Multiple Linear Regression Using Python Ml Geeksforgeeks

Leo Migdal

-Dec 4, 2025, 7:13 AM

multiple linear regression using python ml geeksforgeeks

Linear regression is a statistical method used for predictive analysis. It models the relationship between a dependent variable and a single independent variable by fitting a linear equation to the data. Multiple Linear Regression extends this concept by modelling the relationship between a dependent variable and two or more independent variables. This technique allows us to understand how multiple features collectively affect the outcomes. Steps to perform multiple linear regression are similar to that of simple linear Regression but difference comes in the evaluation process. We can use it to find out which factor has the highest influence on the predicted output and how different variables are related to each other.

Equation for multiple linear regression is: y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \beta_n X_n The goal of the algorithm is to find the best fit line equation that can predict the values based on the independent variables. A regression model learns from the dataset with known X and y values and uses it to predict y values for unknown X. In multiple regression model we may encounter categorical data such as gender (male/female), location (urban/rural), etc. Since regression models require numerical inputs then categorical data must be transformed into a usable form.

This is where Dummy Variables used. These are binary variables (0 or 1) that represent the presence or absence of each category. For example: W3Schools offers a wide range of services and products for beginners and professionals, helping millions of people everyday to learn and master new skills. Enjoy our free tutorials like millions of other internet users since 1999 Explore our selection of references covering all popular coding languages

Create your own website with W3Schools Spaces - no setup required Test your skills with different exercises DigitalOcean vs. AWS Lightsail: Which Cloud Platform is Right for You? Multiple Linear Regression is a fundamental statistical technique used to model the relationship between one dependent variable and multiple independent variables. In Python, tools like scikit-learn and statsmodels provide robust implementations for regression analysis.

This tutorial will walk you through implementing, interpreting, and evaluating multiple linear regression models using Python. Before diving into the implementation, ensure you have the following: Multiple Linear Regression (MLR) is a statistical method that models the relationship between a dependent variable and two or more independent variables. It is an extension of simple linear regression, which models the relationship between a dependent variable and a single independent variable. In MLR, the relationship is modeled using the formula: Example: Predicting the price of a house based on its size, number of bedrooms, and location.

In this case, there are three independent variables, i.e., size, number of bedrooms, and location, and one dependent variable, i.e., price, that is the value to be predicted. Multiple linear regression in machine learning is a supervised algorithm that models the relationship between a dependent variable and multiple independent variables. This relationship is used to predict the outcome of the dependent variable. Multiple linear regression is a type of linear regression in machine learning. There are mainly two types of linear regression algorithms − Let's discuss multiple linear regression in detail −

In machine learning, multiple linear regression (MLR) is a statistical technique that is used to predict the outcome of a dependent variable based on the values of multiple independent variables. The multiple linear regression algorithm is trained on data to learn a relationship (known as a regression line) that best fits the data. This relation describes how various factors affect the result. This relation is used to forecast the value of dependent variable based on the values of independent variables. In linear regression (simple and multiple), the dependent variable is continuous (numeric value) and independent variables can be continuous or discreet (numeric value). Independent variables can also be categorical (gender, occupation), but they need to be converted to numerical values first.

A comprehensive guide to multiple linear regression, including mathematical foundations, intuitive explanations, worked examples, and Python implementation. Learn how to fit, interpret, and evaluate multiple linear regression models with real-world applications. This article is part of the free-to-read Data Science Handbook Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.

This visualization breaks down the multiple linear regression solution into its component parts, making the abstract matrix operations concrete and understandable. The X'X matrix shows how features relate to each other, X'y captures feature-target relationships, and the inverse operation transforms these into optimal coefficients. The best way to understand multiple linear regression is through visualization. Since we can only directly visualize up to three dimensions, we'll focus on the case with two features, which creates a 3D visualization where we can see how the model fits a plane through... In the previous topic, we have learned about Simple Linear Regression, where a single Independent/Predictor(X) variable is used to model the response variable (Y). But there may be various cases in which the response variable is affected by more than one predictor variable; for such cases, the Multiple Linear Regression algorithm is used.

Moreover, Multiple Linear Regression is an extension of Simple Linear regression as it takes more than one predictor variable to predict the response variable. We can define it as: Prediction of CO2 emission based on engine size and number of cylinders in a car. In Multiple Linear Regression, the target variable(Y) is a linear combination of multiple predictor variables x1, x2, x3, ...,xn. Since it is an enhancement of Simple Linear Regression, so the same is applied for the multiple linear regression equation, the equation becomes: b0, b1, b2, b3 , bn....= Coefficients of the model.

Machine Learning can be easy and intuitive – here's a complete from-scratch guide to Multiple Linear Regression. Linear regression is the simplest algorithm you’ll encounter while studying machine learning. Multiple linear regression is similar to the simple linear regression covered last week – the only difference being multiple slope parameters. How many? Well, that depends on how many input features there are – but more on that in a bit. Today you’ll get your hands dirty implementing multiple linear regression algorithm from scratch.

This is the second of many upcoming from scratch articles, so stay tuned to the blog if you want to learn more. Today’s article is structured as follows: You can download the corresponding notebook here. In this tutorial, you will learn how to perform a multiple linear regression in Python. If you don't have pandas and statsmodels already installed, execute the following command in your terminal: For demonstration purposes, let's work with fish market data which you can download by clicking here.

Import it and have a first look at the raw data: The dataset has 159 entries recording the fish species (categorical values!), its weight, three lengths dimensions (vertical, diagonal, cross), height and width. Let's say, you want to predict the weight of a fish from the other variables, i.e,. your linear regression model:

Multiple Linear Regression Using Python Ml Geeksforgeeks

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