Publications by Khyati Naik
hw9_data624_fall2024
library(AppliedPredictiveModeling) ## Warning: package 'AppliedPredictiveModeling' was built under R version 4.3.3 library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.3 ## Warning: package 'tidyr' was built under R version 4.3.2 ## Warning: package 'readr' was built under R version 4.3.2 ## Warning: package 'dplyr' was bu...
6479 sym Python (11249 sym/72 pcs) 5 img
hw8_data624_fall2024
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, 1] (there are also 5 other non-informative variab...
2988 sym Python (20973 sym/64 pcs) 11 img
hw7_data624_fall2024
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: The matrix fingerprints contains the 1,107 binary mo...
5945 sym Python (16228 sym/39 pcs) 5 img
p1_data624_fall2024
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 hundreds of dollars, other than that it is straight forward. I am being somewhat ambiguous on purpose to make this have a little...
4292 sym Python (20507 sym/65 pcs) 16 img
hw6_data624_fall2024
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 primary difference among these figures lies in the number of random numbers each graph represents. As we move from the ACF for 36 random numbe...
6549 sym Python (22102 sym/110 pcs) 36 img
hw5_data624_fall2024
library(fpp3) ## Warning: package 'fpp3' was built under R version 4.3.3 ## ── Attaching packages ──────────────────────────────────────────── fpp3 1.0.0 ── ## ✔ tibble 3.2.1 ✔ tsibble 1.1.3 ## ✔ dplyr 1.1.4 ✔ tsibbledata ...
3380 sym Python (17479 sym/76 pcs) 15 img
hw4_data624_fall2024
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. The data can be accessed via: library...
6675 sym Python (28411 sym/111 pcs) 5 img
hw3_data624_fall2024
library(fpp3) ## ── Attaching packages ──────────────────────────────────────────── fpp3 1.0.0 ── ## ✔ tibble 3.2.1 ✔ tsibble 1.1.3 ## ✔ dplyr 1.1.4 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.2 ...
8937 sym Python (13620 sym/62 pcs) 18 img
hw2_data624_fall2024
library(fpp3) ## ── Attaching packages ──────────────────────────────────────────── fpp3 1.0.0 ── ## ✔ tibble 3.2.1 ✔ tsibble 1.1.3 ## ✔ dplyr 1.1.4 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.2 ...
5860 sym Python (12003 sym/49 pcs) 27 img
hw1_data624_fall2024
library(fpp3) ## ── Attaching packages ──────────────────────────────────────────── fpp3 1.0.0 ── ## ✔ tibble 3.2.1 ✔ tsibble 1.1.3 ## ✔ dplyr 1.1.4 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.2 ...
5048 sym Python (8688 sym/63 pcs) 27 img