Publications by Deepa Sharma
Project 2
Project Introduction: New regulations are requiring ABC Beverage to provide a report with an outline of our manufacturing process, and a predictive model of PH including an explanation of predictive factors. Our data science team is tasked with developing the predictive model from provided historical data and using that model to predict PH on te...
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data624 hw9
Do problems 8.1, 8.2, 8.3, and 8.7 in Kuhn and Johnson. Please submit the Rpubs link along with the .rmd file. Excercise 8.1. Recreate the simulated data from Exercise 7.2: 7.2. Friedman (1991) introduced several benchmark data sets create by simulation. One of these simulations used the following nonlinear equation to create data: y =10sin(πx...
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Data 624 hw8
Do problems 7.2 and 7.5 in Kuhn and Johnson. There are only two but they have many parts. Please submit both a link to your Rpubs and the .rmd file. Excercise:7.2 Friedman (1991) introduced several benchmark data sets create by sim- ulation. One of these simulations used the following nonlinear equation to create data: 22 y = 10sin(πx1x2) + 2...
4368 sym Python (28578 sym/55 pcs) 6 img
HW#7 data 624
Excercise 6.2: Developing a model to predict permeability (see Sect.1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: Part a: Start R and use these commands to load the data: > library(AppliedPredictiveModeling) > d...
6294 sym R (13585 sym/96 pcs)
Project 1
Part A: ATM Forecast, ATM624Data.xlsx In part A, I want you to forecast how much cash is taken out of 4 different ATM machines for May 2010. The data is given in a single file. The variable ‘Cash’ is provided in hundreds of dollars, other than that it is straight forward. I am being somewhat ambiguous on purpose to make this have a little m...
11323 sym Python (21569 sym/118 pcs) 31 img
hw6
Excercise:9.1: Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. a.Explain the differences among these figures. Do they all indicate that the data are white noise? knitr::include_graphics("C:/Users/dkbs0/OneDrive/Desktop/Data 624/Week 7/Figure 9.32 .png") Left Plot (36 Numbers): This ACF plot exhibi...
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Data 624 hw5
Excercise 8.1: Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. a.Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of α and lo , and generate forecasts for the next four months. b.Compute a 95% prediction interval for the first fore...
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Data 624 hw4
Excercise3.1. 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 eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. Using visualizations, explor...
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Data 624 HW3
Excercise 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). # Count missing values per co...
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Data 624 HW 2
Excercise 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? head(global_economy) ## # A tsibble: 6 x 9 [1Y] ## # Key: Country [1] ## Country Code Year GDP Growth CPI Imports Exports Populat...
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