https://github.com/gugarosa/evolutionary_ensembles

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

aerial-scene ensemble-classifier implementation machine-learning meta-heuristic paper

Last synced: 19 Apr 2025

https://github.com/ahmedshahriar/customer-churn-prediction

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.

binary-classification catboost classification-models customer-churn-prediction ensemble-classifier hyperparameter-optimization kaggle lightgbm optuna pandas-python scipy stacking-ensemble xgboost

Last synced: 17 Jan 2025

https://github.com/arasgungore/multiclass-classification-using-ensemble-learning

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

bayessearchcv categorical-cross-entropy convolutional-neural-network convolutional-neural-networks decision-trees deep-learning deep-neural-networks ensemble-classifier ensemble-deep-learning ensemble-learning ensemble-machine-learning ensemble-model financial-machine-learning jupyter-notebook kfold-cross-validation lightgbm machine-learning machine-learning-algorithms manifold-learning multiclass-classification

Last synced: 12 Apr 2025

https://github.com/vasilikizarkadoula/machine-learning-auth-2021

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

decision-trees ensemble-classifier logistic-regression machine-learning stacking-ensemble voting-ensemble

Last synced: 20 Mar 2025

https://github.com/fatimaafzaal/multiple-ensemble-models-diabetes-prediction-project-

This project focuses on predicting the likelihood of diabetes in individuals using ensemble machine learning models. It combines various ensemble techniques, including Random Forest, AdaBoost, Gradient Boosting, Bagging, Extra Trees, XGBoost, Voting Classifier and some others to get predictions.

adaboost catboost colab-notebook diabetes diabetes-prediction ensemble-classifier ensemble-learning ensemble-machine-learning ensemble-model gradient-boosting machine-learning python stacking-classifier voting-classifier xgboost

Last synced: 05 Mar 2025

https://github.com/fatimaafzaal/diabetes-prediction-web-app

This repository contains the code for a web-based diabetes prediction application using a machine learning model. The application is built using Flask and allows users to input various health parameters to predict the likelihood of diabetes using ensemble voting classifier.

diabetes diabetes-prediction ensemble-classifier ensemble-learning ensemble-model flask flask-application logistic-regression machine-learning python random-forest support-vector-machines svm voting-classifier vscode

Last synced: 19 Jun 2025

https://github.com/souvikmajumder26/ieee-ecg-ensemble-xgboost

👨‍💻 Developed AI Models - Ensemble of Random Forest & SVM and XGBoost classifiers to classify five types of Arrhythmic Heartbeats from ECG signals - published by IEEE.

data-science ensemble-classifier ensemble-learning ensemble-machine-learning ensemble-model machine-learning matplotlib numpy pandas python python3 random-forest scikit-learn scikit-learn-python scikitlearn-machine-learning support-vector-machine svm svm-classifier svm-model xgboost

Last synced: 29 Mar 2025

https://github.com/sralter/happy_customers

Predicting whether a customer is happy based on the results from a survey.

eda ensemble-classifier hyperopt lazypredict ml scikit-learn

Last synced: 11 Mar 2025

https://github.com/cozek/profiling-fake-news-spreaders

This repo contains code related to our submission for the task of Profiling Fake News Spreaders in PAN at CLEF 2020

author-profiling ensemble-classifier fake-news pan2020 pytorch

Last synced: 30 Mar 2025

https://github.com/dhairyac/customer-churn-prediction

Analyze, visualize and predict customer churn using Machine Learning

data-analysis data-visualization ensemble-classifier machine-learning performance-metrics python-3 random-forest-classifier softmax-regression svm-classifier

Last synced: 16 Mar 2025

https://github.com/macabdul9/flowers102-recognition

competitive-data-science convolutional-neural-networks deep-learning deep-learning-algorithms ensemble-classifier flower-classification neural-networks transfer-learning

Last synced: 22 Jun 2025

https://github.com/uudigitalhumanitieslab/multiner

A multiNER websevice based on the KB's multiNER

ensemble-classifier named-entity-recognition

Last synced: 23 Mar 2025

https://github.com/fatimaafzaal/diabetes-prediction-project-using-random-forest

This project focuses on predicting the likelihood of a person having diabetes based on various health-related attributes using Random Forest Algorithm

colab-notebook ensemble-classifier ensemble-learning ensemble-machine-learning ensemble-model machine-learning machine-learning-algorithms python random-forest

Last synced: 05 Mar 2025

https://github.com/ironvenom/pulsar-classification

Implementation of various classification models for Pulsar Classification.

classification-models ensemble-classifier python sklearn

Last synced: 11 Jun 2025

https://github.com/thomaslemontagner/minst_digit_recognizer

My notebooks for the Digit Recognizer Kaggle competition https://www.kaggle.com/c/digit-recognizer

cnn-classification ensemble-classifier random-forest

Last synced: 12 Jun 2025

https://github.com/mohanwu/kaggle--porto-seguro

Data analysis on Kaggle competition Porto-Seguro

classification ensemble-classifier kaggle-competition

Last synced: 14 Jun 2025

https://github.com/lucasalegre/random-forest

Implementation of Random Forests (ensemble).

classification ensemble-classifier machine-learning random-forest-classifier

Last synced: 27 Mar 2025

https://github.com/snigdho8869/diabetes-prediction-using-ml-dl

This repository contains machine learning and deep learning models for predicting the onset of diabetes in patients. The models used include Logistic Regression, SVM, Random Forest, Ensemble Learning, Adaptive Boosting, and Deep Neural Networks. The performance of the models is compared, and the best performing model is identified.

adaboost boosting-algorithms deep-neural-networks dl ensemble-classifier ensemble-learning ensemble-machine-learning gradient-boosting keras-tensorflow logistic-regression machine-learning ml random-forest random-forest-classifier support-vector-machine svm voting-classifier

Last synced: 18 Jun 2025