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

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.

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.

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats

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

Heart Disease Prediction using machine and deep learning techniques works on heart dataset

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

Android malware detection using machine learning.

Classification

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