Publications by Omar Pineda, Calvin Wong, Murali Kunissery
Data 624 HW3
library(fpp2) Question 6.2 The plastics data set consists of the monthly sales (in thousands) of product A for a plastics manufacturer for five years. a.Plot the time series of sales of product A. Can you identify seasonal fluctuations and/or a trend-cycle? autoplot(plastics) + ggtitle("Sales of product A for a plastics manufacturer") + ...
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Data 612 Recommender Project 1
Description Restaurant Rating Recommender System This is a recommender system with 20 users and their respective ratings on 5 restaurants in New York It recommends restaurants to users based on other user ratings. Dataset Step-1: I scrapped some data from online to get this rating on the 5 restaurants Load Data Step-2: Load the necessary libr...
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Data 624 HW2
Question 3.1 For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance. usnetelec usgdp mcopper enplanements library(fpp2) library(ggplot2) Question 3.1.1 autoplot(usnetelec) + ylab("billion kwh") + xlab("Year") + ggtitle("Annual US net electricity generation (billion kwh) for 194...
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Data 624 Predictive Analytics - HW1
Predictive Analytics Exercise Use the help function to explore what the series gold, woolyrnq and gas represent. Use autoplot() to plot each of these in separate plots. What is the frequency of each series? Hint: apply the frequency() function. Use which.max() to spot the outlier in the gold series. Which observation was it? : library(fpp2) gol...
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Data 624 HW4
Question 3.1 The UC Irvine Machine Learning Repository 6 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 612 Recommender Project 2
library(recommenderlab) library(reshape2) library(RCurl) library(dplyr) library(ggplot2) library(knitr) library(tidyverse) #Read the Movie data movies.df <- read.csv('movies.csv',header = TRUE, stringsAsFactors = FALSE) kable(head(movies.df)) movieId title genres 1 Toy Story (1995) Adventure|Animation|Children|Comedy|Fantasy 2 Jumanji ...
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Data 624 HW 6
Homework 6 Question 8.1 1.Figure 8.31 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Explain the differences among these figures. Do they all indicate that the data are white noise? These pictures show the correlation between different lags of the series (shown on the x-axis). The y-axis (the correlation) has...
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