Explore how certain hyperparameters and features in a logistic regression model affect image classification

The repository contains the code for the various machine learning algorithms used to make a predictive analysis of tweets on GST in India

- Nesse trabalho vou explorar uma base vista em projetos passados, diabetes dataset. - Nela encontramos informações sobre algumas características de pacientes. Queremos estudar as características das pacientes e encontrar possíveis relações

Includes all of my Jupyter Notebook assignments from my time at MIT's Break Through AI/ML Program.

Leveraging and comparing various ML techniques to forecast credit card defaults [Imbalanced data]

Human Resources Analytics

Classification-Techniques-For-Fraud-Detection

Wolfram Language (aka Mathematica) paclet for Receiver Operation Characteristic (ROC) functions.

Credit card fraud detection based on the Kaggle dataset.

This repository trains and evaluates three CNN models on MNIST, providing performance comparisons and 5 unique visualizations.

This repository basically contains all the projects that I have carried out while learning Machine Learning on DataCamp.

[IEEE ATC 2017] "On the overall ROC of multistage systems". In IEEE International Conference on Advanced Technologies for Communications, 2017.

Light-weight package for classification metrics computed on streams or minibatches of data. Mainly for area under the curve (AUC) of precision-recall (PR) or receiver operating characteristic (ROC) curves. Supports multi-class setting with either macro- or micro aggregation..

This notebook describes how to compute and derive insights from various classification evaluation metrics.

Using 21 predictor variables and applying simple Logistic Regression, predicting whether a particular customer will switch to another telecom provider or not. In telecom terminology, this is referred to as churning and not churning, respectively.

Learning Machine Learning Through Data

A Shiny Application to Evaluate Diagnostic Tests Performance. It allows users to compute key performance indicators and visualize ROC curves, determine optimal cut-off thresholds, display confusion matrix, and export publication-ready plots. It supports both binary and continuous test variables.

Machine learning tutorial 'iris'

This project contains codes and paperwork based on the course CSI5155 at University of Ottawa (delivered by Professor Dr. Herna Viktor).