predicting house pricesa regression example the two previous examples were considered classification problemswhere the goal was to predict single discrete label of an input data point another common type of machine-learning problem is regressionwhich consists of predicting continuous value instead of discrete labelfor instancepredicting the temperature tomorrowgiven meteorological dataor predicting the time that software project will take to completegiven its specifications note don' confuse regression and the algorithm logistic regression confusinglylogistic regression isn' regression algorithm--it' classification algorithm the boston housing price dataset you'll attempt to predict the median price of homes in given boston suburb in the mid- sgiven data points about the suburb at the timesuch as the crime ratethe local property tax rateand so on the dataset you'll use has an interesting difference from the two previous examples it has relatively few data pointsonly split between training samples and test samples and each feature in the input data (for examplethe crime ratehas different scale for instancesome values are proportionswhich take values between and others take values between and others between and and so on listing loading he bost on housing dat aset from keras datasets import boston_housing (train_datatrain_targets)(test_datatest_targetsboston_housing load_data(let' look at the datatrain_data shape ( test_data shape ( as you can seeyou have training samples and test sampleseach with numerical featuressuch as per capita crime rateaverage number of rooms per dwellingaccessibility to highwaysand so on the targets are the median values of owner-occupied homesin thousands of dollarstrain_targets the prices are typically between $ , and $ , if that sounds cheapremember that this was the mid- sand these prices aren' adjusted for inflation licensed to