Publications by Philip Tanofsky
DATA 624 Assignment 4
Do problems 3.1 and 3.2 in the Kuhn and Johnson book Applied Predictive Modeling. Exercise 3.1 The UC Irvine Machine Learning Repository 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 ...
7828 sym R (15445 sym/30 pcs) 8 img
DATA 624 Assignment 3
# Import required R libraries library(fpp3) Exercise 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: Definitions NAIVE - Set all forecasts to be the value of the last observation. Because a naive forecast is optimal when data follow a random walk. SNAIVE ...
10924 sym R (8540 sym/26 pcs) 18 img
DATA 624 Assignment 2
# Import required R libraries library(fpp3) Exercise 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? global_economy %>% autoplot(GDP/Population, show.legend=FALSE) + labs(title= "GDP per capita", y = "$US") T...
8424 sym R (10105 sym/31 pcs) 22 img
DATA 624 Assignment 1
# Import required R libraries library(fpp3) Exercise 2.1 Use the help function to explore what the series gafa_stock, PBS, vic_elec and pelt represent. gafa_stock: Historical stock prices from 2014-2018 for Google, Amazon, Facebook and Apple. All prices are in $USD. Contains data on irregular trading days. PBS: Monthly tsibble with two values, S...
5147 sym R (9523 sym/45 pcs) 12 img
DATA 624 Assignment 6
# Import required R libraries library(fpp3) Exercise 9.1 Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Section a Explain the differences among these figures. Do they all indicate that the data are white noise? Answer: The primary difference among the figures is the value of the bounds. From left ...
10905 sym R (22502 sym/116 pcs) 39 img
DATA 624 Project 2
Technical Report: Determinants of PH in Beverage Process Alexander Ng, Philip Tanofsky Due 12/13/2021 Introduction This technical report tunes multiple models for prediction of beverage PH and identifies determinants of PH among predictors in the manufacturing process. We train and test 5 models for this purpose including the Cubist, Gradient Boo...
20631 sym R (55962 sym/48 pcs) 23 img 21 tbl