Linear Regression In Machine Learning

Table of Contents

Here some important points or content so we can easily understand all the concepts of linear regression

• What is a Linear Regression?

• How to represent of Linear Regression

• Training a Linear Regression model

• Evaluating the model

What is a Linear Regression?

The Linear regression model is used to find a relationship between one or more features(independent variables) and a continuous target variable(dependent variable).

How to represent Linear Regression?

The mathematical equation to represent the linear regression :

First import the all related libraries:

import numpy as np
import matplotlib.pyplot as plt  
# To visualize
import pandas as pd  
# To read data
from sklearn.linear_model import LinearRegression

After this training the data:

data = pd.read_csv('filename.csv')  
# load data set
X = data.iloc[:, 0].values.reshape(-1, 1)  
# values converts it into a numpy array
Y = data.iloc[:, 1].values.reshape(-1, 1)

Fit it into the model:

We can fit the linear regression into the model.

linear_regressor = LinearRegression()  
# create object for the class
linear_regressor.fit(X, Y)  
# perform linear regression
Y_pred = linear_regressor.predict(X)  
# make predictions

#machinelearning #python #datascience

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