Publications by Y. Devore
TIM-8501_Wk1: Evaluate a New Data Set in the Analytical Environment
knitr::opts_chunk$set(echo = TRUE) Load Packages This section of code illustrates how the packages needed for the analysis are loaded using the pacman’s p_load() function. This function will load the packages and install them if needed. Another way to load installed packages is from basic R using library(). ##Load packages from library pacman::p...
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TIM_8501_Week 6: Generate and Interpret Transformation and Scaling
Introduction The researcher will divide the data set into training and testing subsets in this analysis. The testing dataset is used to assess the model’s performance after it has been trained using the training dataset. The data set split is 20% for testing and 80% for training. The train-test ratio, or the Pareto principle, commonly known as th...
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TIM_8501_Week 5: Generate and Interpret Descriptive Statistics
Introduction Descriptive statistics provides an overview of a specific data collection, which may be a sample or a representative of the total population (Hayes, 2023). Measurements of central tendency and measurements of variability are the two categories into which descriptive statistics fall. Descriptive statistics provide brief descriptions of ...
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TIM_8501_Week 4: Generate and Interpret Multivariate Graphs of Data
Introduction Data visualizations that display the connections between three or more variables in a dataset are called multivariate graphs (Chip, 2023). Using these graphs, one can examine intricate data sets and spot linkages and patterns that are not immediately obvious from a straightforward scatterplot or line chart. Analyzing different componen...
3504 sym Python (25395 sym/21 pcs) 5 img
TIM-8501 Week 2: Assess, Filter, Clean, & Present Data
knitr::opts_chunk$set(echo = TRUE) Introduction Although missing data is a problem that frequently arises in data science, visualizing missing data has been mostly ignored. Numerous techniques are available to evaluate missing data and substitute estimated values; however, the ability of the techniques and substitutes to work depends on the missin...
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TIM-8501 Week 3: Conduct and Interpret Univariate Data Visualization & Outlier Analysis
knitr::opts_chunk$set(echo = TRUE) Load Packages ##Load packages from library pacman::p_load( tidyverse, Amelia, naniar, stargazer, psych, ggplot2, ggpubr) Load Data # Load dataset BANK<-read.csv("train.csv") # Use head() to display six lines of data set head(BANK) ## id forecasting.period net_profit_over_total_assets ## 1 0 ...
8261 sym Python (252940 sym/32 pcs) 11 img