After laying the foundation in Introduction to matplotlib and Introduction to matplotlib – Part 2 I want to show you another important chart Bar Charts A bar chart is useful to show total values over time e.g. the revenue of a company. years = (2017, 2018, 2019) revenue = (5000, 7000, 9000) plt.bar(years, revenue, width=0.35)…
# import and instantiate model from sklearn.linear_model import LinearRegression model = LinearRegression() #prepare test data features_train = df_train.loc[:, ‘feature_name’] target_train = df_train.loc[:, ‘target_name’] #fit (train) model and print coefficient and intercept model.fit(features_train , target_train ) print(model.coef_) print(model.intercept_) # calculate model quality from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score target_prediction = model.predict(features_train) print(mean_squared_error(target_train , target_prediction))…
At PyConDE Berlin 2019 Florian Bruhin gave a really nice session about testing with pytest, which I try to recap here. Writing Tests If You want to work with pytest you can install it via: pip install pytest When you know basic python unittest fixtures, good news ahead: pytest is compatible and will run your…