How does Logistic Regression Algorithm work? what are the use cases of this ML algorithm?

Logistic regression is a statistical Machine Learning model used to analyze the relationship between a dependent variable and one or more independent variables. It is a popular method for binary classification problems, where the goal is to predict one of two possible outcomes (such as “yes” or “no”, “true” or “false”, “spam” or “not spam”). In this article, we will explain how logistic regression works by providing some technical examples.

The basic idea behind logistic regression is to use a mathematical function that can map the inputs to the outputs. Specifically, we use the logistic function, also known as the sigmoid function, which has an “S”-shaped curve. The formula for the logistic function is:

σ(z)=1/(1+e−z1​)

where ‘z’ is a linear combination of the input variables and their weights, plus a bias term:

z=β0​+β1​x1​+β2​x2​+⋯+βn​xn

The coefficients ‘beta_i’ are estimated from the training data using a method called maximum likelihood estimation. The goal is to find the values of ‘beta_i’ that maximize the likelihood of the observed outcomes given the input variables.​

To illustrate how logistic regression works, let’s consider a simple example. Suppose we have a dataset of students who took a math test, and we want to predict whether each student passed or failed based on their score. We can represent the data as a table with two columns: the score (x) and the outcome (0 or 1, representing failure or success, respectively). Here is a sample of the data:

Student dataset

We can fit a logistic regression model to this data using the Python library scikit-learn. Here’s how to do it:

The output of this code will be: [1 1 1 1]

This means that the model predicts that a student who scored at least 55 will pass the test.

To visualize the results of the logistic regression model, we can plot the sigmoid function and the predicted probabilities as a function of the input variable. Here’s how to do it:

Here we see that the model predicts that a student who scored less than 52 will fail the test, while students who scored 53, 54 or higher will pass.

use cases of Logistic Regression in real-life Data science projects

Logistic regression is a popular machine learning algorithm that has many use cases in real-life data science projects. Here are some examples of where logistic regression can be useful:

1. Marketing: Logistic regression can be used to predict whether a customer is likely to buy a product or not based on their demographics, previous purchases, and other relevant data. This information can be used to target marketing campaigns and increase sales.
2. Fraud detection: Logistic regression can be used to identify fraudulent transactions based on patterns in the data. For example, it can be used to predict whether a credit card transaction is fraudulent based on the transaction amount, location, and other factors.
3. Medical diagnosis: Logistic regression can be used to predict the likelihood of a patient having a particular medical condition based on their symptoms, medical history, and other relevant data.
4. Sentiment analysis: Logistic regression can be used to classify text data (such as reviews or social media posts) as positive or negative based on the language used.
5. Image classification: Logistic regression can be used to classify images into different categories based on their features. For example, it can be used to classify whether an image contains a cat or a dog based on its pixel values.
6. Credit risk assessment: Logistic regression can be used to predict the likelihood of a borrower defaulting on a loan based on their credit history, income, and other factors.
7. Employee retention: Logistic regression can be used to predict which employees are likely to leave a company based on their job performance, job satisfaction, and other relevant data. This information can be used to develop retention strategies and reduce employee turnover.

These are just a few examples of the many use cases for logistic regression in real-life data science projects. Its simplicity, interpretability, and effectiveness make it a popular algorithm in many industries.