Prerequisite: This assumed that you understand the concept of supervised algorithm and preliminary difference between classification and regression. Otherwise you can refer here.

In the world of Machine Learning, beginners finds it quite challenging to understand Logistic Regression both for it term and similarity & difference with Linear Regression. This algorithm is very popular classification algorithm.

To understand it further, it is also recommended to have an intuitive knowledge on Simple Linear Regression. For that, you can refer here.

Linear Regression and Logistic Regression

Intuition of Logistic Regression

Linear Regression helps us to predict stock prices, employee’s salary or temperature of a day etc, so mainly helps in prediction of continuous variable. Whereas, Logistic Regression deals with other type of problem like spam detection, employee retention, customer identification etc, hence, this help in prediction of categorical variable. The later technique is known as classification, more specifically Binary Classification.

Now, to understand the Logistic Regression, let’s consider the following example.

Figure 1. Sample Datasets

Here, we need to find out potential customers of smartphones based on the age.

In this example, you can observe a pattern like younger customer is more likely to buy a smartphone (say, 1) whereas old customers doesn’t invest much money on it (say, 0). So, understanding correlation we can try and build a machine learning model for the same.

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