Summary

The website provides a tutorial on how to implement a simple linear regression model using Python libraries such as Matplotlib, Pandas, and Sklearn.

Abstract

The web content offers a concise guide for creating a linear regression model in Python, requiring only nine lines of code. It is aimed at individuals with a basic understanding of Python and is recommended for those with experience in Pandas. The tutorial instructs on installing necessary packages, retrieving and preparing a dataset, fitting the model, and visualizing the results. The process involves importing libraries, loading the dataset, reshaping the data, defining and fitting the regression model, and plotting the graph to display the linear relationship between variables. The article concludes with resources for further learning and encourages reader interaction through comments for feedback or assistance.

Opinions

The author emphasizes the simplicity and brevity of the process, claiming it can be done in just nine lines of code.
The tutorial is tailored for learners with some Python knowledge, suggesting that it builds on existing skills.
The inclusion of a real dataset for practice is seen as a valuable learning tool.
The article promotes the use of specific Python libraries (matplotlib.pyplot, pandas, sklearn.linear_model) for data manipulation and model fitting.
Visual aids, such as photographs and the final graph, are considered important for understanding and verifying the linear regression model.
The author encourages reader engagement by inviting questions, suggestions, and feedback in the comments section.
The tutorial acknowledges the work of Adarsh Menon and references Sckit-Learn’s documentation as resources for further exploration.

Visualize Linear Regression with Matplotlib, Pandas, and Sklearn

in just 9 lines of code!

What you will learn:

How to make a simple linear regression model.

What you need:

A basic understanding of Python

Recommended:

Experience with Pandas
An IDE of your choice

Packages

We need matplotlib.pyplot, pandas, and sklearn.linear_model for this tutorial.

pip install matplotlib
pip install pandas
pip install scikit-learn

Import them into your Python file:

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.linear_model import LinearRegression

Retrieving the dataset

You can download the dataset from here or make a file called data.csv, and copy the lines below into it.

32.502345269453031, 31.70700584656992

53.426804033275019, 68.77759598163891

61.530358025636438, 62.562382297945803

47.475639634786098, 71.546632233567777

59.813207869512318, 87.230925133687393

55.142188413943821, 78.211518270799232

52.211796692214001, 79.64197304980874

39.299566694317065, 59.171489321869508

48.10504169176825,  75.331242297063056

52.550014442733818, 71.300879886850353

45.419730144973755, 55.165677145959123

54.351634881228918, 82.478846757497919

44.164049496773352, 62.008923245725825

58.16847071685779,  75.392870425994957

56.727208057096611, 8.43619215887864

48.955888566093719, 60.723602440673965

44.687196231480904, 82.892503731453715

60.297326851333466, 97.379896862166078

45.618643772955828, 48.847153317355072

38.816817537445637, 56.877213186268506

66.189816606752601, 83.878564664602763

65.41605174513407,  118.59121730252249

47.48120860786787,  57.251819462268969

41.57564261748702,  51.391744079832307

51.84518690563943,  75.380651665312357

59.370822011089523, 74.765564032151374

57.31000343834809,   95.455052922574737

63.615561251453308, 95.229366017555307

46.737619407976972, 79.052406169565586

50.556760148547767, 83.432071421323712

52.223996085553047, 63.358790317497878

35.567830047746632, 41.412885303700563

42.436476944055642, 76.617341280074044

Result:

Reading the dataset

Let’s load the data set with

data = pd.read_csv('data.csv')

and reshape it using

x = data.iloc[:, 0].values.reshape(-1, 1)

y = data.iloc[:, 1].values.reshape(-1, 1)

This will turn the data into a format like this, which the program can learn from:

[[53.42680403]
 [61.53035803]
 [47.47563963]
 [59.81320787]
 [55.14218841]
 [52.21179669]
 [39.29956669]
 [48.10504169]
 [52.55001444]
 [45.41973014]
 [54.35163488]
 [44.1640495 ]
 [58.16847072]
 [56.72720806]
 [48.95588857]
 [44.68719623]
 [60.29732685]
 [45.61864377]
 [38.81681754]
 [66.18981661]
 [65.41605175]
 [47.48120861]
 [41.57564262]
 [51.84518691]
 [59.37082201]
 [57.31000344]
 [63.61556125]
 [46.73761941]
 [50.55676015]
 [52.22399609]
 [35.56783005]
 [42.43647694]]

Fitting the model

We’ll define another variable to fit the model and pass in x and y as parameters:

linear_regressor = LinearRegression().fit(x, y)

Now, we can predict the result with .predict(), we’ll pass in x as a parameter.

y_pred = linear_regressor.predict(x)

Drawing the graph

We’ll plot all of the points with plt.scatter,

plt.scatter(x, y)

and draw a line with x and our predicted value, y_pred.

plt.plot(x, y_pred, color='green')

Let’s show the graph.

plt.show()

The regression was successful!

Finished code:

Resources

Code inspired from Adarsh Menon.

Sckit-Learn’s Linear Regression Documentation.

Conclusion

Thanks!

I hope you enjoyed reading this, and that it taught you more about Linear Regression. If you have any questions, suggestions, general feedback, or your code doesn’t work, put in the comments!

Keep on coding!