Publications by Group_Project: Venkata Naga Vamsidhar reddy karasani(vkara4), Anila Cheekati(vchee3), Venkata sai ram tirunagari(Vtiru5) , Pradeep kumar Naidu(Pnaid2), Simhadri Ramanjaneyulu(rsimh3), Subhalaxmi Rout(srout2)
Project 1
Load library Loaded all necessary libraries. library(readxl) # read excel library(dplyr) # mutate library(PerformanceAnalytics) # correlation and histogram library(ggplot2) # ggplot library(forecast) # autoplot library(imputeTS) # impute NAs library(tseries) library(writexl) # excel We have a dataset in excel file, first read the data using rea...
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Week 2
library(fpp2) library(mlbench) library(psych) # skew library(corrplot) # correlation library(PerformanceAnalytics) # correlation and histogram library(DataExplorer) # histogram library(VIM) # kNN impute library(caret) # near zero variance library(dplyr) # arrange library(tidyr) # gather Forecasting: Principles and Practices (Chapter 7 - Exponent...
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Week1
library(fpp2) library(tsibble) Forecasting: Principles and Practices (Chapter 2 - Time serise graphics) 2.1 Use the help function to explore what the series gold, woolyrnq and gas represent. \((a)\) Use autoplot() to plot each of these in separate plots. ?gold ?woolyrnq ?gas autoplot(gold) autoplot(woolyrnq) autoplot(gas) \((b)\) What is the...
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ARIMA
library(knitr) library(fpp2) library(tseries) library(gridExtra) Forecasting: Principles and Practices (Chapter 8 - ARIMA Models) 8.1 Figure 8.3 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. knitr::include_graphics("https://raw.githubusercontent.com/SubhalaxmiRout002/DATA624/main/Week3/Screen%20Shot%202021-0...
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Project 1 Final
Group Members Subhalaxmi Rout Kenan Sooklall Devin Teran Christian Thieme Leo Yi Introduction The dataset for this project was provided as a de-identified excel spreadsheet that included six different groups. Each group had two variables to be forecasted 140 periods into the future using 1622 historical periods. This data had been provided as t...
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PH Prediction
Introduction We have been given a dataset from a beverage manufacturing company that consists of 2,571 rows of data and 33 columns. The dataset contains information on different beverages and their chemical composition. The goal of this analysis is to use the 32 predictive features to predict the Potential for hydrogen (pH), which is a measure of...
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Modeling Exercise - Oil Prediction
Instruction We are interested in the way you approach a data science problem and how you present and/or support your reasons with data. You are asked by the business to provide one day ahead probabilistic forecast (not a point forecast) for the oil prices. The requirement is to use the gamlss package in R ( https://cran.r-project.org/web/packages...
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Code Test
The purpose of this exercise is to design and implement an entire data preparation pipeline in R. We would like you to implement a robust, extensible and generic framework for data preparation. Questions 1) Take as raw inputs to the data preparation process, the oil data from the gamlss package. # load libraries library(gamlss) library(gamlss.a...
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