There are 0 repository under voting-classifier topic.
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).
[ECCV-20] Official PyTorch implementation of HoughNet, a voting-based object detector.
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.
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
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
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.
Supervised Machine Learning Analysis Using Classification Models
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.
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.
Using Classification Techniques, Data reprocessing, Feature Engineering, Feature Extraction and Classification Algorithms from Machine Learning to Predict who can Survive the attack of Tsunami.
Tour of Machine Learning Algorithms for Binary/Multiclass Classification
A mobile application that diagnoses Parkinson’s disease patients using hand drawings
Classification model to predict the probability that a customer defaults based on their monthly customer statements using the data provided by American Express.
A machine learning model that predicts whether an email is spam or not.
A simple demo on how voting classifier is implemented in sklearn python
Advancing Cybersecurity with AI: This project fortifies phishing defense using cutting-edge models, trained on a diverse dataset of 737,000 URLs. It was the final project for the AI for Cybersecurity course in my Master's at uOttawa in 2023.
Classification
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
Compare the results of different voting methods
machine learning ensemble learning types in easy steps with examples
Android malware detection using machine learning.
Forecasting the likelihood of a customer defaulting their auto loan using classification models
Classification Project for SDAIA T5 Data Science Bootcamp. This project will choose the best classification model to predict whether a loan is a short-term loan or a long-term loan, based on some features.
A repository covering all my work on various topics and algorithms while learning Data Science, Machine Learning and Deep Learning.
1. Diabetes Prediction Using Ensemble Techniques 2. Customer Segmentation Using RFM & K-Means 3. Market Basket Analysis
TPs de la materia Organización de Datos - Catedra Collinet - FIUBA - 2C2020
Iris Species Classification usin various ML models.
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.
Simple Codes
This project implements an NLP-based solution for filtering out e-mails as ham or spam.
Stroke Prediction App