Publications by Jagdish Chhabria
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Lasso Regression In Linear regression, the model is not penalized for its choice of weights, at all. As a result, during the training stage, if the model considers one particular feature to be particularly important, it may place a large weight on that feature i.e. derive a large value for its associated co-efficient. This can sometimes lead to ...
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Lasso Regression In OLS regression, the model is not penalized for its choice of weights, at all. As a result, during the training stage, if the model considers one particular feature to be particularly important, it may place a large weight on that feature i.e. derive a large value for its associated co-efficient. This can sometimes lead to ove...
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Assignment Overview / Problem Statement This is role playing. I am your new boss. I am in charge of production at ABC Beverage and you are a team of data scientists reporting to me. My leadership has told me that new regulations are requiring us to understand our manufacturing process, the predictive factors and be able to report to them our pred...
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title: “DS624_Project1_JagdishChhabria” author: “Jagdish Chhabria” date: “10/29/2021” output: html_document: default 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...
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##8.1. Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. ###Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the optimal values of α and ℓ0, and generate forecasts for the next four months. ## Series: Count ## Model: ETS(A,N,N) ## Smoothing param...
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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 eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. ## 'data.frame': 214 obs. of 10 vari...
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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? Since there are a lot of countries in this dataset, the legend takes up too much space. ## # A tibble: 58 x 5 ## # Groups: Year [58] ## Country Year ...
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DATA624_HW1_Chapter2_Jagdish
2.1 Use the help function to explore what the series gafa_stock, PBS, vic_elec and pelt represent. The gafa_stock time series represents historical stock prices from 2014-2018 for Google, Amazon, Facebook and Apple. All prices are in $USD. The PBS time series represents monthly Medicare Australia prescription data. It comprises data for total num...
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#?global_economy #?aus_production #?aus_livestock #?hh_budget tail(aus_retail) 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) aus.pop<-global_economy%>%filter(Country=="Australia") aus.pop%>%autoplot(Population/...
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title: “DS624_HW6_JagdishChhabria” author: “Jagdish Chhabria” date: “10/14/2021” output: pdf_document: default html_document: null toc_float: yes toc_collapsed: yes toc: yes toc_depth: 3 number_sections: yes theme: lumen 9.1) Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. a....
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