Top 3 Regression Techniques in Data Science

In today’s digital era, a majority of the manual tasks are undergoing automation. We live in a world of constant progress on the technical front, with computing getting advanced day after day. Irrespective of whether you are an expert or a beginner in data science, it is impossible to ignore the growing importance of data and our ability to analyze, organize, and contextualize it. Though being skilled in coding is essential for data scientists, it is not all about software engineering alone. Data scientists live at the intersection of statistics, critical thinking, and coding.

It is important fordata scientists to understand the regression techniques as they need to have a clear and deep understanding of the statistical theories and the idea behind each technique, in order to understand how and when to use them. Statistical learning is a fundamental ingredient that shapes a data scientist. This article will analyze the top 3 regression techniques out of the many significant ones that a data scientist must be familiar with.


Let us start with the basics.


What is Data Science?


Data science refers to a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data science experts work in the realm of the unknown.

What is Regression Analysis?


It is a statistical process that helps estimate the relationships between the criterion or dependent variables and one or more independent variables or predictors. It is a reliable method that identifies the variables that are likely to have an impact on a specific topic of interest. Performing a regression analysis will help a data scientist confidently determine the factors that matter the most, the ways in which the factors will influence each other, and the factors that can be ignored. It is a powerful statistical tool that provides a detailed insight that can be applied to improve products and services. The following terms must be comprehended to understand regression analysis fully:


  • Dependent variables: These refer to the main factor that you are trying to predict or understand.


  • Independent variables: These are the factors you hypothesize will impact your dependent variable.



The three main uses of regression analysis are trend forecasting, determining the strength of predictors, and forecasting an effect.


Top 3 Popular Regression Techniques


Some of the renowned regression techniques are as follows.

1. Linear Regression


It is a widely used modelling technique. It is a method that helps predict a target variable by identifying the best linear relationship between the dependent and independent variables. The best fit is arrived at by making sure that the sum of all the distances between the actual observations and the shape at each point is as small as possible. The two types of linear regression are:


  • Simple linear regression- Makes use of a single independent variable for predicting a dependent variable by fitting the best linear relationship.
  • Multiple linear regression- This makes use of more than one independent variable for predicting a dependent variable by fitting the best linear relationship.


In linear regression, a relationship is established between the dependent variable (Y) and one or more independent variables (X) using a best fit straight line called the regression line. If there are multiple independent variables, we can choose a forward selection, backward elimination, and stepwise approach for selecting the most significant independent variables.

2. Logistic Regression


This regression technique is widely used for classification problems. Classification refers to a data mining technique that assigns categories to a group of data to aid in accurate predictions and analysis. Classification, sometimes called a decision tree, is useful in analyzing large datasets. Logistic regression is a type of classification technique that is the appropriate regression analysis to conduct to carry out when the dependent variable if binary (dichotomous). Logistic regression is a predictive analysis. It is used to describe data and explain the relationship between one dependent binary variable and one or more ordinal, nominal, interval, or ratio-level independent variables.


Let us now understand the important points to keep in mind.


  • Logistic regression can handle various types of relationships as it applies a non-linear log transformation to the predicted odds ratio. It does not require a linear relationship between dependent and independent variables.


  • The independent variables must not be correlated with each other.


  • It requires large sample sizes.


  • If the value of the dependent variable is ordinal, it is called ordinal logistic regression.


  • If the dependent variable is multiclass, it is multinomial logistic regression.


3. Stepwise Regression


This is used while dealing with multiple independent variables. In this regression technique, the independent variables are selected through an automatic process, without human intervention. This is done by observing statistical values like t-stats, R-square, and AIC metrics to discern significant variables. The stepwise regression technique fits the regression model by dropping/adding one co-variate at a given time based on a specific criterion. Some of the commonly used stepwise regression methods are as follows.


  • Forward selection- This starts with the most significant predictor in the model and keeps adding a variable for each step.


  • Standard stepwise regression- It adds and removed predictors as required for each step.


  • Backward elimination- This starts with all the predictors in the model and eliminates the least significant variable in each step.



This technique aims to maximize prediction power with a limited number of predictor variables.



Linear and logistic regressions are the most popular techniques learned by people in data science. Apart from the three techniques mentioned above, there are innumerable types of regression analysis that can be performed as each form has its own significance and a specific condition to which it can be applied.


Regression analysis is a useful statistical method that can be leveraged by data scientists to determine the degree to which particular independent variables influence dependent variables. Next time, if someone in your business proposes a hypothesis, suggest conducting a regression analysis to determine how the variables that are likely to affect that hypothesis and how confident they can be with that hypothesis. Regression analysis is beneficial to both data scientists and the company on the whole, as it helps allocate resources more efficiently, make informed business decisions, and ultimately boost your bottom line.


I hope you have now got an overview of the meaning and techniques of regression analysis. If you are interested in knowing about data science certifications, check out Global Tech Council.