✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation

The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

Landscape of ML/DL performance evaluation metrics

A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

Evaluating machine learning methods for detecting sleep arousal, bachelor thesis by Jacob Stachowicz and Anton Ivarsson (2019)

calculate ROC curve and find threshold for given accuracy

Analytical tool to help the company decide whether the employee will stay or not

A dataset containing over 70,000 data points, 12 features, and one target variable were used to analyze if machine learning could predict if an individual has cardiovascular disease.

INN Hotels Project

Udacity DSND capstone project on the Bertelsmann-Arvato challenge on customer segmentation report and supervised learning model.

The objective of this capstone project is to use Natural Language Processing (NLP) to create a machine-learning model that predicts the quality of questions posted on Stack Overflow, a popular question-and-answer platform for software developers.

This project aims to study the influence factors of international students' mobility with the case of international students from B&R countries studying in China.

Détecter les faux billets à partir du jeu de données englobant statistiques sur 6 caracthéristiques des billets

This problem is a typical Classification Machine Learning task. Building various classifiers by using the following Machine Learning models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Light GBM and Support Vector Machines with RBF kernel.

This code includes reading the data file, data visualization, variable splitting, model building, prediction and different metrics calculation using knn.

This repository contains code and documentation for a machine learning project focused on predictive maintenance in industrial machinery. The project explores the development of a comprehensive predictive maintenance system using various machine learning techniques.

Supervised Classfication models - Logistic Regression & Decision Tree, AUC-ROC Curve

This code includes reading the data file, data visualization, variable splitting, model building, prediction and different metrics calculation using knn.