Let's build the final model using a random forest regressor. A random forest is a universal machine learning technique, that is, it can handle different kinds of data; it could be a category (classification), a continuous variable (regression), or features of any kind, such an image, price, time, post codes, and so on (that is, both structured and unstructured data). It doesn't generally overfit too much, and it is very easy to stop it from overfitting. For these reasons, random forest is a versatile ML technique which we can effectively use to solve our problem.
Building the model
Validation data and error metrics
Our initial step is choosing a suitable size for validation data. Before delineating the validation...
