Francesco Petrini

Neural Network models created in junction with an online course.

This repository contains my COEN 169 Web Information Management Project. Essentially, this project mimicked the Netflix Prize competition in which Netflix challenged teams to beat their recommendation algorithm. My project utilizes several methods to calculate vector similarity including cosine similarity, Pearson correlation, and item-based cosine similarity. Additionally it has the functionality to incorporate Inverse User Frequency and Case modification for improved results. The source code is still a little messy and work is still ongoing!

This repository contains a collection of material I used to study for CS/COEN interviews. It contains everything from solutions to common interview questions, some solutions to CTCI, data structure implementations, sorting and searching algorithms, etc. I hope this helps whoever finds it!

Face recognition and classification using k-NN after using principal component analysis (PCA). Scikit Learn was used as the ML framework for this project.