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Data science has given a lot when it comes to predicting smart results and trends for businesses and firms. There are a variety of methods and ways in which the data is analyzed and processed to produce meaningful information from a chunk of unstructured data.

One such method used in data science is logistic regression, it is a statistical data analyzing method which helps us in predicting results based on pre-requisite or prior relevant data.

Let us know more about logistic regression in this article.

Logistic regression produces a dependent variable or outcome variable as its outcome. A dependent variable is dependent or calculated with the help of independent variables which is our prior information. For example, we can use logistic regression to find out whether any particular team will win the match or not in the upcoming cricket match.

Prior data could be the history of wins and losses of that team, the current form of players, the current form of the opposition team, past record of the team on that particular ground/stadium, etc. This information is our pre-requisite and then based on this information only logistic regression predicts whether the team will win the cricket match or not.

Logistic regression always gives an absolute value. If you look at the aforementioned example, there would be no discontinuous outcome, either the prediction is that the team will win or it will not. if the probability of winning comes more than 50% after performing logistic regression, we could say that the team can win the next match.

If you look at other regression techniques like linear regression, it is less preferred in comparison to logistic regression as it produces a discontinuous outcome which will provide less clarity.

The prior information/historical data is a very important factor for a successful prediction using logistic regression, the quality information we have about past events and attributes helps in making the prediction more profound and absolute. And as more relevant data flows in as historical data, better will be our analyzing model.

In data science, the first and foremost task is data preparation. Data preparation is the process through which unstructured data is converted into structured data which will help us in extracting meaningful data.

A lot of sub-processes like data cleaning, data aggregation, data segmentation, etc. are performed under the process of data preparation. Logistic regression also helps in data preparation by allowing data sets to go in predefined buckets/slots where they can be used to predict future results.

This regression technique has also many use cases in the current scenario besides data science such as in the healthcare industry, business intelligence, machine learning, etc. Logistic regression is further classified into three types that are binomial, ordinal and multinomial.

They are classified on values that are being held by the outcome variable. We can say that this regression technique finds the relationship between outcome variable/dependent variable and one or more independent variable which also falls under the category of prior information.

The data calculated through regression can also be mapped on a graph. The formula is:

Y = mx + c

Where,

Y is the data to be predicted, m is the slope of the line, x is our prior information and c is our intercept on the y-axis. A logarithmic line separates the dependent and independent variables. Mapping the result on a graph gives us a clearer understanding of our predicted data or value. Logistic regression is often confused as a regression machine learning algorithm, it is more of a statistical algorithm. This article was all about logistic regression and its uses in the field of data science.

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