Encoding categorical data is a crucial step in data preprocessing. By converting categorical data into a numeric format, machine learning models can interpret and work more effectively.

In ML project stage 2, preprocess categorical data: Encode into numerical format (e.g., one-hot, ordinal) based on data nature and model type (regression, classification).

Using categorical data comes with another challenge: high cardinality. Cardinality refers to the number of possible values for a particular category. For example, the cardinality of a list of all ...

Numerical data involves values that are measurable or countable. Numerical data can be further defined as discrete data (can be counted) or continuous data (can be measured). For each example listed ...

Clustering Methods: Squeezer for categorical data K-Prototypes for mixed data Numerical clustering with various algorithms (KMeans, DBSCAN, Gaussian Mixture, etc.) Evaluation Metrics: Category Utility ...

Most previous clustering algorithms focus on numerical data whose inherent geometric properties can be exploited naturally to define distance functions between data points. However, much of the data ...

One-hot encoded data alone: Jaccard Distance is typically the better and more common choice. Overlap Coefficient can be used but is less common and often less effective. One-hot encoded data mixed ...

Discovering the potential group structure of objects is of crucial importance to data mining. Most of the existing clustering approaches are applicable only to purely numerical or categorical data, ...