Machine Learning using Python| How to build a Logistic Regression Moel in Python

Logistic regression is a statistical model used to analyze and predict the relationship between a binary dependent variable and one or more independent variables. It is a type of regression analysis used when the dependent variable is binary, meaning it can take only two values, such as 0 or 1, true or false, yes or no, etc.

In logistic regression, the dependent variable is modeled using a logistic function, which produces an S-shaped curve that maps the probability of the dependent variable taking a particular value, given the values of the independent variables. The logistic function takes the form of:

P(y=1|x) = 1 / (1 + exp(-z))

Where P(y=1|x) is the probability of the dependent variable (y) taking the value 1, given the independent variables (x), exp is the exponential function, and z is the linear combination of the independent variables and their associated coefficients. The logistic regression model estimates the coefficients of the independent variables that maximize the likelihood of the observed data.

Logistic regression is widely used in various fields, such as finance, marketing, and healthcare, for predicting outcomes such as default, churn, disease diagnosis, etc.