LINEAR REGRESSION-PYTHON

Linear regression is one of the supervised Machine learning algorithms in Python that observes continuous features and predicts an outcome. Depending on whether it runs on a single variable or on many features, we can call it simple linear regression or multiple linear regression.

This is one of the most popular Python ML algorithms and often under-appreciated. It assigns optimal weights to variables to create a line ax+b to predict the output. We often use linear regression to estimate real values like a number of calls and costs of houses based on continuous variables. The regression line is the best line that fits Y=a*X+b to denote a relationship between independent and dependent variables.

Let’s plot this for the diabetes dataset.

>>> import matplotlib.pyplot as plt

>>> import numpy as np

>>> from sklearn import datasets,linear_model

>>> from sklearn.metrics import mean_squared_error,r2_score

>>> diabetes=datasets.load_diabetes()

>>> diabetes_X=diabetes.data[:,np.newaxis,2]

>>> diabetes_X_train=diabetes_X[:-30] #splitting data into training and test sets

>>> diabetes_X_test=diabetes_X[-30:]

>>> diabetes_y_train=diabetes.target[:-30] #splitting targets into training and test sets

>>> diabetes_y_test=diabetes.target[-30:]

>>> regr=linear_model.LinearRegression() #Linear regression object

>>> regr.fit(diabetes_X_train,diabetes_y_train) #Use training sets to train the model

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

>>> diabetes_y_pred=regr.predict(diabetes_X_test) #Make predictions

>>> regr.coef_

array([941.43097333])

>>> mean_squared_error(diabetes_y_test,diabetes_y_pred)

3035.0601152912695

>>> r2_score(diabetes_y_test,diabetes_y_pred) #Variance score

0.410920728135835

>>> plt.scatter(diabetes_X_test,diabetes_y_test,color =’lavender’)

<matplotlib.collections.PathCollection object at 0x0584FF70>

>>> plt.plot(diabetes_X_test,diabetes_y_pred,color=’pink’,linewidth=3)

[<matplotlib.lines.Line2D object at 0x0584FF30>]

>>> plt.xticks(())

([], <a list of 0 Text xticklabel objects>)

>>> plt.yticks(())

([], <a list of 0 Text yticklabel objects>)

>>> plt.show()