Classification algorithms are employed to assign data to specific groups, as discussed earlier. In this section, we will explore an algorithm commonly used for such tasks: Logistic Regression.

Logistic regression is particularly suitable for regression analysis when the dependent variable is dichotomous, meaning it has two distinct categories. Similar to other regression analyses, logistic regression serves as a predictive analysis. It enables us to describe data and explain the relationship between a dependent binary variable and one or more independent variables that may be nominal, ordinal, interval, or ratio-level. (Statistics Solutions, 2022).

Logistic regression is specifically designed for binary classification tasks, where the objective is to classify data into two categories. However, it is worth knowing that logistic regression can also be extended for multiple-category classification by using a softmax classifier otherwise known as multinomial logistic regression.

In summary, logistic regression is a powerful algorithm that excels in binary classification tasks. It offers valuable insights and predictive capabilities to analyze and interpret relationships between variables in a variety of applications.

Visualization of logistic regression

Image from **The math behind Logistic Regression by** Khushwant Rai

The provided image illustrates the functioning of a logistic regression algorithm.

Advantages:

• Ease of implementation: Logistic regression is relatively straightforward to implement and does not require complex coding.
• Reduced likelihood of overfitting: Compared to other models, logistic regression is less prone to overfitting, meaning it can generalize well to new data.

Disadvantages:

• Limited to linear problems: Logistic regression is primarily suitable for solving linear classification problems. It may not perform optimally when dealing with non-linear relationships in the data.
• Difficulty capturing complex relationships: Logistic regression struggles to capture intricate and non-linear relationships between variables, which may lead to reduced accuracy in certain scenarios.

Utilization of Simple Logistic Regression Algorithm:

https://www.youtube.com/watch?v=AB4BtP9RSNM

NB: The video above may seem a bit confusing but you’ll be able to build similar models in no time.