Publications by Shoshana Farber
DATA 622 - Homework 4
Load Data The data is taken from Kaggle and can be accessed here. The data contains medical records of 5,000 patients who had heart failure, collected during their follow-up period. Each patient profile contains information for the following clinical features: Variable Description age age of the patient (years) anaemia decrease of red blood c...
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DATA 622 - Homework 3
Data In homework 2, I used a dataset of responses from an airline satistfaction survey to create a decision tree and a random forest model to predict customer satisfaction. The dataset was taken from Kaggle and can be found here. It contains the results of an airline passenger satisfaction survey. The dataset consists of 129,880 rows and 24 col...
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DATA 624 - Homework 9
Kuhn and Johnson Chapter 8 Exercise 8.1 Recreate the simulated data from Exercise 7.2: set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" (a) Fit a random forest model to all of the predictors, then estimate th...
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DATA 624 - Homework 8
Kuhn and Johnson, Chapter 7 Exercise 7.2 Friedman (1991) introduced several benchmark data sets create by simulation. The package mlbench contains a function called mlbench.friedman1 that simulates these data. set.seed(200) trainingData <- mlbench.friedman1(200, sd = 1) # We convert the 'x' data from a matrix to a data frame # One reason is th...
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DATA 622 - Homework 2
Data The dataset was taken from Kaggle and can be found here. It contains the results of an airline passenger satisfaction survey. The dataset consists of 129,880 rows and 24 columns. The predictor variables include passenger details, such as gender, whether they are a loyal customer, age, type of travel, and which class they were sitting in, a...
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DATA 624 - Homework 7
Kuhn and Johnson, Chapter 6 Exercise 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 the data: data(permeability...
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DATA 624 - Project 1
Part A - ATM Forecast Prompt 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. Explain and demonstrate your process, techniques used and not used, and your actual forecast. Provide your written report on your findings, visuals,...
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DATA 624 Homework 6
Exercise 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? The plots differ in the size of the autocorrelations and the confidence intervals (blue lines) for each. The confidence interval narrows ...
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DATA 622 - Homework 1
Exploratory Analysis Data Loading Both datasets were found on Kaggle. The first dataset contains attributes relating to apple quality and can be accessed here. The second dataset records information and pricing for vehicles and can be accessed here. apple_quality <- read.csv("C:/Users/Shoshana/Documents/CUNY SPS/cuny-sps/DATA_622/data/apple_qua...
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DATA 624 - Homework 5
Exercise 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 \(\alpha\) and \(\ell_0\), and generate forecasts for the next four months. # filter for Victorian pigs victorian_pig...
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