Machine Learning In Python Simple Linear Regression

Simple linear regression models the relationship between a dependent variable and a single independent variable. In this article, we will explore simple linear regression and it's implementation in Python using libraries such as NumPy, Pandas, and scikit-learn. Simple Linear Regression aims to describe how one variable i.e the dependent variable changes in relation with reference to the independent variable. For example consider a scenario where a company wants to predict sales based on advertising expenditure. By using simple linear regression the company can determine if an increase in advertising leads to higher sales or not. The below graph explains the relationship between advertising expenditure and sales using simple linear regression:

The relationship between the dependent and independent variables is represented by the simple linear equation: In this equation m signifies the slope of the line indicating how much y changes for a one-unit increase in x, a positive m suggests a direct relationship while a negative m indicates an... 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 Recommended Video CourseStarting With Linear Regression in Python Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Starting With Linear Regression in Python Linear regression is a foundational statistical tool for modeling the relationship between a dependent variable and one or more independent variables. It’s widely used in data science and machine learning to predict outcomes and understand relationships between variables.

In Python, implementing linear regression can be straightforward with the help of third-party libraries such as scikit-learn and statsmodels. By the end of this tutorial, you’ll understand that: To implement linear regression in Python, you typically follow a five-step process: import necessary packages, provide and transform data, create and fit a regression model, evaluate the results, and make predictions. This approach allows you to perform both simple and multiple linear regressions, as well as polynomial regression, using Python’s robust ecosystem of scientific libraries. This page demonstrates how to perform simple linear regression using Ordinary Least Squares with scikit-learn, see here for the documentation and here for an example. The code on this page uses the Statsmodels, Matplotlib, Seaborn, NumPy and scikit-learn packages.

These can be installed from the terminal with the following commands: Once finished, import these packages into your Python script as follows: This page will use the World Copper Market 1951-1975 Dataset from Statsmodels, see here for the documentation and the “copper” tab on this page for an example. We will use the INCOMEINDEX column - an index of real per capita income - as the independent variable and the WORLDCONSUMPTION column - the world consumption of copper in millions of kilograms -... A complete hands-on guide to simple linear regression, including formulas, intuitive explanations, worked examples, and Python code. Learn how to fit, interpret, and evaluate a simple linear regression model from scratch.

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. Simple linear regression is the foundation of predictive modeling in data science and machine learning. It's a statistical method that models the relationship between a single independent variable (feature) and a dependent variable (target) by fitting a straight line to observed data points.

Think of it as finding a straight line that passes through or near your data points on a scatter plot. Simple linear regression offers simplicity and interpretability. When you have two variables that seem to have a linear relationship, this method helps you understand how one variable changes with respect to the other. For example, you might want to predict house prices based on square footage, or understand how study hours relate to test scores. Learning how to build a simple linear regression model in machine learning using Jupyter notebook in Python In the previous article, the Linear Regression Model, we have seen how the linear regression model works theoretically using Microsoft Excel.

This article will see how we can build a linear regression model using Python in the Jupyter notebook. To predict the relationship between two variables, we’ll use a simple linear regression model. In a simple linear regression model, we’ll predict the outcome of a variable known as the dependent variable using only one independent variable. We’ll directly dive into building the model in this article. More about the linear regression model and the factors we have to consider are explained in detail here. July 6, 2025 · Machine Learning · 2 min read

Linear regression is one of the most fundamental algorithms in machine learning. It helps us understand the relationship between variables and predict continuous outcomes. In this tutorial, you’ll learn how to implement linear regression using Python with pandas, scikit-learn, and matplotlib. By the end of this tutorial, you will be able to build, train, and evaluate your first machine learning model. For this tutorial, we’ll use a simple dataset with hours studied vs. scores achieved.

Visualizing helps understand the relationship between hours studied and scores. Discover content by tools and technology Python, with its rich ecosystem of libraries like NumPy, statsmodels, and scikit-learn, has become the go-to language for data scientists. Its ease of use and versatility make it perfect for both understanding the theoretical underpinnings of linear regression and implementing it in real-world scenarios. In this guide, I'll walk you through everything you need to know about linear regression in Python. We'll start by defining what linear regression is and why it's so important.

Then, we'll look into the mechanics, exploring the underlying equations and assumptions. You'll learn how to perform linear regression using various Python libraries, from manual calculations with NumPy to streamlined implementations with scikit-learn. We'll cover both simple and multiple linear regression, and I'll show you how to evaluate your models and enhance their performance. Linear regression is a statistical method used to model the relationship between a dependent variable (target) and one or more independent variables (predictors). The objective is to find a linear equation that best describes this relationship. Linear regression is widely used for predictive modeling, inferential statistics, and understanding relationships in data.

Its applications include forecasting sales, assessing risk, and analyzing the impact of different variables on a target outcome.

People Also Search

• Simple Linear Regression in Python - GeeksforGeeks
• Python Machine Learning Linear Regression - W3Schools
• Linear Regression in Python
• Machine Learning in Python: Simple Linear Regression
• Simple Linear Regression: Complete Guide with Formulas, Examples ...
• Simple Linear Regression Model using Python: Machine Learning
• Simple Linear Regression in Python | by Andi Ardiansyah - Medium
• Simple Linear Regression Implementation in Python
• Build Your First Machine Learning Model: Linear Regression with Python
• Linear Regression in Python: A Guide to Predictive Modeling