Publications by Chun San Yip
Data624 Project2
Project Requirements 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 predictive model of P...
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Data624 HW10
Market Basket and Clusters Imagine 10000 receipts sitting on your table. Each receipt represents a transaction with items that were purchased. The receipt is a representation of stuff that went into a customer’s basket - and therefore ‘Market Basket Analysis’. That is exactly what the Groceries Data Set contains: a collection of receipts ...
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Data624 HW9
Chapter 8 Regression Trees and Rule-Based Models Do problems 8.1, 8.2, 8.3, and 8.7 in Kuhn and Johnson. Ex 8.1 8.1. Recreate the simulated data from Exercise 7.2: library(AppliedPredictiveModeling) library(mlbench) library(caret) set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simul...
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Data624 HW8
In Kuhn and Johnson do problems 7.2 and 7.5 Ex 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 = 10 sin(πx1x2) + 20(x3 − 0.5)2 + 10x4 + 5x5 + N(0, σ2) where the x values are random variables uniformly distributed between [0...
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Data624 HW7
In Kuhn and Johnson do problems 6.2 and 6.3 Ex 6.2 Permeability 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: (a) Start R and use these commands to load...
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Data624 Project1
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 more business feeling. Explain and dem...
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Data624 HW6
Do exercises 9.1, 9.2, 9.3, 9.5, 9.6, 9.7, 9.8 in Hyndman. Ex 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? A white noise series is stationary — it does not matter when you observe it, it sho...
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Data624 HW5
Do exercises 8.1, 8.5, 8.6, 8.7, 8.8, 8.9 in Hyndman. Ex 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 ℓ0, and generate forecasts for the next four months. b. Co...
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Data624 HW4
Do problems 3.1 and 3.2 in the Kuhn and Johnson book Applied Predictive Modeling. Ex 3.1 The UC Irvine Machine Learning Repository6 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 eig...
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Data624 HW3
Do exercises 5.1, 5.2, 5.3, 5.4 and 5.7 in the Hyndman book. Ex 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 f...
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