Descriptive Analytics Recipes

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About this project

Descriptive Analytics Recipes provides essential methods for analyzing historical business data. This repository focuses on uncovering insights through graph-based techniques, enabling a deeper understanding of past performance. Perfect for those seeking to enhance decision-making and avoid repeating historical mistakes in their analytics journey.

The Descriptive Analytics Recipes repository provides a comprehensive guide to understanding and implementing descriptive analytics through graph-based methodologies. Descriptive analytics involves examining historical business data to answer the question, what happened? This fundamental analysis is essential for informing future strategies and preventing the repetition of past mistakes. Although often undervalued in the field of business analytics, it plays a crucial role in shaping data-driven decision-making.

Understanding Descriptive Analytics

Descriptive analytics focuses on historical data relevant to specific business contexts. Whether it pertains to consumer behavior in an online store or loan servicing patterns from a banking institution, an in-depth understanding of these events forms the foundation for more advanced analytics techniques such as predictive and prescriptive analytics. The repository discusses workflows essential for operational data preparation, helping to lay the groundwork for effective analysis.

Graph-Based Methods

Graphs are a powerful analytical tool within descriptive analytics, especially when dealing with relational data. This repository emphasizes the conversion of relational data into graph structures for enhanced analytical capabilities. A detailed document on graph mapping is available, illustrating how relational data can be transformed into graphs for insightful analysis.

Use Cases Covered

The focus of this repository lies on operational and analytical datasets that are inherently relational. It is important to note that Large Language Models are not within the scope of this work. Instead, developed solutions leverage traditional machine learning techniques that have historically supported knowledge discovery in databases.

Case Studies

For practical application and inspiration, refer to the case studies available in the examples directory. These offerings serve as a resource to understand specific implementations of descriptive analytics.

Contributions

Contributions are encouraged. Whether by opening issues, submitting pull requests, or proposing new recipes, the development of this repository thrives on community engagement. Should questions arise, please feel free to open an issue or reach out to the repository maintainers.