Implement Linear Regression In Python Using Jupyter Notebook

In this post, we will be putting into practice what we learned in the introductory linear regression article. Using Python, we will construct a basic regression model to make predictions on house prices. Linear regression is a type of analysis used to make predictions based on known information and a single independent variable. In the previous post, we discussed predicting house prices (dependent variable) given a single independent variable, its square footage (sqft). We are going to see how to build a regression model using Python, pandas, and scikit-learn. Let's kick this off by setting up our environment.

Let's start by installing Python from the official repository: https://www.python.org/downloads/ Download the package that is compatible with your operating system, and proceed with the installation. To make sure Python is correctly installed on your machine, type the following command into your terminal: Linear regression is the foundation of predictive modeling and machine learning. Whether you’re predicting house prices, sales figures, or temperature trends, linear regression provides a powerful yet interpretable approach to understanding relationships between variables. This comprehensive guide will walk you through implementing linear regression in Jupyter Notebook from start to finish, covering everything from data preparation to model evaluation.

By the end, you’ll have a complete working implementation you can adapt to your own projects. Before diving into the code, you need to ensure your Jupyter Notebook environment has the necessary libraries installed. The primary tools we’ll use are NumPy for numerical operations, Pandas for data manipulation, Matplotlib and Seaborn for visualization, and scikit-learn for the machine learning implementation. Open your terminal or command prompt and install the required packages if you haven’t already: Once installed, launch Jupyter Notebook by typing jupyter notebook in your terminal. This opens a browser window where you can create a new notebook.

Create a new Python 3 notebook and you’re ready to begin. Start your notebook by importing the essential libraries: Linear regression is a statistical method that is used to predict a continuous dependent variable i.e target variable based on one or more independent variables. This technique assumes a linear relationship between the dependent and independent variables which means the dependent variable changes proportionally with changes in the independent variables. In this article we will understand types of linear regression and its implementation in the Python programming language. Linear regression is a statistical method of modeling relationships between a dependent variable with a given set of independent variables.

We will discuss three types of linear regression: Simple linear regression is an approach for predicting a response using a single feature. It is one of the most basic and simple machine learning models. In linear regression we assume that the two variables i.e. dependent and independent variables are linearly related. Hence we try to find a linear function that predicts the value (y) with reference to independent variable(x).

Let us consider a dataset where we have a value of response y for every feature x: x as feature vector, i.e x = [x_1, x_2, ...., x_n], Hey - Nick here! This page is a free excerpt from my new eBook Pragmatic Machine Learning, which teaches you real-world machine learning techniques by guiding you through 9 projects. Since you're reading my blog, I want to offer you a discount. Click here to buy the book for 70% off now.

In the last lesson of this course, you learned about the history and theory behind a linear regression machine learning algorithm. This tutorial will teach you how to create, train, and test your first linear regression machine learning model in Python using the scikit-learn library. You can skip to a specific section of this Python machine learning tutorial using the table of contents below: A practical, hands-on collection of Jupyter notebooks and Python scripts focused on the implementation of linear regression models. This repository is designed for students, data science enthusiasts, and practitioners interested in learning how to build, fit, and evaluate linear regression on real-world datasets using Python. This repository provides step-by-step resources demonstrating the practical application of linear regression for predictive analytics.

The emphasis is on working with data—from preprocessing through model fitting to evaluation and visualization—using popular Python libraries. Simple Linear Regression Implementation Fitting and interpreting a model on datasets with a single predictor. Multiple Linear Regression Implementation Extending linear regression to scenarios with multiple features/predictors. Data Preprocessing Techniques Handling missing values, encoding categorical variables, and feature scaling to prepare real datasets for regression analysis. 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. Linear regression is one of the most widely used statistical methods for modeling the relationship between a dependent variable and one or more independent variables.

At its core, linear regression seeks to find the best-fitting straight line—known as the regression line—that predicts the outcome variable from the input features. This technique is foundational in predictive modeling because of its simplicity, interpretability, and efficiency. Linear regression is applicable in many domains such as finance, healthcare, and marketing, where understanding relationships between variables is crucial. For example, predicting housing prices based on features like square footage and location, or forecasting a company’s revenue based on advertising spend, are classic examples of linear regression in action. Now, let’s translate this theory into practice. We will use the built-in Diabetes dataset from the scikit-learn library for our demonstration.

The Diabetes dataset is a classic example, frequently used to showcase linear regression, where features such as body mass index (BMI), blood pressure, and others are used to predict a measure of disease progression. Before beginning, ensure you have installed Python and the necessary libraries. You can install them using pip: Now, import the libraries in your Python script or Jupyter Notebook: We next turn to explore another type of statistical decision-making tool, linear regression. Linear regression is a tool to allow us to explore the connections between 2 or more variables.

To begin, we’ll look at simple linear regression, which models the linear association between two variables. This allows us to see how changes in one variable affect the other. To motivate the theory, we’ll take a look at how statistics related to primary school education in a given country are associated with the literacy rate in that country. The World Development Indicators dataset, maintained by the World Bank, contains information about a wide variety of topics related to global development. The most recent version of the dataset, as of writing, provides over 1500 indicators for 217 economies from 1960 to 2021. The dataset is licensed under a Creative Commons Attribution 4.0 International License.

For our example, we will look at three statistics related to literacy. We will limit our analysis to the 89 countries that have valid data for all three statistics for at least one year in the time range 2017-2021. For each country, we will consider the most recent year from that time range for which all three statistics are available. Let’s take a look at this data, which is in a file called world_bank_literacy.csv (click here to download a copy).

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