The code is an example of how to use different regression algorithms (KNN, SVR, MLP) to predict car prices based on certain features such as engine size, horsepower, and number of cylinders. It uses GridSearchCV to tune the hyperparameters of the models and evaluates the performance of the models using mean squared error as a metric.
The data is loaded from a csv file using Pandas and then preprocessed. Categorical variables are encoded using one-hot encoding and some variables are transformed to numerical variables using mapping. The data is then split into train and test sets.
The KNN model is used to make predictions and tune its hyperparameters using GridSearchCV. The SVR model is also used and its hyperparameters are tuned using GridSearchCV. Finally, a Multi-layer Perceptron (MLP) model is used and its hyperparameters are also tuned using GridSearchCV.
The performance of each model is evaluated using mean squared error on the test set. Overall, the code provides a good example of how to use different regression algorithms and tune their hyperparameters for predicting car prices.