The Data Science Method (DSM) — Modeling

This is the fifth article in a series about how to take your data science projects to the next level by using a methodological approach similar to the scientific method coined the Data Science Method. This article is focused on the Modeling step, which includes: fitting the model, reviewing the model performance, and identifying the final model. If you missed the previous article(s) in this series, you can go to the beginning here, or click on each step title below to read a specific step in the process.

The Data Science Method

1. Problem Identification
2. Data Collection, Organization, and Definitions
3. Exploratory Data Analysis
4. Pre-processing and Training Data Development
5. Modeling
6. Documentation

Photo by Deleece Cook on Unsplash

Early practitioners and those less familiar with data science often think data scientists spend their entire day training machine learning models and tuning those models. However, we know that effective data science is the process of converting business problems into thoughtfully designed data problems where thorough problem identification work and data understanding is achieved before any model development work takes place. Modeling is the step that allows leveraging your data to make predictive insights and…