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.

Use machine learning models to detect lies based solely on acoustic speech information

Some example projects that was made using Tensorflow (mostly). This repository contains the projects that I've experimented-tried when I was new in Deep Learning.

This contains the Jupyter Notebook and the Dataset for the mentioned Classification Predictive Modeling Project

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.

This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.

Pixel based classification of satellite imagery - feature generation using Orfeo Toolbox, feature selection using Learning Vector Quantization, CLassification using Decision Tree, Neural Networks, Random Forests, KNN and Naive Bayes Classifier

Detection of Object-Based Forgery in Advanced Video

Pusion (Python Universal Fusion) is a generic and flexible framework written in Python for combining multiple classifier’s decision outcomes.

In this project, the success results obtained from SVM, KNN and Decision Tree Classifier algorithms using the data we have created and the results obtained from the ensemble learning methods Random Forest Classifier, AdaBoost and Voting were compared.

📄 Official implementation regarding the paper "Creating Classifier Ensembles through Meta-heuristic Algorithms for Aerial Scene Classification".

Multiple Model Ensembling

Building the best machine learning model to detect phishing websites.

It is the nlp task to classify empathetic dialogues datasets using RoBERTa, ERNIE-2.0 and XLNet with different preprocessing method. You can get some detailed introduction and experimental results in the link below.

Used ensemble methods such as boosting, voting, Bagging

Two ensemble models made from ensembles of LightGBM and CNN for a multiclass classification problem.

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.

How to Train an Ensemble of Convolutional Neural Networks for Image Classification (Article on Medium)

Machine Learning codes

Predicting Workload in Virtual Flight Simulations using EEG Spectral and Connectivity Features