You are given a dataset (named real_state) with different house properties (dataset available in Learning Central). Your goal is to train machine learning models in the training set to predict the house price of a unit area in the test set. The problem should be framed as both regression and classification. For regression, the house price of a unit area is given; for classification, there would be two labels (expensive and not-expensive) depending on the house price of a unit area: expensive if it is higher or equal to 30, and not-expensive if it is lower than 30.

The task is therefore to train two machine learning models (one regression and another one classification) and check their performance. The student can choose the models to solve this problem. Write, for each of the models, the main Python instructions to train and predict the labels (one line each, no need to include any data preprocessing instructions in the pdf) and the performance in the test set in terms of Root Mean Squared Error (regression) and accuracy (classification). While you will need to write the full code to get to the results, only these instructions are required in the pdf

Real state dataset

The dataset is related to the price of houses in a certain area. The dataset includes several features (6 features from X1 to X6) and the house price per unit area in the last column. The first column for each instance is an identifier for each instance. The datasets can be viewed as regression task or a binary classification task (expensive>=30; not-expensive<30). Training and test sets are provided.

The test set : as test_full_Real-estate.csv

https://drive.google.com/file/d/18wOErCEhEpiohIF3FcTZm-PiXs2fZRIR/view?usp=share_link

The trained set: as train_full_Real-estate.csv in the following link

https://drive.google.com/file/d/1Rljfuqgg9RXxlNSrvs4Pocfn6A5YMfo1/view?usp=share_link