Course Content

Probability Theory Basics

## Probability Theory Basics

5. Covariance and Correlation

# Challenge: Solving the Task Using Correlation

Task

One of the most important tasks in machine learning is building a **linear regression model** (you can find more information in the Linear Regression with Python course).

Since we use a linear function in this model, we can use the correlation between features and target to indicate **how significant a particular feature** is for this model.

We will use the 'Heart Disease Dataset' now: it contains `14`

features, including the predicted attribute, which refers to the presence of heart disease in the patient. Your task is to calculate attribute importance using correlation:

- Calculate correlations between features and target.
- Print these correlations in ascending order.

Everything was clear?

# Challenge: Solving the Task Using Correlation

Task

One of the most important tasks in machine learning is building a **linear regression model** (you can find more information in the Linear Regression with Python course).

Since we use a linear function in this model, we can use the correlation between features and target to indicate **how significant a particular feature** is for this model.

We will use the 'Heart Disease Dataset' now: it contains `14`

features, including the predicted attribute, which refers to the presence of heart disease in the patient. Your task is to calculate attribute importance using correlation:

- Calculate correlations between features and target.
- Print these correlations in ascending order.

Everything was clear?