Introduction to Linear Regression in Python   

In this short guide you can learn the basics of linear regression in Python. Linear regression is a model which represents the relation of variables - they can be independent or dependent. Linear regression can help to answer the question of dependency or numeric variables.

So let's explain it in a simple example. Suppose you are a data scientist which is asked:

Is there a relation between money and happiness?

To prove it you will need to do it in several steps:

• define the question (it's already done)
• find data
• analyse it
• summarize it

Step 1: Find and prepare data for Linear Regression

Once the question is defined we can search for data. Then you may need to do some preprocessing of the data like:

• clean - bad records, incomplete data
• format issues

Suppose we have data like:

import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# import and preview dataset
df = pd.read_csv("https://raw.githubusercontent.com/softhints/dataplotplus/master/data/happyscore_income.csv")

Data is taken from Kaggle and there's no need to clean or process it.

Step 2: Scatterplot and Linear Regression

In this step we are going to plot simple Scatterplot in Python. We need two numeric variables like:

# basic plot scatter
plt.xlabel('Happy Score')
plt.ylabel('Income')
plt.scatter('happyScore', 'avg_income', data=df, s=20, color='green')

result:

Next step is to perform the linear regression. We are going to search for the relation between happyScore and avg_income. We are using class: LinearRegression from sklearn.linear_model

X = df['happyScore'].values.reshape(-1, 1)
Y = df['avg_income'].values.reshape(-1, 1)

linear_regressor = LinearRegression()
linear_regressor.fit(X, Y)
Y_pred = linear_regressor.predict(X)

The result is array of predicted values like:

array([[ 1842.30538429],
       [  481.79795859],
       [11387.31647189],
       [14073.99675104],
       [14434.50975975],
       [ 5541.85554504],
       [ 3318.69199137],

Step 3: Plot Linear Regression in Python

Finally we are going to** plot the regression line on the scatterplot** from the previous step:

plt.xlabel('Happy Score')
plt.ylabel('Income')

plt.scatter(X, Y, color='green', alpha=0.5)
plt.plot(X, Y_pred, color='red')
plt.show()

the result shows that there might be a relation between those two:

Step 4: Polynomial Linear Regression in Python

Finally let's check how to extend linear regression with polynomial features in Python. This approach is more precise in some cases:

from sklearn.preprocessing import PolynomialFeatures

# prepare Polynomial Features
pr = PolynomialFeatures(degree = 5)
X_poly = pr.fit_transform(X)
pr.fit(X_poly, Y)

# Polynomial Linear Regression
lin_reg = LinearRegression()
lin_reg.fit(X_poly, Y)

# plot
plt.scatter(X, Y, color='green', alpha=0.5)
plt.scatter(X, lin_reg.predict(pr.fit_transform(X)))
plt.show()

The result:

We are fitting the model with degree = 5 which:

• degree equal to 1 will be the same as the linear regression
• higher degree will take more time to compute
• higher degree will match relation better

Example for degree 15:

Resources

• Notebook
• matplotlib.pyplot.scatter
• sklearn.linear
• sklearn.preprocessing