Linear regression isone of the most used among regression models and is most suitable for Continouos data. Just like for Boston data, the target being the house prices is continouos and these prices are infleunced by a number of factors considered as features here.
Before implementing machine learning models, data analysis is perofrmed on the data to best understand the relationship between these features and how they infleunce the target (house price). jSome of these features include:
- The relationship between Crime rate in that area and the price. As the crime rate increases, house prices reduce and less people even go for such houses because there is little or no security there.
- RM (Average number of rooms per dwelling) also greatly influences the house price. When the number of rooms per dwelling went too high, very few people were interested in such as the house price also went to high to balance the expenses made on such.
- Also age of the house and price are inversely proportional. Modern houses are very expensive unlike old design houses all worn out.
Real estate companies greatly need such information to be able to best meet to the needs of the majority of their clients. With the relationship between these features and the target (price), these companies will best know the type of houses to build and then put for sale else they will investing in the wrong places for the wrong groups of people.
THis therefore concludes houses will sell best in areas where there are low crime rates, lesser toxins polluting the environment, not too many number of rooms per dwelling and also modern houses.