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

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.

Time Series Ensemble Forecasting

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.

Learning with Subset Stacking

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…

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.

Identify the type of disease present on a Cassava Leaf image

Notebook files for classification and detection of anomalous trend in time series inverter data

Based on data such as general bio-data, payment history, and subscriptions, this stacking-ensemble model predicts whether a customer continues to use the service or not (attrition) with an accuracy of 83.14%

Implementation of Ensemble Learning, Decision Tree, Random Forest, SVM, KNN, Logistic Regression, Bagging, Boosting and Stacking approach to analysis and predict the abnormal and normal behavior of Imbalanced Colon Dataset.

This project is dedicated to accurately classify Alzheimer's disease into Demented, Non-demented and Converted Category.

A deep convolutional network made of stacked feature extractors

Ensembles of machine learning models

성균관대학교 우수학부생 연구학점제를 통해 작성한 논문입니다.

Language classification problem: which identifies South Africa's 11 official Languages through text data

EasyVisa Project

An NLP research project utilizing the "cardiffnlp/twitter-roberta-base-sentiment-latest" pre-trained transformer for tweet tokenization. The project includes an attention-based biLSTM model that predicts sentiment labels for tweets as negative (-1), neutral (0), or positive (1).