This article provides the Concept Examples and Applications of Logistic Regression.

**What is Logistic Regression?**

Basically, Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. Hence, we can use it to model a binary outcome. In other words, we need to use a variable which can take only two values, such as 0 or 1, yes or no, success or failure, etc. So, it gives an estimate of the probability that the binary outcome is true given the values of the independent variables, using a logistic function. As a result of logistic regression we get a logistic curve, that we use to model the relationship between the dependent variable and one or more independent variables.

**Example of Logistic Regression**

In fact, an example of logistic regression could be predicting the likelihood of a customer buying a product based on their age, income, and location. So, the independent variables in this case would be age, income, and location, while the dependent variable (binary outcome) would be whether the customer purchased the product or not (yes/no, 1/0).

While, using historical data, a logistic regression model could be trained to estimate the probability of a customer purchasing the product based on their age, income, and location. For example, the model might find that customers who are older, have higher incomes, and live in urban areas are more likely to purchase the product. Further, we can use this information for a business to target their marketing efforts and make decisions about which products to offer to different customer segments.

Similarly, we can use Logistic Regression in a banking application. For the purpose of deciding whether to approve a loan application or not, we can use it.

**Applications of Logistic Regression**

In order to read about the applications of Logistic Regression, click here.

**Further Reading**

Examples of OpenCV Library in Python

A Brief Introduction of Pandas Library in Python