In this example we will build a predictive model to predict house price (price is a number from some defined range, so it will be regression task). For example, you want to sell a house and you don’t know the price which you can take — it can’t be too low or too high. To find house price you usually try to find similar properties in your neighborhood and based on gathered data you will try to assess your house price. We will do something similar, but with Machine Learning methods! OK, let’s start!

We will use Boston Housing dataset, which you can download from here. However, I made one trick on original dataset for you, which help you understand ML better, I splitted it into train and test samples — you can get is from my github. We will use train samples (data_train.csv file) for model learning and test samples (data_test.csv) for predictions. I divided data into two sets to show you how you can use trained model — for predicting the unknown. If you are interested in the meaning of each column in data, you can check it here. For model training we will use MLJAR, because it has easy web interface (if you don’t have an account, please signup and get free credits for start). Let’s start a new project. Make sure that you select Regression as a task. It is important, because different algorithms are used for regression and different for classification.