Scikit Learn Tutorial: Linear Regression Multiple Variable

In this tutorial, we will be using the Scikit Learn library to perform linear regression using multiple variables. Linear regression is a widely used statistical method for modeling the relationship between a dependent variable and one or more independent variables. Multiple variable linear regression, also known as multivariate linear regression, allows us to model the relationship between a dependent variable and multiple independent variables.

Prerequisites

Before we begin, make sure you have the following prerequisites:

• Python installed on your machine
• Scikit Learn library installed
• Basic knowledge of linear regression

Importing the necessary libraries

First, let’s import the necessary libraries for this tutorial:


import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression


Loading the dataset

Next, let’s load the dataset that we will be using for our linear regression model. You can use any dataset of your choice, but for this tutorial, we will be using a sample dataset that contains multiple independent variables and one dependent variable.


# Load the dataset into a pandas dataframe
data = pd.read_csv('dataset.csv')

# Separate the independent and dependent variables
X = data[['independent_var1', 'independent_var2', 'independent_var3']]
y = data['dependent_var']


Splitting the dataset

Before we can create our linear regression model, we need to split our dataset into training and testing sets. This will allow us to train our model on a subset of the data and evaluate its performance on another subset.


# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)


Creating the linear regression model

Now that we have our dataset ready, we can create our linear regression model using the Scikit Learn library.


# Create a linear regression model
model = LinearRegression()

# Train the model on the training set
model.fit(X_train, y_train)


Evaluating the model

Finally, let’s evaluate the performance of our linear regression model on the testing set.


# Make predictions on the testing set
predictions = model.predict(X_test)

# Evaluate the performance of the model
# (you can use any evaluation metric of your choice here)


And that’s it! You have now successfully created a linear regression model using multiple variables with the Scikit Learn library.