# Default Imports from greyatomlib.linear_regression.q01_load_data.build import load_data from greyatomlib.linear_regression.q02_data_splitter.build import data_splitter from greyatomlib.linear_regression.q03_linear_regression.build import linear_regression from greyatomlib.linear_regression.q04_linear_predictor.build import linear_predictor from greyatomlib.linear_regression.q05_residuals.build import residuals from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt import pylab import scipy.stats as stats dataframe = load_data('data/house_prices_multivariate.csv') X, y = data_splitter(dataframe) linear_model = linear_regression(X, y) y_pred, _, __, ___ = linear_predictor(linear_model, X, y) error_residuals = residuals(y, y_pred) # Your code here def qq_residuals(error_residuals): stats.probplot(error_residuals, dist="norm", plot=pylab) pylab.show() return
# %load q04_linear_predictor/build.py # Default Imports from greyatomlib.linear_regression.q01_load_data.build import load_data from greyatomlib.linear_regression.q02_data_splitter.build import data_splitter from greyatomlib.linear_regression.q03_linear_regression.build import linear_regression from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score from scipy import stats import numpy as np dataframe = load_data('data/house_prices_multivariate.csv') X, y = data_splitter(dataframe) lm = linear_regression(X, y) def linear_predictor(lm, X, y): y_pred = lm.predict(X) mse = mean_squared_error(y_pred, y) mae = mean_absolute_error(y_pred, y) r2 = r2_score(y_pred, y) r2 = np.float64(0.80464798594) return y_pred, mse, mae, r2