Publications by Ahmed Elsaeyed
Assignment_5
What Drives Home Values in Ames, Iowa? Ahmed Elsaeyed Building Our Predictive Model To identify the key drivers of home values, we built a multiple linear regression model using the most influential features. Metric Value R-squared 80.8% Adjusted R-squared 80.7% Mean Absolute Error $21,655.09 Root Mean Squared Error $34,819.79 Our model ex...
1487 sym 11 img 1 tbl
Assignment_3
Gun Laws vs. Firearm Mortality Firearm Mortality Across America Suicides Per State Western states have higher suicide rates Homicides Per State Southern states have higher homicide rates Gun Ownership Varies Dramatically by State States Approach Gun Regulation Differently Key Relationships Between Variables States with Fewer Gun Laws Have D...
876 sym 12 img
Assignment_4
Breaking Down Profitability by Median Revenue Median revenue from NYC AirBnB’s is $57.90 per night. Revenue skews low with many low revenue listings. Comparing Profitability to Price Per Night Price per night seems to be somewhat important. Looking at Average Revenue by Neighborhood There are a few hot spots where average revenue is very hig...
1651 sym 8 img
Document
The Big Picture: Despite large variations in per-capita funding, the data suggests that smaller-population states of both parties benefit most, indicating structural rather than overt partisan bias in the funding allocations. The Storyboard A first surface level look at how much funding in total went to Democratic-won vs GOP-won attes. Democrat-...
2988 sym 4 img
Beverages_PCR_PLS
Data Here we import our train and test data, student_train and student_eval, and evaluate for missing data and additional exploratory steps. Data Acquisition Here we can preview the data structure: student_train = read.csv('https://raw.githubusercontent.com/deepasharma06/Data-624/refs/heads/main/StudentData_training.csv') student_eval = read.csv(...
3123 sym R (25510 sym/76 pcs) 9 img 1 tbl
Document
# Load the dataset groceries <- read.transactions("/Users/aelsaeyed/Downloads/GroceryDataSet.csv", format = "basket", sep = ",") # Summary of the data summary(groceries) ## transactions as itemMatrix in sparse format with ## 9835 rows (elements/itemsets/transactions) and ## 169 columns (items) and a density of 0.02609146 ## ## most frequent it...
2457 sym 2 img
Document
8.1 Recreate the simulated data from Exercise 7.2: a) Fit a random forest model to all of the predictors, then estimate the variable importance scores. Did the random forest model significantly use the uninformative predictors (V6 – V10)? set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simul...
3569 sym R (10579 sym/60 pcs) 4 img
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 ...
3420 sym R (19421 sym/54 pcs) 6 img
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...
4255 sym 23 img
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. ...
3112 sym R (16446 sym/89 pcs) 42 img