Publications by Stefano Biguzzi
DATA 607: Week 11 Assignment - Recommender Systems
Week 11 Assignment - Recommender Systems Stefano Biguzzi 11/03/2020 Introduction and Assignment Prompt Your task is to analyze an existing recommender system that you find interesting. You should: Perform a Scenario Design analysis as described below. Consider whether it makes sense for your selected recommender system to perform scenario desig...
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DATA 607: Week 10 Assignment - Sentiment Analysis
Loading Libraries library(tidyverse) library(tidytext) library(janeaustenr) library(stringr) library(wordcloud) library(reshape2) library(gutenbergr) Part I - Sentiment analysis with tidy data The following section is code from Chapter 2 of Text Mining with R: A Tidy Approach by Julia Silge and David Robinson1 2.1 The sentiments dataset g...
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DATA 607: Project 4 - Document Classification
Project 4 - Document Classification Stefano Biguzzi 11/11/2020 Project guidelines It can be useful to be able to classify new “test” documents using already classified “training” documents. A common example is using a corpus of labeled spam and ham (non-spam) e-mails to predict whether or not a new document is spam. For this project, you...
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DATA 605 - Assignment 4: Eigenimagery
Loading Libraries Necessary for Assignment # Loading Libraries library(doParallel) library(foreach) library(EBImage) library(jpeg) library(kableExtra) library(OpenImageR) library(imager) setwd("C:/Users/biguz/Desktop/CUNY Data Science/Spring2021/DATA 605/Assignments/Assignment 4/") Creating the plot_jpeg Function # plot_jpeg Function pl...
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DATA 624 - Project 1
Introduction This project consists of 3 parts - two required and one bonus and is worth 15% of your grade. The project is due at 11:59 PM on Sunday Apr 11. I will accept late submissions with a penalty until the meetup after that when we review some projects. I am giving you data via an excel file, please provide your written report on your findi...
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DATA 624 - HW 6
Question 9.1 Figure 9.32: Left: ACF for a white noise series of 36 numbers. Middle: ACF for a white noise series of 360 numbers. Right: ACF for a white noise series of 1,000 numbers. Part a Explain the differences among these figures. Do they all indicate that the data are white noise? All the ACF plots in figure 9.32 indicate that the data are...
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DATA 624 - HW4
Question 3.1: The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. The data can be accessed via: ...
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DATA 624 - HW3
Question 5.1 Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: Australian Population (global_economy). Bricks (aus_production). NSW Lambs (aus_livestock). Household wealth (hh_budget). Australian takeaway food turnover (aus_retail). Part I: Australian Populatio...
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DATA 624 - HW2
Loading Libraries library(fpp3) Question 3.1 Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? The two highest GPDs per capita are Monaco and Liechtenstein. They have a clear upward trend for their respective GDPs per ...
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DATA 624 - HW1
Loading libraries library(fpp3) Question 2.1 Part A: Use autoplot() to plot some of the series in these data sets. gafa_stock shows the daily open close prices of different stocks. Below is a graph showing the daily high price for each stock. PBS shows sales data on pharmaceuticals in Australia. Below we have a graph showing the total co-pay c...
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