learning side, I have used AutoML from the deep learning platform H2O. And the interactive application is build in RShiny.