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Explain Logistic Regression.

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Logistic regression is the proper regression analysis used when the dependent variable is categorical or binary. Like all regression analyses, logistic regression is a technique for predictive analysis. Logistic regression is used to explain data and the relationship between one dependent binary variable and one or more independent variables. Also, it is employed to predict the probability of a categorical dependent variable.

We can use logistic regression in the following scenarios:

To predict whether a citizen is a Senior Citizen (1) or not (0)

To check whether a person is having a disease (Yes) or not (No)

There are three types of logistic regression:

Binary Logistic Regression: In this, there are only two outcomes possible.

Example: To predict whether it will rain (1) or not (0)

Multinomial Logistic Regression: In this, the output consists of three or more unordered categories.

Example: Prediction on the regional languages (Kannada, Telugu, Marathi, etc.)

Ordinal Logistic Regression: In ordinal logistic regression, the output consists of three or more ordered categories.

Example: Rating an Android application from 1 to 5 stars.

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