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The Logistic regression is a linear model used for binomial regression. It is used in medical science and to predict a customer’s tendency to purchase a product. It makes use of predictor variables for this purpose.

Logistic Regression allows easier analysis of results in the form of odds ratios and statistical hypothesis testing.

A generalized linear model has taken input for a non-linear link function. The linear model has the following form −

z = c_{1}x_{1}+ c_{2}x_{2}+ … c_{n}x_{n}+ i = ct x + i

Here,

c is the coefficient vector, i is the intercept value x is the observation vector

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