# Spiral Model Technique For Data Science & Machine Learning Lifecycle

arXiv: https://arxiv.org/abs/2510.06987 · HTML: https://mlsanity.com/paper/2510.06987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Employee turnover prediction and retention policies design: a case study](https://mlsanity.com/paper/1707.01377/) (2017)

## Abstract (arXiv)

Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is mostly depicted that it is possible in a traditional data science life cycle to start the process again after reaching the end of cycle. This paper suggests a new technique to incorporate data science life cycle to business problems that have a clear end goal. A new technique called spiral technique is introduced to emphasize versatility, agility and iterative approach to business processes.