Publications by Semyon Toybis
Project2Data624
library(readxl) library(caret) library(tidyverse) library(VIM) library(ggplot2) library(GGally) library(car) library(glmnet) Abstract We are tasked with with analyzing the manufacturing process of a beverage company, ABC Beverage. Specifically, we are tasked with analyzing what factors in the process can help us predict the PH of a beverage. We tr...
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HW9DATA624
HW9DATA624 Author Semyon Toybis Assignment We are required to complete questions 8.1, 8.2, 8.3, and 8.7 from chapter 8 of “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson. 8.1 First, I recreate the simulated data as instructed per the text book: set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simula...
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HW8DATA624
HW8DATA624 Author Semyon Toybis Assignment We are required to complete questions 7.2 and 7.5 from chapter 7 of “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson. 7.2 We are tasked with tuning models on simulated data via the Friedman1 function from the mlbench package. Below, I use the code provided in the book to create the data...
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HW7DATA624
HW7DATA624 Author Semyon Toybis Assignment We are required to complete questions 6.2 and 6.3 from chapter 6 of “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson. 6.2 A Below I load the permeability data set which contains the matrix “fingerprints” and a matrix for permeability. data("permeability") Fingerprints contains bi...
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Project1DATA624
Project1 Author Semyon Toybis Project 1 A - ATM Data We are tasked with forecasting how much cash is taken out of four different ATM machines for the month of May 2010. First, I import the data: atm_data <- read_xlsx('ATM624Data.xlsx', col_types = c('date','text','numeric')) head(atm_data) # A tibble: 6 × 3 DATE ATM Cas...
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HW5DATA624
HW6DATA624 Author Semyon Toybis Assignment We are required to complete questions 9.1, 9.2, 9.3, 9.5, 9.6, 9.7, and 9.8 from chapter 9 of “Forecasting: Principles and Practice” Third Edition by Rob Hyndman and George Athanasopoulos. 9.1 A The above plots are autocorrelation plots for three different time series, showing the autocorrelation...
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DATA 624 ARIMA Demo
Plot & Transform Source Data We use federal unemployment rate data located at FRED - https://fred.stlouisfed.org/series/UNRATE. This is US Monthly Unemployment Data. library(ggplot2) library(tsibble) library(tidyverse) library(lubridate) library(forecast) library(tseries) library(fabletools) library(fable) library(feasts) library(kableExtra) librar...
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HW5DATA624
HW5DATA624 Author Semyon Toybis Assignment We are required to complete questions 8.1, 8.5, 8.6, 8.7, 8.8, and 8.9 from chapter 8 of “Forecasting: Principles and Practice” Third Edition by Rob Hyndman and George Athanasopoulos. 8.1 - Victoria pigs from aus_livestock aus_livestock |> filter(Animal=='Pigs', State=='Victoria') |> autoplot() ...
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HW4DATA624
hw4DATA624 Author Semyon Toybis Assignment We are required to complete questions 3.1 and 3.2 from chapter 3 of “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson. 3.1 - UC Irvine Machine Learning - Glass Data A - exploring the data As described in the book, the data consists of 214 glass samples labeled as one of seven categories...
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HW3_DATA624
HW3DATA624 Author Semyon Toybis Assignment We are required to complete questions 5.1, 5.2, 5.3, 5.4, and 5.7 from chapter 5 of “Forecasting: Principles and Practice” Third Edition by Rob Hyndman and George Athanasopoulos. 5.1 We are tasked with using NAIVE(y), SNAIVE(y) or RW(y ~ drift()), which ever is most appropriate, for a variety o...
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