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Classification with Python

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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.

Taak

Swipe to start coding

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

  1. Import LogisticRegression class.
  2. Create an instance of class LogisticRegression with default parameters and train it.
  3. Print the accuracy on the same X, y dataset.

Oplossing

import pandas as pd
from sklearn.linear_model import LogisticRegression

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
X = df.drop('deposit', axis=1)
y = df['deposit']

lr = LogisticRegression().fit(X, y)
print(lr.score(X, y))

Was alles duidelijk?

Hoe kunnen we het verbeteren?

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Sectie 2. Hoofdstuk 3
import pandas as pd
from sklearn.linear_model import ___

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
X = df.drop('deposit', axis=1)
y = df['deposit']

lr = ___().___(X, y)
print(lr.___(X, y))

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