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The work in this project is aimed at building the best predictive model in the context of a classification problem: specifically, multiple deep learning architectures using convolutional neural networks (CNN) are tested with the aim of classifying images of objects commonly found in museums, divided into 5 categories: drawings, engravings, iconographies, paintings, and sculptures.
The aim of the project is to train a classifier that classifies, with the best possible performance, images of objects commonly found in museums, divided into 3 categories: artifacts, sculptures, paintings. Different classification methods are compared: KNN, Naive Bayes and Logistic Regression.
Visualization analysis of taxi and apps (Uber, Via, Lyft etc.) trips in New York City from 2009 to 2017
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