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Linear regression model trained to make predictions using the sci-kit-learn library.

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by vigneshchennai74 dot icon Updated: Apr 6, 2023

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This code demonstrates how a simple linear regression model can be trained and used to make predictions in Python using the sci-kit-learn library. The LinearRegression class from the sklearn.linear_model module in sci-kit-learn is used to build and train linear regression models in Python. 


Linear Regression is a supervised machine learning algorithm used for regression problems. In regression problems, the goal is to predict a continuous target variable based on one or more input variables. The linear regression algorithm fits a linear equation to the observed data between the dependent (target) and independent (predictor) variables. The equation is represented by a line that best captures the relationship between the variables.


The model.predict() method in sci-kit-learn's LinearRegression class is used to make predictions for new data based on a trained linear regression model.


Linear Regression is widely used for many applications, including forecasting, modeling, and understanding the relationship between variables.

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

Code

In this solution we have used LinearRegression

How to use .predict() method in python for linear regression?

PythondotLines of Code : 33dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import numpy as np
from sklearn.linear_model import LinearRegression

# sample dummy data 

# independent variables
time = np.arange(1,36)
price = np.random.randint(1,100,35)
ads = np.random.randint(1,10,35)
# dependent variable
y = np.random.randn(35)

# Reshape it into 35X3 where each row is an observation
train_X = np.vstack([time, price, ads]).T

# Fit the model
model = LinearRegression().fit(train_X, y)

# Sample observations for which 
# forecast of dependent variable has to be made
time1 = np.arange(37, 47)
price1 = np.array([85]*len(time1))
ads1 = np.array([4]*len(time1))

# Reshape such that each row is an observation
test_X = np.vstack([time1, price1, ads1]).T

# make the predictions
print (model.predict(test_X))'

array([0.22189608, 0.2269302 , 0.23196433, 0.23699845, 0.24203257,
       0.24706669, 0.25210081, 0.25713494, 0.26216906, 0.26720318])
  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 the output


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 "use .predict() method in python for Linear regression" in kandi. You can try any such use case!

Environment Tested

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 sci-kit-learn 1.0.2 version
  3. The solution is tested on numpy 1.21.6 version


Using this solution, we are able going to learn how to predict a simple linear regression model 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 use the .predict() method in python for Linear regression in Python.

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
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                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                          Security
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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
                                  Quality
                                    Security
                                      License
                                        Reuse

                                          If you do not have Scikit-learn and numpy 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. and numpy

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