(Originally from https://pair-code.github.io/understanding-umap/) - Dimensionality reduction is a powerful tool for machine learning practitioners to visualize and understand large, high dimensional datasets.

Relational database: RDBMS stores data in the form of a collection of tables and relations can be defined between the common fields of these tables. Most modern database management systems like MySQL, Microsoft SQL Server, Oracle, IBM DB2 and Amazon Redshift are based on RDBMS.

Arrow functions: Arrows are a function shorthand using the => syntax. Defining variables: let -- cannot be redeclared, declared before use, block scope. const -- cannot be redeclared, updated, block scoped. properties of const object can be updated. Spread operator: three dots (…) to obtain the list of parameters. Template literals: To create multiline strings and perform string interpolation.

R-squared: R-squared is a statistical measure of how close the data are to the fitted regression line. It is the percentage of the response variable variation that is explained by a linear model. R-Squared is a statistical measure of fit that indicates how much variation of a dependent variable is explained by the independent variable(s) in a regression model.

Definition: high correlation between the exploratory variables in regression analysis. Perfect Multicollinearity: If two or more independent variables have an exact linear relationship between them then we have perfect multicollinearity. (e.g weight in pounds or kilograms)

The basic idea of solving this problem is to define a rule to separate the value before and after changing.