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Get a perfect Regression Line in scikit learn,

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by vigneshchennai74 dot icon Updated: Feb 15, 2023

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The regression line is created using the linear regression algorithm from scikit-learn, which is a machine learning library for Python. The regression line shows the relationship between the input feature and the target variable and can be used to make predictions about the target variable given specific values of the input feature.


The regression line and linear regression are important tools that can be used to analyze and model the relationship between variables in a wide range of fields, helping us make better predictions, optimize processes and designs, and ultimately improve outcomes in various aspects of our lives.


Necessary libraries for performing linear regression analysis on a dataset using scikit-learn:

  • matplotlib.pyplot is a plotting library used for creating visualizations of the data and regression line.
  • numpy is a numerical computing library that provides support for array operations and linear algebra.
  • datasets module from sklearn provides access to several popular datasets for practice purposes.
  • linear_model module from sklearn contains the LinearRegression class that enables fitting a linear regression model to the data.
  • mean_squared_error and r2_score are metrics from sklearn.metrics that are used to evaluate the performance of the linear regression model.


The regression line and the concept of linear regression are very helpful in many different areas of study and practice in this world. Like:

  • Marketing and advertising: Regression analysis can be used to model the relationship between advertising spending and sales, as well as to identify which factors have the greatest impact on consumer behavior. This information is used to optimize marketing campaigns and increase sales.
  • Medicine and healthcare: Regression analysis is often used to model the relationship between different health outcomes and various risk factors such as age, weight, and lifestyle choices. This information can be used to develop more effective prevention and treatment strategies.


Here is the example of how to plot the perfect regression line using Sk-learn:

Preview of the output that you will get on running this code from your IDE

Code

In this solution we have used Scikit learn Library

How do I get a perfect Regression Line in scikit learn,

PythondotLines of Code : 52dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
# Code source: Jaques Grobler
# License: BSD 3 clause

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score

# Load the diabetes dataset
diabetes = datasets.load_diabetes()

# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)

# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print("Mean squared error: %.2f"
  % mean_squared_error(diabetes_y_test, diabetes_y_pred))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))

# Plot outputs - test data
#plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')
#plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)

# Plot outputs - training data
plt.scatter(diabetes_X_train, diabetes_y_train,  color='black')
plt.plot(diabetes_X_train, regr.predict(diabetes_X_train), color='blue', linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()
  1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
  2. Run the file to get a perfect regression line


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "How to i get a perfect Regression Line in Scikit learn" in kandi. You can try any such use case!

Dependent Library

scikit-learnby scikit-learn

Python doticonstar image 54584 doticonVersion:1.2.2doticon
License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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            scikit-learnby scikit-learn

            Python doticon star image 54584 doticonVersion:1.2.2doticon License: Permissive (BSD-3-Clause)

            scikit-learn: machine learning in Python
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have Scikit-learn that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the scikit learn page in kandi.

                      You can search for any dependent library on kandi like Scikit-learn

                      Environment Test

                      I tested this solution in the following versions. Be mindful of changes when working with other versions.


                      1. The solution is created in Python 3.7.15 version
                      2. The solution is tested on scikit-learn 1.0.2 version


                      Using this solution, we are able to get a perfect regression line using Scikit learn library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us get a perfect line in Python.

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