Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method

Deep Neural network using CNN pre-trained model to visually diagnose between 3 types of skin lesions

Evaluation of supervised predictions for two-class and multi-class classifiers

Machine-learning models to predict whether customers respond to a marketing campaign

Toolkit for Doing Research with ECMAScript-based Statistics (DRESS Kit)

The classification goal is to predict whether the client will subscribe (1/0) to a term deposit (variable y).

Survival modelling using Cox proportional hazard regression model

pyWitness performs data analyses and model fitting of recognition memory data, specifically eyewitness identification data.

Resample precision-recall curves correctly!

[Master Thesis 2017] Scripts for calculating metrics to assess performance of a drug design software.

Python code to obtain metrics like receiver operating characteristics (ROC) curve and area under the curve (AUC) from scratch without using in-built functions.

This project aims to predict the occurrence of diabetes using machine learning techniques. The dataset used for this analysis is the "diabetes_prediction_dataset.csv" file, which contains various features related to an individual's health condition.

These codes are written as a part of ECE219 Large Scale Data Mining course at UCLA.

Two-Dimensional Data Whitening and Receiver Operational Characteristic Determination

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

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

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

Classification-Techniques-For-Fraud-Detection