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

Example

The GPT helps users identify which features in their dataset are most relevant for clustering. It can suggest removing irrelevant or redundant features, thus streamlining the data preparation process.

Scenario

A marketing team wants to cluster their customer database to understand purchasing patterns. The GPT analyzes the dataset and recommends using features such as purchase frequency, average transaction value, and product categories, while advising against including unrelated demographic data like zip codes if they don't contribute to clustering objectives.

Example

The GPT guides users through the elbow method to determine the ideal number of clusters for their data.

Scenario

A financial institution aims to classify its clients into risk categories based on transaction history and account usage. By using the elbow method, the GPT helps determine that four clusters offer a balance between underfitting and overfitting, representing different levels of financial risk.

Example

Once the number of clusters is determined, the GPT executes the K-Means algorithm to segment the data and provides detailed descriptions of each cluster.

Scenario

An e-commerce company uses the GPT to divide its product inventory into clusters based on sales performance metrics such as volume, growth rate, and return rate. The analysis results in clusters like 'Bestsellers,' 'Steady Performers,' and 'At Risk,' helping the company focus its marketing and inventory strategies.