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

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

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.

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.

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.

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.

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.

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

This repository demonstrates the project dissertation topic at Kingston University (London)

Forecasting the likelihood of a customer defaulting their auto loan using classification models

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

A model build on RAVDESS dataset, for speech emotion recognition. 85.59% validation accuracy

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.

Classification model to predict the probability that a customer defaults based on their monthly customer statements using the data provided by American Express.