Linear vs Logistic Regression: Key Differences Explained 📊

Whether it's predicting the stock market, estimating the likelihood of a customer churning, or even guessing the type of fruit based on its color and shape, regression is a powerful tool in the data scientist's toolbox.

Linear regression can be applied to a wide range of problems where the goal is to predict a continuous outcome based on one or more independent variables. For example, you could use linear regression to predict the price of a house based on its size, location, and other factors.

Logistic regression, on the other hand, is typically used to predict a binary outcome, such as success or failure, win or lose. It is particularly useful for classification problems, where the goal is to predict which of two or more classes a given input belongs to. For example, you could use logistic regression to predict whether a customer will churn or not based on their behavior. You could also use it to predict whether an email is spam or not based on its content.