Data Science From Scratch To Production MVP Style: Model
Modeling Now this section is of least important so we’re going to be incredibly sloppy here. We’ll perform a simple train test split and create a simple Linear Regression model. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) model = LinearRegression().fit(X_train, y_train) model.score(X_test, y_test) 0.498875410976377 model.coef_ array([635.1964214]) model.intercept_ 5810.549007860056 pyplot.scatter(X_test, y_test, color = 'red') pyplot.plot(X_train, model.predict(X_train), color = 'blue') pyplot.title('temperature_fahrenheight vs ice_cream_sales_usd (Test set)') pyplot.xlabel('temperature_fahrenheight') pyplot.ylabel('ice_cream_sales_usd') pyplot.show() While our model isn’t great, lets pretend we’re satisfied with it and move on to preparing to wrap our model in an API and getting it into production....