Linear Regression Python Implementation Geeksforgeeks

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],

Linear Regression is a supervised learning algorithm which is both a statistical and a machine learning algorithm. It is used to predict the real-valued output y based on the given input value x. It depicts the relationship between the dependent variable y and the independent variables xi ( or features ). The hypothetical function used for prediction is represented by h( x ). Linear regression with one variable is also called univariant linear regression. After initializing the weight vector, we can find the weight vector to best fit the model by ordinary least squares method or gradient descent learning.

Mathematical Intuition: The cost function (or loss function) is used to measure the performance of a machine learning model or quantifies the error between the expected values and the values predicted by our hypothetical... The cost function for Linear Regression is represented by J. So, our objective is to minimize the cost function J (or improve the performance of our machine learning model). To do this, we have to find the weights at which J is minimum. One such algorithm which can be used to minimize any differentiable function is Gradient Descent. It is a first-order iterative optimizing algorithm that takes us to a minimum of a function.

Dataset used in this implementation can be downloaded from link. 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... Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables.

It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between the dependent and independent variables, they are considering and the number of independent variables being used. This article is going to demonstrate how to use the various Python libraries to implement linear regression on a given dataset. We will demonstrate a binary linear model as this will be easier to visualize. In this demonstration, the model will use Gradient Descent to learn. You can learn about it here.

Step 1: Importing all the required libraries The low accuracy score of our model suggests that our regressive model has not fit very well with the existing data. This suggests that our data is not suitable for linear regression. But sometimes, a dataset may accept a linear regressor if we consider only a part of it. Let us check for that possibility. We can already see that the first 500 rows follow a linear model.

Continuing with the same steps as before. Step 8: Evaluation Metrics For Regression Linear regression is a widely used statistical method to find the relationship between dependent variable and one or more independent variables. It is used to make predictions by finding a line that best fits the data we have. The most common approach to best fit a linear regression model is least-squares method which minimize the error between the predicted and actual values. Equation of a straight line is given as:

To build a simple linear regression model we need to calculate the slope (m) and the intercept (b) that best fit the data points. These parameters can be calculated using mathematical formulas derived from the data. Consider a dataset where the independent attribute is represented by x and the dependent attribute is represented by y. Intercept (b): b = \bar{y} - m \cdot \bar{x} Slope = 28/10 = 2.8 Intercept = 14.6 - 2.8 * 3 = 6.2. Therefore the desired equation of the regression model is y = 2.8 x + 6.2

We use these values to predict the values of y for the given values of x. Python is one of the most popular programming languages. It’s simple to use, packed with features and supported by a wide range of libraries and frameworks. Its clean syntax makes it beginner-friendly. The following is a simple program that displays the message “Hello, World!” on the screen. To understand working of this code, refer to our article Python Introduction.

Try our ongoing free course Python Skillup with weekly topic coverage, notes, daily quizzes and coding problems. In this section, we’ll cover the basics of Python programming, including installing Python, writing first program, understanding comments and working with variables, keywords and operators. 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. Linear regression is one of the simplest yet most powerful machine learning algorithms.

It’s used to predict numerical values, like house prices or sales figures, based on input features. This guide explains linear regression in simple terms, dives into its mathematical foundations, and provides a step-by-step Python implementation. Imagine you’re trying to predict someone’s house price based on its size. Linear regression finds a straight line that best fits the relationship between size (input) and price (output). For non-technical readers, think of it as drawing a line through a scatter plot to make predictions. For technical readers, linear regression models the relationship as: [ y = \beta_0 + \beta_1x + \epsilon ] Where:

The goal is to minimize the mean squared error (MSE) to find the best ( \beta_0 ) and ( \beta_1 ). Let’s implement linear regression using Python with the Scikit-Learn library and a real dataset.

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