# Logistic Regression with Python

Logistic Regression Algorithm in Machine Learning with Python.

In Machine Learning the logistic regression algorithm continues to be one of the most widely used methods in data mining in general and in the classification of binary data in particular. In this article, I will introduce you to the implementation of Logistic Regression with Python.

## Introduction to Logistic Regression Algorithm

Regression in logistic regression does not mean that it is a regression algorithm. Logistic regression extends linear regression with a logistic function to make it suitable for classification.

The logistic regression algorithm is arguably the simplest machine learning algorithm for classification tasks. It is helpful to think of the problem as having only two entities and a dataset divided into two classes.

To create a classifier using the logistic regression algorithm, you must first find the row that best divides the data into target classes. A two-dimensional line can be described by two parameters. These two numbers are the model parameters that you need to determine.

The process of using the logistic regression algorithm mainly consists of four simple steps:

1. You can start the search by selecting the parameter values ​​at random, thus placing a random line in the two-dimensional figure.
2. Measure how far this line separates the two classes. In logistic regression, you use statistical deviance as a measure of goodness-of-fit.
3. Guess new values ​​of the parameters and measure the power of separation.
4. Repeat until there are no better guesses. This is an optimization procedure that can be performed with a range of optimization algorithms. Gradient descent is a popular choice for a simple optimization algorithm.

## Logistic Regression with Python

You can read more about the implementation of the logistic regression algorithm with Python below:

## Conclusion

The logistic regression algorithm is relatively simple to understand, compared to more complex algorithms. It is also simple in terms of calculation, which makes it adaptable to large data sets. This can sometimes overfit the data, and you often have to use a technique called regularization that limits this danger. 