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Introduction To Machine Learning
  1. What Is Machine Learning Beginners Guide
  2. Supervised Vs Unsupervised Learning Key Differences
  3. Scikit Learn Tensorflow Keras Beginners Guide
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Data Preprocessing And Feature Engineering
  1. Understanding Data Types Machine Learning
  2. Handling Missing Data Outliers Data Preprocessing
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Supervised Learning With Scikit Learn
  1. Master Scikit Learn Basics Api Data Splitting Workflows
  2. Predict House Prices Linear Regression Scikit Learn
  3. Logistic Regression Spam Detection Scikit Learn
  4. Decision Trees Random Forests Scikit Learn
  5. Master Support Vector Machines Svm Classification
  6. Model Evaluation Cross Validation Precision Recall F1 Score
Unsupervised Learning With Scikit Learn
  1. Introduction To Clustering Kmeans Dbscan Hierarchical
  2. Master Pca Dimensionality Reduction Scikit Learn
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Introduction To Deep Learning Tensorflow Keras
  1. What Is Deep Learning Differences Applications
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Cnns For Image Processing
  1. Introduction To Cnns For Image Processing
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Rnns And Lstms
  1. Understanding Rnns Lstms Time Series Data
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Natural Language Processing
  1. Text Preprocessing Nlp Tokenization Word Embeddings
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  3. Text Classification Bert Tensorflow Keras Guide
Deploying Machine Learning Models
  1. Exporting Models Tensorflow Scikit Learn
  2. Deploy Machine Learning Models Flask Fastapi
  3. Deploying Ml Models To Cloud Platforms
All Course > Python Machine Learning > Supervised Learning With Scikit Learn Oct 11, 2024

Logistic Regression for Spam Detection with Scikit-Learn

In the last lesson, we explored linear regression, which helps predict continuous values like house prices. Now, we'll shift gears to logistic regression, a method used for binary classification tasks. Unlike linear regression, which predicts numbers, logistic regression predicts probabilities that fall between 0 and 1. This makes it perfect for problems like spam detection, where we need to classify emails as either "spam" or "not spam."

Table of Contents

I recently worked on a project where I had to build a spam filter for an email service. The goal was to classify thousands of emails quickly and accurately. Using logistic regression, I was able to create a model that could predict whether an email was spam with high confidence. This practical experience taught me how powerful logistic regression can be for binary classification tasks.

In this tutorial, we’ll walk through the steps of building a spam detection model using Scikit-Learn. We’ll also learn how to evaluate its performance using metrics like accuracy, precision, recall, and F1 Score. By the end, you’ll have a solid understanding of logistic regression and how to apply it to real-world problems.

Difference Between Linear and Logistic Regression

Linear regression is used to predict continuous outcomes, like house prices or temperature. It works by fitting a straight line to the data, which minimizes the difference between predicted and actual values. However, when dealing with binary outcomes—like spam or not spam—a straight line isn’t the best fit.

Logistic regression, on the other hand, uses a sigmoid function to map predictions to probabilities. The sigmoid function ensures that the output is always between 0 and 1, which makes it ideal for classification tasks. For example, if the model predicts a probability of 0.8 for an email, it means there’s an 80% chance the email is spam.

In my spam detection project, I initially tried using linear regression, but the results were poor. The model couldn’t handle the binary nature of the problem. Switching to logistic regression made a huge difference, as it was designed specifically for such tasks.

Applying Logistic Regression to Spam Detection

To build a spam detection model, we first need a dataset of labeled emails. Each email is represented by features like word frequency, presence of specific keywords, or email length. These features help the model learn patterns that distinguish spam from non-spam emails.

Here’s how I approached the problem:

  1. Data Preparation: I cleaned the data by removing stop words, punctuation, and converting text to lowercase.

  2. Feature Extraction: I used a technique called TF-IDF (Term Frequency-Inverse Document Frequency) to convert text into numerical features.

  3. Model Training: I split the data into training and testing sets, then trained a logistic regression model using Scikit-Learn.

Here’s a code snippet for training the model:

from sklearn.linear_model import LogisticRegression  
from sklearn.feature_extraction.text import TfidfVectorizer  
from sklearn.model_selection import train_test_split  

# Sample data  
emails = ["win a free prize", "meeting at 3 pm", "claim your reward"]  
labels = [1, 0, 1]  # 1 for spam, 0 for not spam  

# Feature extraction  
vectorizer = TfidfVectorizer()  
X = vectorizer.fit_transform(emails)  

# Train-test split  
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2)  

# Train model  
model = LogisticRegression()  
model.fit(X_train, y_train)

Evaluating Model Performance

Once the model is trained, we need to evaluate its performance. For binary classification, metrics like accuracy, precision, recall, and F1 Score are commonly used.

  • Accuracy: Measures the percentage of correct predictions.

  • precision: Tells us how many predicted spam emails were actually spam.

  • Recall: Indicates how many actual spam emails were correctly identified.

  • F1 Score: Combines precision and recall into a single metric.

In my project, the model achieved an accuracy of 95%, but the recall was lower at 85%. This meant that some spam emails were slipping through the filter. To improve this, I fine-tuned the model by adjusting the threshold for classification.

Here’s how to calculate these metrics in Scikit-Learn:

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

# Predictions  
y_pred = model.predict(X_test)  

# Evaluation  
accuracy = accuracy_score(y_test, y_pred)  
precision = precision_score(y_test, y_pred)  
recall = recall_score(y_test, y_pred)  
f1 = f1_score(y_test, y_pred)  

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

Conclusion

In this tutorial, we learned how logistic regression works and how to apply it to a spam detection problem. We covered the differences between linear and logistic regression, the steps to build and train a model, and how to evaluate its performance using key metrics.

Logistic regression is a powerful tool for binary classification, but it’s just the beginning. In the next lesson, we’ll dive into decision trees and random forests, which offer even more flexibility and accuracy for complex datasets. If you’re ready to take your machine learning skills to the next level, don’t miss the next tutorial!

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