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Andrea Marinelli's Projects

california_house_price_analysis icon california_house_price_analysis

This project was undertaken as the culmination of our statistical learning course. Its primary objective was to utilize data from the 1990 U.S. Census to predict median house values, employing multiple regression models and advanced statistical analysis to attain precise predictions and gain valuable insights.

django_blog_app icon django_blog_app

In this project, I experimented with creating a blog using the Django framework. The project includes account management, post creation and display, and the use of templates and static files for design. It primarily utilizes Python, HTML, and CSS.

gradient-descent-and-bcgd-methods icon gradient-descent-and-bcgd-methods

This work was completed as homework for the Mathematical Optimization for Data Science course. The objective of the analysis was to compare Gradient Descent and Block Coordinate Gradient Descent, which I implemented from scratch and tested on publicly available datasets.

markowitz_portfolio_optimization icon markowitz_portfolio_optimization

This project was carried out as the final assignment for the Mathematical Optimization for Data Science course. The goal of the analysis was to compare two variants of the Frank-Wolfe Method with the Projected Gradient Method on the Markowitz portfolio optimization problem.

semantic_segmentation_models_for_fisheye_automotive_images icon semantic_segmentation_models_for_fisheye_automotive_images

This project is the final work for the Computer Vision course at the University of Padova. It focuses on the comparative analysis of two advanced semantic segmentation models applied to the WoodScapes dataset with fisheye images. The study uses transfer learning to adapt the models from the Cityscapes dataset to handle the radial distortion.

spotify_growth_analysis_time_series_forecasting icon spotify_growth_analysis_time_series_forecasting

This project analyzes the growth of an emerging artist on Spotify, examining data from January 2021 to October 2023. We explore key factors such as streams, playlist reach, and follower counts using time series forecasting m

youtube-downloader icon youtube-downloader

These Python scripts enable interaction with YouTube using the Pytube module and conversion of audio/video files to MP3 using the Pymovie module. They allow for easy downloading of YouTube videos and converting them or other media files into MP3 format, streamlining the process of extracting audio from video content.

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