If you’re trying your first machine learning algorithm, there are some formulas that will be useful to you (that might be overwhelming to learn at first). All machine learning algorithms are governed by a set of conditions, and your job is it make sure your algorithm fits the assumptions to ensure superior performance. There are different algorithms for different conditions. For example, don’t even try to use linear regression on a categorical dependent variable or you will be disappointed with low values of R² and F statistics. Instead, use algorithms like Logistic Regression, Decision Trees, SVM, and Random Forest. Here is a good reading to get a better sense of these algorithms: Essentials of Machine Learning Algorithms.
For beginners, I also highly recommend this website that talks through some of the programs that can be done in R:
The first step in developing a data warehouse to support a business intelligence program is to develop a plan. In that plan, it should include some forward-thinking regarding what questions various users from the Board of Directors to frontline staff may ask to ensure that in the design that the functionality will be possible to meet the business objectives of the organization, as well to properly manage expectations at the beginning.
The following provides some general guidance as it relates to building a data warehouse and the foundational aspects of business intelligence programs:
Determine business objectives
Collect and analyze information
Identify core business processes
Construct a conceptual data model
Locate data sources and plan data transformations
Set tracking duration
Implement the plan
Having a thoughtful plan can help ensure that the project is appropriately resourced, saves anticipated costs, as well as ensure that the value that is perceived to be added by the business intelligence program is realized.