from sklearn.linear_model import LinearRegression # First of all we load dataset and normalize it using DataLoader from Plotter import Plotter data_loader = DataLoader() data_loader.load_and_normalize_data() train_data = data_loader.get_train_data()[1:] test_data = data_loader.get_train_data()[:1] # Then we use DataExplorer to explore data. Info allows us to see how many null values exists. We also c data_explorer = DataExplorer() data_to_visualize = train_data[['WoodDeckSF', 'SalePrice']] data_explorer.describe(train_data) data_explorer.info(train_data) # And we use Plotter to create some data visualization plotter = Plotter() # plotter.plot(data_to_visualize, 'WoodDeckSF') # plotter.show_histogram(data_to_visualize) # plotter.show_box_plot(data_to_visualize) # Create model model = LinearRegression().fit(train_data, train_data['SalePrice']) r_sq = model.score(train_data, train_data['SalePrice']) model.predict(train_data[:1])
