K-Nearest Neighbors, Decision Trees, and Random Forests

Are there other machine learning algorithms, besides LinearRegression(), that is suitable for the Boston Housing dataset? Absolutely. There are many regressors in the scikit-learn library that may be used. Regressors are generally considered a class of machine learning algorithms that are suitable for continuous target values. In addition to Linear Regression, Ridge, and Lasso, we can try K-Nearest Neighbors, Decision Trees, and Random Forests. These models perform well on a wide range of datasets. Let's try them out and analyze them individually.

K-Nearest Neighbors

The idea behind K-Nearest Neighbors (KNN) is straightforward. When choosing the output of a row with an unknown label, the prediction is the same as the output of its k-nearest neighbors, where k may be any whole number.

For instance, let's say that k=3. Given an unknown label, we take n columns for this row and place them in n-dimensional space...