What is Linear Regression?

What is Linear Regression?

• Do you have a face lock on your smartphone?
• Do you ask Google to send a message for you or play music for you?
• Do you use Google Maps and go with the route with the least traffic?
• Do you shop online and get product recommendations? Or, you get movie recommendations on Netflix? 
• Does Facebook suggest you “People You May Know”?

Most probably, your answer to these questions is a “YES”.

So, you are surrounded by Machine Learning all through your day. Today, almost all the manual tasks are replaced by automated ones. There are machine learning algorithms that can help you play chess or even help you in carrying out surgeries. Today, there are many surgeries that require the least tearing of the body part; that’s because machine learning can assist in surgeries.

An era of constant technological processes and machine learning is revolutionizing the way we live. This revolution stands out in the way data scientists and machine learning professionals have democratized computing tools and techniques. Taking up a machine learning online course and making a career in this domain at this time can benefit you in the long run. 

Let us now explore what is machine learning and a commonly used machine learning algorithm known as Linear Regression.

What is Machine Learning?

Machine learning is a concept where a computer program can learn and adapt to the latest data without human intervention. Machine learning is a subset of artificial intelligence in which computer algorithms are kept ongoing irrespective of changes in the economy of the world. 

Machine learning utilized algorithms to transform a data set into a model. The algorithm(supervised, unsupervised, regression, classification, etc.) is chosen depending on the type of problem, the nature of data, and the computing resources available. 

A source code or complex algorithm is developed into a computer that enables a machine to identify data and make predictions about the data that it identifies. 

Some of the common machine learning algorithms are:

• Linear Regression
• Logistic Regression
• KNN or K-Nearest Neighbors
• Decision Trees
• Naive Bayes
• Random Forest
• SVM or Support Vector Machines
• LVQ or Learning Vector Quantization

In this article, we will discuss the most commonly used algorithm, which is Linear Regression. 

Linear Regression aka Least Square Regression

Regression defined

The main objective of regression is constructing a model efficient enough to predict the dependent attributes from a bunch of attribute variables. A regression problem is identified when the output variable is either a real or ongoing value which may include weight, height, area, or salary, etc.

The most basic and commonly used algorithm in predictive analytics is Linear Regression. 

The idea behind linear regression is to identify two things:

1. Does a predictor variable perform well in predicting an outcome that is a dependent variable?
2. Which variables are significant predictors of the outcome variable?

These regression estimations are used to define the relationship between one dependent variable and one or more independent variables. 

The simplest and most basic form of linear regression that has one dependant and one independent variable is given by the formula:

                Y=c+b*x

Where 

Y= estimated dependent variable score

c= constant (intercept)

b= regression coefficient (slope) and 

x= score on the independent variable.

The coefficients b and c are obtained by minimizing the sum of the squared difference of distance between the regression line and data points.

Regressions’ dependent variable is also called an outcome variable, endogenous variable, criterion variable, or regressand. The independent variable is also called as predictor variables, exogenous variables, or regressors. 

Regression analysis is used for three major estimations, namely

Identifying the strength of predictors.

The regression can be used to demonstrate the strength of the effect that the independent variable makes on a dependent variable. Some typical questions include what is the strength of a relationship between dosage and effect, sales and income, or sales and marketing expenditure.

Forecasting an effect

Regression can also be used to forecast the effects or results of changes. So, the regression analysis helps us to recognize what and how much the change in dependent variable occurs with a change in one or more independent variables. A typical question is “ how much income do I get if I work for an hour overtime twice a week?”

Forecasting the trends

Regression analysis helps in forecasting trends and future values. It also helps in determining point estimates. A typical question under this section may be, “what will be the price of petrol in 6 months?”

Types of Linear Regression

Simple Linear Regression- it works on 1 dependent variable that may be interval or ratio, and 1 independent variable which may be (interval, ratio, or dichotomous).

Multiple Linear Regression- it works on 1 dependent variable which may be a ratio or interval, and 2 or more independent variables which may be interval or ratio or dichotomous.

Logistic Regression- it works on 1 dependent variable which may be dichotomous and 2 or more independent variables that may be an interval, ratio, or dichotomous.

Ordinal Regression- it works on 1 dependent variable that is ordinal, and 1 or more independent variables that may be nominal or dichotomous.

Multinomial Regression- it works on 1 nominal variable and one or more independent variables which may be an interval, ratio, or dichotomous.

Discriminant Regression- it works on 1 dependent variable which is nominal, and one or more independent variables, which are interval or ratio.

An important thing to consider while selecting the model is model fitting. When independent variables are added to the linear regression model, it will enhance the explained variance of the model, which is expressed as R2. But it is important to note that overfitting can take place if you add too many variables to the model, which may result in reduced model generalizability. A simple model is usually preferable to a more complex model, as defined by Occam’s razor. 

Conclusion

If you wish to make a career in machine learning, you can start right away. This is because the scope of machine learning is everywhere as it has applications around all the sectors.

There are online training courses that can help you launch an excellent career in the domain. You get to choose learning hours at your convenience, and also the choice in mode of learning. The doubt sessions are carried out by industry experts to ensure that your preparation is done well.

Book your seat now!