The Julia Package Ecosystem

Códigos para correr analisis de grafos exploratorios en R usando EGAnet

This tutorial provides a comprehensive introduction to hierarchical clustering in Julia. Experiment with different distance metrics and linkage methods to see how they affect the results. The dendrogram is an invaluable tool for understanding the hierarchical structure of your data. Remember to choose the method that best suits your data and the goals of your analysis.

It is analysis of a business task.

reference notes

Association in the movie dataset is examined

This example provides a much more complete and practical demonstration of how to create and visualize Kaplan-Meier survival curves in Julia using the `SurvivalAnalysis` package.

This analysis applies K-means clustering and Principal Component Analysis (PCA) to retail transaction data to uncover purchasing patterns. After data cleaning and transformation, clusters of frequently purchased items were identified to optimize store layout, marketing strategies, and inventory management. The analysis highlights the journey from initial clustering attempts using the Elbow Method to refining results with the Silhouette Method, ultimately finding that two clusters provided the best segmentation. Visualizations and insights derived from the clustering process offer actionable strategies for retail optimization.