Machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision.

The data models that are typical of traditional data analytics are often static and of limited use in addressing fast-changing and unstructured data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.

via GIPHY

While traditional data analysis would need a model built on past data and expert opinion to establish a relationship between the variables, machine learning starts with the outcome variables (e.g. saving energy) and then automatically looks for predictor variables and their interactions

In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm the goal(s) and then it “learns” from the data which factors are important in achieving that goal.

Content Credits:- Rusha Bhattacharya