Model for Identification of Alzheimer's Disease by Brain MRI.

Stroke prediction project

NeuroMelNet - End-to-End binary classification model for AI speech detection with PyTorch implementation. Python API & Tg-bot API

An ML-based project designed to accurately classify email messages as either spam or ham (non-spam)

Refactory Final

This is a basic implementation of support vector machines(SVMs) and Perceptron for multi classes (MLP) based on their generalized closed form of formulas.

The project contains a CatBoost model that makes predictions according to an uploaded csv file using a flask wrapper.

Fast and customizable framework for automatic ML model creation (AutoML)

KANs for text classification on GLUE tasks

The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.

The code loads dog and cat images, extracts HOG descriptors, labels them, splits the data into training and test sets, trains an SVM model, and predicts a test image.

UCL PHAS0056 (Machine Learning for Physicists) Final Project. Applying ML techniques to the binary classification and energy reconstruction of simulated neutrino events in LArTPCs

Modèles de classification binaire.

A fast and frugal tree classifier for sklearn

A set of deep learning models for FRB/RFI binary classification.

This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.

A repository that contains ML algorithms and processing on a question paring dataset from Kaggle (Text Processing, Categorical Encoding, Advanced Feature Engineering, Binary Classification).

build a models that predicts whether an individual makes over $50,000 per year.

Lecture notes taken in the Quantitative Foundations of Artificial Intelligence class in Fall 2023, taught by Prof. Dr. Ludger Overbeck at Justus Liebig University Giessen.

Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data