Linear Regression Using Python scikit-learn
Let's say you have some people's height and weight data. Can you use it to predict other people's weight? Find out using Python scikit-learn.
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In this article, I am going to explain how to use scikit-learn/sk-learn, a machine learning package in Python, to do linear regression for a set of data points.
Below is a video tutorial on this:
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.
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,"->",row)
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
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:
Slope and intercept:
m=reg.coef_ b=reg.intercept_ print("slope=",m, "intercept=",b)
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!
Published at DZone with permission of Vinay Kumar. See the original article here.
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