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bigdatacomputing-unipd

Homework projects for the "Big Data Computing" course at UniPd. The overall objective is to explore and utilize parallelism using Apache Spark.

churn-prediction

computervision-assignments

Programming assignments for "Computer Vision and Cognitive Systems" course at UniPd

crowdcounting_xception_csrnet

Address the crowd counting problem on the Mall dataset (sparse) by exploring regression-based (Xception) and density-based (CSRNet) approaches.

deep-belief-network

deeplearning-assignments

eeg-emotion-classification

Emotion recognition from EEG data (Bachelor's thesis), using the DEAP dataset. Performed manual feature selection across three domains: time, frequency, and time-frequency. Used different classifiers, including XGBoost, AdaBoost, Random Forest, k-NN, SVM, etc.

font-classification

ibm-course-analyzingdatawithpython

ibm-course-datavisualization

ibm-course-pythonprojectfordatascience

lstm_yfinance

meb-anomaly-fw-optimization

Implementation of three variants of the Frank-Wolfe method for solving the Minimum Enclosing Ball problem, and application to anomaly detection.

optimisation-bcgd-semisupervised

Comparison of Gradient Descent and Block Coordinate Gradient Descent methods through a semi supervised learning problem.

pluralsight-course-buildingyourfirstmachinelearningsolution

statistical-analysis-lifeexpectancy

A comprehensive Statistical Analysis in R of a Life Expectancy dataset, covering Data Wrangling, EDA (Visualizations and Hypothesis Testing), and Supervised Learning (Regression and Classification).

time-series-forecasting-museum-visitors

Analyze and forecast time series data on the number of visitors to various Italian museums using techniques such as TimeGPT, SARIMAX, Holt-Winters, Lasso, Gradient Boosting, GAM, etc.

udemy-course-completepython3andpandasdatasciencemasterclass

web-scraping-mkd-stock

webscraping-mymoviesit

Script for scraping MYmovies.it using BeautifulSoup

yashi-game-sat-logic

Solving the Yashi game using a combination of graph theory and SAT-solving. First, connectivity of the graph is verified, and if true, identification of all possible cycles is done. Then, after generating all necessary constraints, they are passed to a SAT solver.