Implementing Simple Linear Regression with scikit-learn

To perform a simple linear regression with python 3, a solution is to use the module called scikit-learn. Example of implementation:

from sklearn import linear_model

import matplotlib.pyplot as plt
import numpy as np
import random

#----------------------------------------------------------------------------------------#
# Step 1: training data

X = [i for i in range(10)]
Y = [random.gauss(x,0.75) for x in X]

X = np.asarray(X)
Y = np.asarray(Y)

X = X[:,np.newaxis]
Y = Y[:,np.newaxis]

plt.scatter(X,Y)

#----------------------------------------------------------------------------------------#
# Step 2: define and train a model

model = linear_model.LinearRegression()
model.fit(X, Y)

print(model.coef_, model.intercept_)

#----------------------------------------------------------------------------------------#
# Step 3: prediction

x_new_min = 0.0
x_new_max = 10.0

X_NEW = np.linspace(x_new_min, x_new_max, 100)
X_NEW = X_NEW[:,np.newaxis]

Y_NEW = model.predict(X_NEW)

plt.plot(X_NEW, Y_NEW, color='coral', linewidth=3)

plt.grid()
plt.xlim(x_new_min,x_new_max)
plt.ylim(0,10)

plt.title("Simple Linear Regression using scikit-learn and python 3",fontsize=10)
plt.xlabel('x')
plt.ylabel('y')

plt.savefig("simple_linear_regression.png", bbox_inches='tight')
plt.show()

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Source: Benjamin H.G. Marchant, https://www.moonbooks.org/Articles/How-to-implement-a-simple-linear-regression-using-scikit-learn-and-python-3-/
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