Understanding regression datasets – loading, managing, and visualizing the House Sales dataset

The training process of machine learning (ML) models can be divided into three main sub-groups:

• Supervised learning (SL): The expected outputs are known for at least some data
• Unsupervised learning (UL): The expected outputs are not known but the data has some features that could help with understanding its internal distribution
• Reinforcement learning (RL): An agent explores the environment and makes decisions based on the inputs acquired from the environment

There is also an approach that falls in between the first two sub-groups called weakly SL, where there are not enough known outputs to follow an SL approach for one of the following reasons:

• The outputs are inaccurate
• Only some of the output features are known (incomplete)
• They are not exactly the expected outputs but are connected/related to the task we intend to achieve (inexact)

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