This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.

University Project for "Performance Evaluation of Computer Systems and Networks" course (MSc Computer Engineering @ University of Pisa). Supermarket simulator developed in OMNeT++ 5.7

Modeling and performance evaluation of an opportunistic cellular network. Developed using Omnetpp-6.0.1

Implementation and perfomance analisys of the conv2D algorithm in C++ and Cuda.

Heart Disease Prediction - UCI Heart Disease Dataset

Analysis and optimization of performance, memory and web coverage

In this project we make queries that query big data like the movielens dataset that is used here,then we run these experiments to compare perfomance at local machine clusters and at livy server clusters.

This project's main focus was in code perfomance so all code was thought to be as optimized as possible

Program for analyzing the performance of web pages

Determine which portfolio outperformed the others using an analysis python notebook and visualizes the major metrics of the portfolios.