Exploring and Pre-Processing Data

These flashcards cover key definitions and concepts from the Business Data Mining course, focusing on variable types, statistical measures, and data pre-processing techniques.

CRISP-DM

A data mining process model that includes phases like business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Variable Types

Categories of variables including categorical, ordered, intervals, and true numeric values, which determine how data can be analyzed.

Categorical Variable

A variable that represents types or groups and is not ordered, such as colors or names.

Continuous Variable

A numerical variable that supports all mathematical operations, such as weight, height, or length.

Boxplots

Visual representations used to summarize the distribution of a dataset and identify outliers.

Normalization

The process of adjusting values measured on different scales to a notionally common scale, often by subtracting the minimum value and dividing by the range.

Mean

The average of a set of values, calculated by summing the values and dividing by the count of values.

Median

The middle value in a dataset when the values are sorted in order.

Standard Deviation

A measure of the amount of variation or dispersion of a set of values.

Outlier

An observation that significantly differs from other observations in a dataset, which can skew results if not handled properly.