Revisiting Regression Analysis

Revisiting Regression Analysis

In Supervised Learning, we mostly deal with two types of variables i.e numerical variables and categorical variables.

In Supervised Learning, we mostly deal with two types of variables i.e numerical *variables and *categorical variables. Wherein regression deals with numerical variables and *classification *deals with categorical variables. Where,

Regressionis one of the most popular statistical techniques used for Predictive Modelling and Data Mining in the world of Data Science. Basically,

Regression Analysis is a technique used for determining the relationship between two or more variables of interest.

However, Generally only 2–3 types of total 10+ types of regressions are used in practice. Linear Regression and Logistic Regression being widely used in general. So, Today we’re going to explore following 4 types of Regression Analysis techniques:

  • Simple Linear Regression
  • Ridge Regression
  • Lasso Regression
  • ElasticNet Regression

We will be observing their applications as well as the difference among them on the go while working on Student’s Score Prediction dataset. Let’s get started.

1. Linear Regression

It is the simplest form of regression. As the name suggests, if the variables of interest share a linear relationship, then Linear Regression algorithm is applicable to them. If there is a single independent variable(here, Hours), then it is a Simple Linear Regression. If there are more than 1 independent variables, then it is a Multiple Linear Regression. The mathematical equation that approximates linear relationship between independent (criterion ) variable X and dependent(predictor) variable Y is:

Image for post

where, β0 and β1 are intercept and slope respectively which are also known as parameters or model co-efficients.

data-science regression-analysis elastic-net ridge-regression lasso-regression

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Regularization with Ridge, Lasso, and Elastic Net Regressions

Overview of the differences in 3 common regularization techniques — Ridge, Lasso, and Elastic Net.

Multicollinearity / Ridge / Lasso / Elastic-Net Regression using R

This Article is in the continuation of my Previous Article in which I have shown you How Multiple Linear Regression is prepared and using the information obtained from its diagnostic plot.

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Exploratory Data Analysis is a significant part of Data Science

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

Regularization with Ridge, Lasso, and Elastic Net Regressions

Overview of the differences in 3 common regularization techniques — Ridge, Lasso, and Elastic Net.