mlim: single and multiple imputation with automated machine learning

Classification model was created to conduct an analysis that can detect the Non- Human Traffic presence on website using, Gradient Boosting Classifier & RF.

Snakemake pipeline to reproduce the results in the forthcoming paper "One week ahead prediction of harmful algal blooms in Iowa lakes"

Unofficial pytorch implementation on DAR-BN

Using deep learning models from hungingface ,fine tune them and creating streamlit app for sentiment analysis

WeLT TablERT-CNN Trainer

WeLT-SpERT Model

WeLT-ASpERT Trainer

The aim of this project is to predict fraudulent credit card transactions using machine learning models.

Build a custom convolutional neural network neural network from scratch in Tensorflow to identify the type of skin cancer from image.

This repository implements machine learning models for predicting industrial machine failures, addressing class imbalance using SMOTE and employing ensemble methods like Random Forest and XGBoost. It includes data preprocessing, model training and evaluation to improve predictive maintenance.

A Comparative Analysis of Machine Learning Models for Credit Card Transactions with an Emphasis on Maximizing Recall.