Logistic Regression in Python

Logistic regression is a powerful statistical method used for binary classification problems. In this tutorial, we will learn how to implement logistic regression using Python.

Installation

Before we start, make sure you have the following packages installed:

• scikit-learn
• pandas
• numpy

You can install them using pip:

pip install scikit-learn pandas numpy

Data Preparation

Let's start by creating a simple dataset using pandas:

import pandas as pd

data = {
    'feature1': [1, 2, 3, 4, 5],
    'feature2': [2, 3, 4, 5, 6],
    'label': [0, 1, 0, 1, 0]
}

df = pd.DataFrame(data)

Logistic Regression Model

Now, we will create a logistic regression model using scikit-learn:

from sklearn.linear_model import LogisticRegression

# Create a logistic regression model
model = LogisticRegression()

# Train the model
model.fit(df[['feature1', 'feature2']], df['label'])

Making Predictions

Once the model is trained, we can use it to make predictions:

# Make a prediction
prediction = model.predict([[3, 4]])

print(prediction)

Evaluate the Model

To evaluate the performance of our logistic regression model, we can use metrics such as accuracy, precision, recall, and F1 score.

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Evaluate the model
accuracy = accuracy_score(df['label'], prediction)
precision = precision_score(df['label'], prediction)
recall = recall_score(df['label'], prediction)
f1 = f1_score(df['label'], prediction)

print(f"Accuracy: {accuracy}")
print(f"Precision: {precision}")
print(f"Recall: {recall}")
print(f"F1 Score: {f1}")

Conclusion

In this tutorial, we learned how to implement logistic regression using Python. Logistic regression is a simple yet effective model for binary classification problems. For more information on logistic regression, you can read this detailed guide.

Logistic Regression