I am not going to explain training data, testing data, and model evaluation concepts here, but they are important.

We know that the equation of a line is given by y=mx+b, where m is the slope and b is the intercept.

Our goal is to find the best values of slope (m) and intercept (b) to fit our data.

Linear regression uses the ordinary least squares method to fit our data points.

import statement:

from sklearn import linear_model

I have the height and weight data of some people. Let's use this data to do linear regression and try to predict the weight of other people.

height=[[4.0],[4.5],[5.0],[5.2],[5.4],[5.8],[6.1],[6.2],[6.4],[6.8]]
weight=[  42 ,  44 , 49, 55  , 53  , 58   , 60  , 64  ,  66 ,  69]

print("height weight")
for row in zip(height, weight):
    print(row[0][0],"->",row[1])

Output:

height weight
4.0 -> 42
4.5 -> 44
5.0 -> 49
5.2 -> 55
5.4 -> 53
5.8 -> 58
6.1 -> 60
6.2 -> 64
6.4 -> 66
6.8 -> 69

import statement to plot graph using matplotlib:

import matplotlib.pyplot as plt

Plotting the height and weight data:

plt.scatter(height,weight,color='black')
plt.xlabel("height")
plt.ylabel("weight")

Declaring the linear regression function and call the fit method to learn from data:

reg=linear_model.LinearRegression()
reg.fit(height,weight)

Slope and intercept:

m=reg.coef_[0]
b=reg.intercept_
print("slope=",m, "intercept=",b)

Output:

slope= 10.1936218679 intercept= -0.4726651480

plt.scatter(height,weight,color='black')
predicted_values = [reg.coef_ * i + reg.intercept_ for i in height]
plt.plot(height, predicted_values, 'b')
plt.xlabel("height")
plt.ylabel("weight")

And that's it!

Linear regression in python scikit learn | Quick KT