Data Science GPT: K-Means Clustering-K-means data clustering tool

Example

For a dataset containing user data such as age, location, purchase history, and session activity, this function would identify which variables are most relevant for clustering to target user behaviors effectively.

Scenario

A retail company wants to understand customer purchase patterns to develop personalized marketing campaigns. The GPT recommends focusing on age, purchase frequency, and average spend per visit as the most relevant features for clustering.

Example

Using inertia or within-cluster sum of squares, this function determines the optimal number of clusters needed for effective segmentation, usually visualized through an elbow plot.

Scenario

An online education provider uses the elbow method to decide the number of student segments for customized course recommendations. The analysis helps to minimize variability within clusters and maximize variability between clusters.

Example

Once the number of clusters is set, the GPT applies the K-means algorithm to segment the data accordingly and describes each cluster's characteristics.

Scenario

A health tech company clusters patient data into distinct groups based on health metrics and treatment history to optimize resource allocation and patient care strategies.