# Challenge: Implementing Logistic Regression

Now let's implement the Logistic Regression in Python!

For this, the `LogisticRegression`

class is used.

Note that by default, Logistic Regression uses the **ℓ2** regularization (`penalty='l2'`

). We will talk about regularization in later chapters. For now, we will stick to the default parameters.

The dataset for this chapter is about marketing campaigns based on phone calls from a Portuguese banking institution. The goal is to predict whether the user will subscribe to a term deposit.

The data is already preprocessed and ready to be fed to the model. Following chapters will cover the preprocessing needed for Logistic Regression.

Task

Build a Logistic Regression model and calculate the accuracy on the training set.

- Import
`LogisticRegression`

class. - Create an instance of class
`LogisticRegression`

with default parameters and train it. - Print the accuracy on the same
`X, y`

dataset.

Everything was clear?

Course Content

Classification with Python

## Classification with Python

5. Comparing Models

# Challenge: Implementing Logistic Regression

Now let's implement the Logistic Regression in Python!

For this, the `LogisticRegression`

class is used.

Note that by default, Logistic Regression uses the **ℓ2** regularization (`penalty='l2'`

). We will talk about regularization in later chapters. For now, we will stick to the default parameters.

The dataset for this chapter is about marketing campaigns based on phone calls from a Portuguese banking institution. The goal is to predict whether the user will subscribe to a term deposit.

The data is already preprocessed and ready to be fed to the model. Following chapters will cover the preprocessing needed for Logistic Regression.

Task

Build a Logistic Regression model and calculate the accuracy on the training set.

- Import
`LogisticRegression`

class. - Create an instance of class
`LogisticRegression`

with default parameters and train it. - Print the accuracy on the same
`X, y`

dataset.

Everything was clear?