Publications by Ahmed Elsaeyed
Beverages_PCR_PLS
Data Here we import our train and test data, student_train and student_eval, and evaluate for missing data and additional exploratory steps. Data Acquisition Here we can preview the data structure: student_train = read.csv('https://raw.githubusercontent.com/deepasharma06/Data-624/refs/heads/main/StudentData_training.csv') student_eval = read.csv(...
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Document
# Load the dataset groceries <- read.transactions("/Users/aelsaeyed/Downloads/GroceryDataSet.csv", format = "basket", sep = ",") # Summary of the data summary(groceries) ## transactions as itemMatrix in sparse format with ## 9835 rows (elements/itemsets/transactions) and ## 169 columns (items) and a density of 0.02609146 ## ## most frequent it...
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Document
8.1 Recreate the simulated data from Exercise 7.2: a) Fit a random forest model to all of the predictors, then estimate the variable importance scores. Did the random forest model significantly use the uninformative predictors (V6 – V10)? set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simul...
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624-HW7
6.2 Taking a look at the data: data(permeability) summary(permeability) ## permeability ## Min. : 0.06 ## 1st Qu.: 1.55 ## Median : 4.91 ## Mean :12.24 ## 3rd Qu.:15.47 ## Max. :55.60 head(permeability) ## permeability ## 1 12.520 ## 2 1.120 ## 3 19.405 ## 4 1.730 ## 5 1.680 ## 6 ...
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624 Project 1
Part 1: ATM Data and Forecasting Taking a look at the data: atm_data <- read_excel("/Users/aelsaeyed/Downloads/ATM624Data.xlsx") head(atm_data) ## # A tibble: 6 × 3 ## DATE ATM Cash ## <dbl> <chr> <dbl> ## 1 39934 ATM1 96 ## 2 39934 ATM2 107 ## 3 39935 ATM1 82 ## 4 39935 ATM2 89 ## 5 39936 ATM1 85 ## 6 39936 ATM2 90...
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624-HW6
Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Explain the differences among these figures. Do they all indicate that the data are white noise? Left: ACF for a white noise series of 36 numbers. Middle: ACF for a white noise series of 360 numbers. Right: ACF for a white noise series of 1,000 numbers. ...
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624-HW5
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 ℓ_0, and generate forecasts for the next four months. b. Compute a 95% prediction interval for the first forecast usi...
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624-HW4
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 eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. (a) Using visualizations, explore the pr...
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Ahmed_Elsaeyed_624_HW1
Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec. Use ? (or help()) to find out about the data in each series. What is the time interval of each series? Use autoplot() to produce a time plot of each series. For the last plot, modify the axis labels and title. help(aus_p...
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AElsaeyed_FinalAssignment
Probability I listed out the quantitative variables and then checked their skew. I ordered the skew by most skew to least. The most skewed variable is MiscVal, but I chose to use LotArea because its more interesting and relevant. The dependent variable is SalePrice. ## MiscVal PoolArea LotArea LowQualFinSF BsmtFinSF2 ## 24.42652...
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