Getting Started with Machine Learning Regression Models: A Beginner’s Guide
Machine learning has emerged as a buzzword in the technological landscape, enabling businesses to generate actionable insights from the vast amounts of data they collect. One of the most popular techniques in machine learning is regression modelling, which is used to analyze and predict continuous variables based on the relationships between input variables.
In this article, we’ll explore the basics of regression modelling, its underlying principles, and its practical applications. We’ll also walk you through the steps required to build and evaluate regression models using Python.
What is Regression Modelling?
Regression modelling involves building a mathematical model that describes the relationship between a dependent variable and one or more independent variables. The dependent variable represents the output we want to predict, while the independent variables are the inputs that can influence it.
Regression models are used for a variety of purposes, such as predicting consumer behaviour, forecasting sales, and estimating market demand. They are also used for scientific research and social science studies.
Types of Regression Models
There are several types of regression models, including linear regression, logistic regression, polynomial regression, and ridge regression, among others. In this article, we’ll focus on linear regression, which is the simplest and most widely used type of regression model.
Building a Linear Regression Model
The first step in building a linear regression model is to identify the dependent and independent variables. Let’s consider the example of predicting house prices based on various factors such as location, number of rooms, and year of construction.
In this case, the dependent variable is the price of the house, while the independent variables are the location, the number of rooms, and the year of construction. We can represent this relationship as follows:
price = b0 + b1*location + b2*rooms + b3*year
where b0, b1, b2, and b3 are the coefficients of the model, representing the relationship between the variables.
Once we have identified the variables, we need to collect data for them and split the dataset into training and testing sets. We’ll use the training set to build the model and the testing set to evaluate its performance.
Evaluating the Model
The performance of a regression model can be evaluated based on several metrics, such as the mean squared error (MSE), the coefficient of determination (R-squared), and the root mean squared error (RMSE).
The MSE represents the average difference between the predicted values and the actual values, while the R-squared value indicates the proportion of the variation in the dependent variable that can be explained by the independent variables. The RMSE is the square root of the MSE and represents the average distance between the predicted and actual values.
Conclusion
Regression modelling is a powerful technique that can help businesses and researchers make accurate predictions based on past data. By building regression models, you can uncover hidden patterns and relationships in your data, and use them to drive informed decisions.
In this article, we’ve provided an overview of regression modelling, its types, and how to build and evaluate a linear regression model. We hope this beginner’s guide will help you get started with machine learning and unleash its full potential.
