✋🏼🛑 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

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.

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

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

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

No description provided.

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.

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.

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.

INN Hotels Project

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

A machine learning application deployed on Streamlit Cloud that predicts the likelihood of fraudulent transactions. Users input transaction details, the app analyzes key features, displays prediction results and confidence, and offers an interactive, modern interface built with Streamlit and Plotly.

Repository where it is intended to address the detection of patients who will be readmitted for problems with diabetes.

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.

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

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.

Analyze the data of INN Hotels to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Decision Tree classifier using Logistic Regression

This is an contrast of linear regression model, used to examine the association between the independent variable(category or contineous) with dependent variable(binary), which is an discrete outcome.

Streamlit-based KNN classification app with real-time predictions, performance metrics, and visual insights.

The task is to predict the value of target column in the test set.

A Comprehensive Guide to Titanic Machine Learning from Disaster

A hotel chain is having issues with cancellations. This project analyzes customer booking data to identify which factors significantly influence cancellations, build models using logistic regression and decision trees to predict cancellations in advance, and help formulate profitable policies for cancellations and refunds for the hotel group

We conduct cluster analysis on customers and machine learning to predict if a customer will receive insurance benefits.