Defining ML problems

After we have identified the business requirements, we need to define the problem by identifying the features and target of the problem. For Example 1, the house price is the target, and features are the house attributes that affect the house price, such as the location, the house size (total square footage), the age of the house, the number of bedrooms and bathrooms of the house, and so on. Table 3.1 shows a small sample dataset:

Table 3.1 – Example 1 dataset

The problem is then defined as building a model among the features and the target and discovering their relationships. During the problem definition process, we will understand the problem better, decide whether ML is the best solution for the problem, and to what category the problem belongs.

Is ML the best solution?

When facing a problem, the first thing we need to do is choose the best modeling/solution for the problem. For example, given the initial position and speed...