Modeling: Naive Bayes in R

Based on: https://www.datascience-pm.com/crisp-dm-2/

Data Mining process steps includes Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, Deployment.

Business Understanding step – we need to understand the business and establish the question we need to answer for the data mining

Data Understanding step – we need to understand the data. We can use statistics such as descriptive, regression analysis to understand the data.

Data Preparation step – it is the cleaning of the data and we can remove duplicates here.

Modeling step – we create clustering models, prediction models, classification models.

Evaluation step – we evaluate which models is more accurate and select.

Deployment steps – we can create data products.

For Data Science, at the Deployment steps, we create data products for businesses. We can create softwares that predicts something. In the table, the goal is to build data products for a business.

For Data Mining, at the Deployment steps, we create reports or PowerPoints slides on our results. In the…