Predictive Modelling — An beginner’s overview of Linear Regression Model

In our previous Day 1 post, we introduced the concept of regression in the context of machine learning, emphasizing its role in prediction modeling. <img alt="" src="https://miro.medium.com/v2/resize:fit:690/0*XnRejNK7EH77GkCG.png" style="height:400px; width:690px" /> <a href="https://learn.g2.com/hubfs/G2CM_FI416_Learn_Article_Images-%5BRegression_Analysis%5D_V1a.png" rel="noopener ugc nofollow" target="_blank">https://learn.g2.com/hubfs/G2CM_FI416_Learn_Article_Images-%5BRegression_Analysis%5D_V1a.png</a> Regression is a powerful technique that examines the relationship between two types of variables: dependent and independent variables. Fundamentally, it seeks to understand how the dependent variable changes concerning variations in the independent variable. Within the realm of regression, there are three primary types: linear, logistic, and polynomial. Here’s a succinct breakdown of their distinctions: 1. Linear Regression: — Purpose: Linear regression is employed for predictive tasks, aiming to establish a linear relationship that accurately represents the connection between two variables. 2. Logistic Regression: — Purpose: Unlike linear regression, logistic regression serves classification tasks. Its goal is to predict the probability of an instance belonging to a specific class. It employs a sigmoid function, (yielding values of either true or false.) 3. Polynomial Regression: — Purpose: Polynomial regression, while sharing similarities with linear regression, diverges when examining nonlinear relationships between variables. Instead of fitting a straight line, polynomial regression employs a polynomial function (e.g., quadratic or cubic equations) to fit the data. <a href="https://medium.com/@anjanakrishnan3100/predictive-modelling-an-beginners-overview-of-linear-regression-model-b01d820da57e">Learn More</a>