What are some common modeling techniques in data science?

What are some common modeling techniques in data science?

Some common modeling techniques include linear regression, decision trees, and neural networks. Would you like to know more about any specific technique?

Can you explain linear regression?

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship and is often used for prediction.

What are decision trees?

Decision trees are a type of model that uses a tree-like graph of decisions and their possible consequences. They are easy to interpret and can handle both classification and regression tasks.

How do neural networks work?

Neural networks are inspired by the human brain and consist of layers of interconnected nodes. They learn from data by adjusting the weights of connections based on the error of predictions.