Evaluating a model from the business perspective is critical to ensure that it is meets the goals and needs of the organization, provides a good return on investment (ROI), is resource efficient, mitigates risks, has a positive impact on the business, and is aligned with the overall strategy of the organization.

In this post, we look at the model building process from a high level and walk through some specific tasks and considerations that should be made throughout this step. After reading, you will have enough information to make a model development game plan for your project!

Data Preparation is a key to success because having a clean, robust, well-selected training set will Prevent unintended biases in your dataset from poorly selected training data, It will Simplify the modeling process through the inclusion of well-thought-out feature design, Aid in reproducibility through a well-documented data cleaning/preparation pipeline, and Save you time in the future since your data will be well-prepped for future iterations and analysis. It will Increase stakeholders' confidence in your analysis because you have meticulously understood and accounted for the gaps in your dataset.

Phase 2 of CRISP-DM: Data Understanding, is foundational to our success in achieving the business goals established in phase 1. We need to document, clarify, revisit and share our findings from the data understanding phase. The work we do here sets us up for being able to deliver a high-quality model, API, dashboard, report, or whatever is the expected deliverable.

Let's discuss the first phase of CRISP-DM: Business Understanding. Recall that CRISP-DM stands for the "CRoss Industry Standard Process for Data Mining" and it's a six-phase process for organizing and iterating through a data project. Feel free to check out my previous posts where we discuss Why CRISP-DM is a Data Scientist's Secret Weapon [...]

CRISP-DM stands for the CRoss Industry Standard Process for Data Mining. It is a standard process for knowledge discovery consisting of 6 phases that can be applied across a wide range of applications. The 6 phases are Business Understanding, Data Understanding, Data Preparation, Modeling, Model Evaluation, and Deployment. The model was designed to be a high-level framework that included a strategy for mapping the generic process model to the specialized level.