State-of-the art Automated Machine Learning python library for Tabular Data

Time Series Ensemble Forecasting

This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.

This is a induction motor faults detection project implemented with Tensorflow. We use Stacking Ensembles method (with Random Forest, Support Vector Machine, Deep Neural Network and Logistic Regression) and Machinery Fault Dataset dataset available on kaggle.

Learning with Subset Stacking

🏆데이콘 AI해커톤 대회 우수상 솔루션🏆

Binary Classification for detecting intrusion network attacks. In order, to emphasize how a network packet with certain features may have the potentials to become a serious threat to the network.

Kaggle Competition

Identify the type of disease present on a Cassava Leaf image

There are many studies done to detect anomalies based on logs. Current approaches are mainly divided into three categories: supervised learning methods, unsupervised learning methods, and deep learning methods. Many supervised learning methods are used for log-based anomaly detection.

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

Wind Power Prediction using Stacking Ensemble Machine Learning Algorithm

A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease

Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.

A machine learning model to predict whether a customer will be interested to take up a credit card, based on the customer details and its relationship with the bank.

Εxercises for Machine Learning course in Faculty of Informatics of Aristotle's University of Thessaloniki

EasyVisa Project

VoiceVibes presents a solution for speech emotion recognition, featuring six innovative AI model architectures designed to accurately categorize emotional expressions conveyed through speech.

Machine Learning Application In The Medical Field Used for predicting the occurrence of stroke for the patients depends on the patient information