Linear Regression

"it allows us to understand the relationships between two continuous variables "

What is Linear Regression?

It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation Y= a *X + b

Y = a * X + b

Y – Dependent Variable
a – Slope
X – Independent variable
b – Intercept

Here we have identified the best fit line having linear equation y=0.2811x+13.9. Now using this equation, we can find the weight, knowing the height of a person.

Linear Regression is of mainly two types:

Simple Linear Regression
- Simple Linear Regression is characterized by one independent variable
Multiple Linear Regression
- Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables.

While finding best fit line, you can fit a polynomial or curvilinear regression. And these are known as polynomial or curvilinear regression.

Sample Linear Model

#Import Library

#Import other necessary libraries like pandas, numpy...

from sklearn import linear_model

#Load Train and Test datasets

#Identify feature and response variable(s) and values must be numeric and numpy arrays

x_train=input_variables_values_training_datasets

y_train=target_variables_values_training_datasets

x_test=input_variables_values_test_datasets

# Create linear regression object

linear = linear_model.LinearRegression()

# Train the model using the training sets and check score

linear.fit(x_train, y_train)

linear.score(x_train, y_train)

#Equation coefficient and Intercept

print('Coefficient: \n', linear.coef_)

print('Intercept: \n', linear.intercept_)

#Predict Output

predicted= linear.predict(x_test)

References

Google Sites

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