### Learn all about Regression Analysis and the Different Types of Regression

Linear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.

Due to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.

The truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:

• Ridge regression
• Lasso regression
• Polynomial regression
• Stepwise regression, among others.

Linear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.

Regression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because they’re easy to understand and implement using a programming language of your choice.

### As a beginner with Regression Analysis, you’ll have a lot of questions:

• What is Regression Analysis?
• When should you use Regression?
• How many types of Regressions do we have?
• How much mathematical knowledge is required to understand regression?
• Ridge vs. Lasso Regression - what’s the difference?
• Which types of problems can be solved using regression?
• What are the major challenges faced by regression techniques?
• Is Regression Analysis relevant in the industry?
• Which programming language works best for regression?
• What kind of machine learning projects can you do using regression techniques?

### Fundamentals of Regression Analysis Course Curriculum

• 1
• Welcome!
• 2
##### Introduction to Regression
• What is Regression Analysis?
• Why do we use Regression?
• 3
##### Types of Regression
• How many types of regression techniques do we have?
• 4
##### Linear Regression
• Introduction to Linear Models
• Understanding Cost function
• Convexity of cost function
• Assumptions of Linear Regression
• Implementing Linear Regression
• Generalized Linear Models
• 5
##### Logistic Regression
• Introduction to Logistic Regression
• Odds Ratio
• Implementing Logistic Regression
• Multiclass using Logistic Regression
• Challenges with Linear Regression
• 6
##### Ridge Regression
• What is Ridge Regression?
• Notebook
• 7
##### Lasso Regression
• What is Lasso Regression?
• Implementation
• 8
##### Selecting the Right Model
• How to select the right regression model?
• 9
• What Next?

### Instructor(s)

• #### Analytics Vidhya

Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals.

What is Regression Analysis?

Regression Analysis is one of the most widely and popularly used techniques of analyzing data. Almost in all the data science courses that exist, regression is one of the first machine learning algorithms to be taught.

It is used when there is a cause and effect relationship between the dependent variable (target) and independent variable/variables (predictors).

How many types of Regression do we have?

There are multiple types of regression techniques for making predictions. Apart from linear regression, here are a few others that are commonly used in the industry and in research:

• Ridge regression
• Lasso regression
• Polynomial regression
• Stepwise Regression
• ElasticNet Regression, among others.

How much mathematical knowledge is required to understand regression?

The maths behind regression analysis is simple and easy to understand. The knowledge of maths includes:

• Probability
• Partial Derivation
• Linear Algebra
• Statistics

Don’t worry! We will cover the core concepts in the course itself.

Ridge vs. Lasso Regression - what’s the difference?

In mathematical terms, Ridge penalises the loss function by adding the squared value of coefficients whereas Lasso Regression penalises the loss function by adding the absolute value of the coefficient of the variable.

You’ll find out more about each regression in the course.

Which types of problems can be solved using regression?

Any problem having a cause and effect relationship can be solved by regression analysis. Regression techniques help you solve both linear and classification problems. Some practical implementations include:

• Predicting prices of a commodity
• Predicting demand of a commodity
• Predicting binary outcomes such as Credit Default
• Predicting multi-class problems such as Genre of Movie, etc.

What are the major challenges faced by regression techniques?

Some of the problems faced by regression techniques include-

• Multicollinearity - A situation where the predictor variables are correlated with one another.
• Correlation of error terms - This is when the error terms form a pattern when plotted in the graph.
• Underfitting/Overfitting - If there is an abundance of predictor variables the regression model might overfit and if there is not enough data it will lead to underfitting.

Is Regression Analysis relevant in the industry?

Absolutely! Regression analysis is one of the most commonly used methods in analytics, statistics, and data science projects. Despite the incredible number of breakthroughs in machine learning and the plethora of other algorithms out there, linear regression remains the most popular technique in a lot of organizations.

Which programming language works best for regression?

Here’s the beauty of regression analysis - you can use any tool or programming language to build regression models. You can perform regression analysis in MS Excel, R, Python, Minitab, KNIME - the list goes on and on.

We have used Python to implement the different regression types in this course.

What kind of machine learning projects can you do using regression techniques?

You can pick up almost any regression problem out there and use the techniques you learn in the course. We suggest heading over to Analytics Vidhya’s DataHack platform and picking up the problem or project you can relate to.

### FAQ

#### Common questions related to the Fundamentals of Regression Analysis course

• Who should take the Fundamentals of Regression Analysis course?

This course is aimed towards beginners in data science, machine learning and even statistics. Regression analysis and the different forms of regression like linear regression are key concepts in these fields. We have designed the course such that even newcomers will be able to follow along easily and be able to build regression models by the end of the course!

• I have some programming experience but no background in machine learning or statistics. Is this course right for me?

Absolutely! The Fundamentals of Regression Analysis course is easy to follow along and we have provided the appropriate resources where necessary throughout the course.

• What is the fee for the course?

This course is free of cost!

• How long would I have access to the “Fundamentals of Regression Analysis” course?

Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.

• How much effort do I need to put in for this course?

You can complete the “Fundamentals of Regression Analysis” course in a few hours. You are also expected to apply your knowledge of the different regression types and learning of this course to solve machine learning problems. The time taken in projects varies from person to person.

• I’ve completed this course and have decent knowledge about Regression Analysis, linear regression, and logistic regression. What should I learn next?

That’s great! We highly recommend expanding your skillset and portfolio by taking the next step in the Applied Machine Learning course. That is a comprehensive course covering the entire end-to-end machine learning pipeline and includes a thorough deep dive into the various machine learning algorithms, including linear regression and logistic regression, of course!