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

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.

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

Analysis and optimization of performance, memory and web coverage

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

A full-stack web application for analyzing and predicting insurance agent performance using machine learning.

Heart Disease Prediction - UCI Heart Disease Dataset

Simple Python sample-profiler running in Docker.

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

Program for analyzing the performance of web pages

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.