Foundations Toolkit and Datasets for Data Science
Average classification accuracy
Plot Confusion Matrix
Confusion Matrix
find.na
Visualizing the Optimal Number of k
k-Nearest Neighbour Classification
liver: Foundations Toolkit and Datasets for Data Science
Mean Absolute Error (MAE)
Min-Max scaling of numerical variables
Mean Squared Error (MSE)
One Hot Encoder
Partition the data
Confdidence interval for proportion
Feature scaling
Skewness
Skim a data frame to get useful summary statistics
Confdidence interval for mean
Confdidence interval for mean using z-distribution
Z-score scaling of numerical variables
Provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book <https://book-data-science-r.netlify.app>, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver," symbolizing deep and immersive engagement with knowledge.