Conteúdo do Curso
Classification with Python
Classification with Python
Challenge: Implementing Logistic Regression
To implement Logistic Regression in Python, the LogisticRegression
class is used:
For now, you can stick with the default parameters. Creating and fitting the model can be done in a single line:
python
The dataset for this chapter comes from a Portuguese banking institution and contains information from marketing campaigns conducted via phone calls. The goal is to predict whether a client will subscribe to a term deposit, based on their personal, financial, and contact-related details, as well as outcomes of previous marketing interactions.
The data is already preprocessed and ready to be fed to the model.
Swipe to start coding
You are given a Portuguese bank marketing dataset stored as a DataFrame
in the df
variable.
- Split the dataset into training and test sets, allocating 80% for the training data. Set
random_state=42
, and store the resulting sets in theX_train
,X_test
,y_train
,y_test
variables. - Initialize and fit a Logistic Regression model on the training set, storing the fitted model in the
lr
variable. - Calculate the accuracy on the test set and store the result in the
test_accuracy
variable.
Solução
Obrigado pelo seu feedback!
Challenge: Implementing Logistic Regression
To implement Logistic Regression in Python, the LogisticRegression
class is used:
For now, you can stick with the default parameters. Creating and fitting the model can be done in a single line:
python
The dataset for this chapter comes from a Portuguese banking institution and contains information from marketing campaigns conducted via phone calls. The goal is to predict whether a client will subscribe to a term deposit, based on their personal, financial, and contact-related details, as well as outcomes of previous marketing interactions.
The data is already preprocessed and ready to be fed to the model.
Swipe to start coding
You are given a Portuguese bank marketing dataset stored as a DataFrame
in the df
variable.
- Split the dataset into training and test sets, allocating 80% for the training data. Set
random_state=42
, and store the resulting sets in theX_train
,X_test
,y_train
,y_test
variables. - Initialize and fit a Logistic Regression model on the training set, storing the fitted model in the
lr
variable. - Calculate the accuracy on the test set and store the result in the
test_accuracy
variable.
Solução
Obrigado pelo seu feedback!