Predictive Analytics Using Regression Analysis In Data Science

Linear and logistic are the first of two kinds of regressions that usually data science professionals learn in data science. Due to so much popularity, they are having lots that most of the data scientists even fall into the confusion that there no regressions exist other than these two. To be very true, there are many regressions, but yet, to be honest, no regression is that much (popular) than these two. 

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What is Regression Analysis?

Regression analysis is a form of predictive modeling techniques that define and investigates the relationship between dependent and independent variables. This is a very popular technique mostly used in forecasting, time series modeling, or finding the relationships between two variables. Regression Analysis stands out to be one of the most important tools for data modeling and data analysis.  


Why do we use Regression Analysis?  


As regression analysis is mostly used for estimating relationships between two or more variables, there are multiple benefits of using regression analysis, and they are:


  1. It defines the significant relationships between dependent and independent variables

  2. It defines the strength of the impact of multiple independent variables on dependent variables 


Regression analysis helps in comparing many variables at a particular time. Such as the number of sales after discounts, the number of promotional activities, or the dip in the sale after the surge in price value. 


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Linear Regression


It is one of the widely used regression techniques used for analyzing and data modeling. In this method, the dependent variable is continuous, whereas the independent variable is continuous or discrete, and the nature of the line is linear. Linear Regression is the process of establishing the relationship between dependent variables with more than one variable using a straight line. The major difference between linear regression and multiple regression is that in linear regression there is only one independent variable, whereas in multiple regression there is more than one regression variable. 


Logistic regression


Logistic regression is based on the concept of probability, where it is used to find the probability of the event = success and probability of the event = failure. It is always better to use logistic regression, where the probability gives the output in two forms (yes/no), (head/tail), (0/1), (true/false). Here the binomial distribution is also applicable in this process and one of the most popular methods in regression analysis. 


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