Simple linear regression model with scikit-learn

Simple Leaner Regression Model is use to find the relation ship between two variable. It is commonly used in the predict analysis. Suppose we want to know price of pizza on the basis of size. We will train a model on the different size of pizza and its price. Then we will give the size of the pizza to train model it will predict its price. suppose we have different size of pizza x =  [[6], [8], [10], [14], [18]]] and its price y = [[7], [9], [13], [17.5], [18]]. Let's implement this problem it scikit-learn. Firs import Linear Regress from scikit-learn pakage.
from sklearn.linear_model import LinearRegression
Import Numpy module because when b give the data to model it will only accept if the data in Numpy array.
from sklearn.linear_model import LinearRegression
import numpy as np
Import matplot libraray which use to Draw a plot of our data
import matplotlib.pyplot as plt
x = [[6], [8], [10], [14], [18]]
x = np.reshape(x, (-1, 1))
Her we rehshape array to 2d because it accept 2d array otherwise it will show error.
y = [[7], [9], [13], [17.5], [18]]
y = np.reshape(y, (-1, 1))
model = LinearRegression()
model.fit(x, y)
train model using fit function. model.predict is use to get the output of the model
print('A 12" pizza shuld cost: $%.2f' % model.predict([12])[0])
plt.figure()
plt.title('Piza price ploted againt diameter')
plt.xlabel('Diameter in inch')
plt.ylabel('Pice in Dollars')
plt.plot(x, y, 'k.')
plt.axis([0, 25, 0, 25])
plt.grid(True)
plt.show()


Here complete code

import matplotlib.pyplot as plt

import numpy as np

from sklearn.linear_model import LinearRegression

x = [[6], [8], [10], [14], [18]]

x = np.reshape(x, (-1, 1))

y = [[7], [9], [13], [17.5], [18]]

y = np.reshape(y, (-1, 1))

model = LinearRegression()

model.fit(x, y)

print('A 12" pizza shuld cost: $%.2f' % model.predict([12])[0])

plt.figure()

plt.title('Piza price ploted againt diameter')

plt.xlabel('Diameter in inch')

plt.ylabel('Pice in Dollars')

plt.plot(x, y, 'k.')

plt.axis([0, 25, 0, 25])

plt.grid(True)

plt.show()