voting-classifier

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.

Contains code for a voting classifier that is part of an ensemble learning model for tweet classification (which includes an LSTM, a bayesian model and a proximity model) and a system for weighted voting

To design a predictive model using xgboost and voting ensembling techniques and extract insights from the data using pandas, seaborn and matplotlib

Classifying Audio to Emotion

Using Classification Techniques, Data reprocessing, Feature Engineering, Feature Extraction and Classification Algorithms from Machine Learning to Predict who can Survive the attack of Tsunami.

Fake News Detection System for detecting whether news is fake or not. The model is trained using "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. Link for dataset: https://arxiv.org/abs/1705.00648.

A mobile application that diagnoses Parkinson’s disease patients using hand drawings

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

A simple demo on how voting classifier is implemented in sklearn python

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.

In this project, algorithms such as Svm, Knn, Decision Tree were trained and performance results were recorded with the data sets we created. Later, community learning algorithms such as Random Forest, AdaBoost and Voting were trained, performance results were recorded and these performance results were compared and analyzed.

Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

Rock The Poll: A SXSW Hackathon Entry 2018

A repository covering all my work on various topics and algorithms while learning Data Science, Machine Learning and Deep Learning.

TPs de la materia Organización de Datos - Catedra Collinet

Simple Codes

Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department

This project implements an NLP-based solution for filtering out e-mails as ham or spam.

To implement an Ensemble Classifier of Logistic regression, Naive Bayes & Decision Tree. The goal is to implement a classification model to predict the “Status” feature in the provided Parkinson dataset.

Voting Classifier and Artificial Neural Network for predicting heart disease.

Supervised Algorithms For The Detection Of COVID-19 From Chest CT & X-ray Scan Images

This project uses ensemble method models of decision trees, voting classifier, support vector machines, adaboost, logistic regression, dummy classifier, and bagging classifier to predict malignant or benign cells for breast cancer.

Application for Prediction of Acute Liver Failure using Machine Learning Algorithm.

Aulas do curso Dominando Data Science da Flai.

Document Classification using Python and scikit-learn and nltk

Built a classifier for evaluating quality of machine translation to predict best matching sentence to the reference sentence