Data Glossary Lab

A categorical variable is a variable that can have one of a limited number of possible values (categories) without any intrinsic ordering involved.

A chatbot is a conversational AI interface, not just an automated answering machine. While traditional web forms and FAQs require users to manually hunt for information, a chatbot allows them to simply ask for it.

Classification is a supervised learning problem when it is necessary to predict categorical outcomes based on input features. Examples of classification problems are fraud detection and email spam filters. Commonly used classification algorithms are k-nearest neighbors, decision trees, random forest, etc.

Cloud Computing is the on-demand delivery of computing power, database storage, applications, and other IT resources via the internet with pay as you go pricing.

Clustering is an unsupervised learning problem concerned with grouping all the observations of a dataset according to their similarity by some common characteristics. Common clustering algorithms are k-means, hierarchical clustering, spectral clustering, etc.

Cohort Analysis is a specialized subset of behavioral analytics that breaks down a large dataset into related groups, or "cohorts," based on shared characteristics or experiences within a specific time span. Instead of viewing all users as a single unit, this analysis tracks how specific segments behave over time, allowing businesses to identify patterns in engagement, retention, and churn.

Computer Science (CS) is a multifaceted field of study focused on the theoretical and practical aspects of processing information in digital computers, the design of computer hardware and software, and the diverse applications of computing technology.

Computer vision is an area of computer science concerned with enabling computers to achieve high-level understanding from digital images or videos.

A confusion matrix is a table illustrating the predictive performance of a classification model, showing true positives, true negatives, false positives, and false negatives.