email classifier using machine learning

1. EMAIL SPAM
DETECTION

2. CONTENT
■ Slide 1: Title: Email Spam Classification
■ Slide 2: Title: Dataset Overview
■ Slide 3: Title: Data Preprocessing & Vectorization
■ Slide 4: Title: Naive Bayes & Logistic Regression
Classifiers
■ Slide 5: Title: Accuracy Comparison & Email Prediction

3. INTRODUCTION
• Introduction:
• Email spam is a significant issue affecting individuals, businesses, and organizations
worldwide.
• Spam emails often contain malicious content, scams, or unwanted advertising.
• Accurately classifying spam and ham (non-spam) emails is crucial for ensuring email
security and protecting users from potential threats.
• Goal of the Code:
• The purpose of the presented code is to demonstrate the application of machine learning
algorithms for email spam classification.
• By training and evaluating classifiers on a labeled dataset, the code aims to accurately
differentiate between spam and legitimate emails.
• Importance of Email Spam Classification:
• Enhanced Security: Efficient spam classification helps prevent users from falling victim to
phishing attempts, malware distribution, and fraudulent schemes.
• User Experience: Reducing the influx of spam emails improves productivity and ensures
that users receive relevant and legitimate messages.
• Resource Optimization: Identifying and filtering out spam emails saves storage space and
reduces the burden on email servers.

4. Data
Overview
• 'spam_ham_dataset.csv' dataset
used in the code.
• Contains labeled examples of spam
and ham emails.
• Data Splitting:
• Dataset split into 80% training
and 20% testing sets.
• Random state 42 for
reproducibility.
• Vectorization:
• CountVectorizer converts text
data into numerical feature
vectors.
• Training and testing data
transformed using
CountVectorizer.
Data
Preprocessing &
Vectorization

6. Naive Bayes & Logistic
Regression Classifiers
• Naive Bayes Classifier:
• Training: MultinomialNB classifier trained with vectorized training
data.
• Prediction: Naive Bayes predicts labels for the test data.
• Accuracy Calculation: accuracy_score metric used to evaluate
Naive Bayes accuracy.
• Logistic Regression Classifier:
• Training: Logistic Regression classifier trained with vectorized
training data.
• Prediction: Logistic Regression predicts labels for the test data.
• Accuracy Calculation: accuracy_score metric used to evaluate
Logistic Regression accuracy.

7. Accuracy Comparison & Email
Prediction
Accuracy
Comparison
• Bar chart comparing accuracies of
Naive Bayes and Logistic
Regression classifiers.
• X-axis: Algorithm names (Naive
Bayes, Logistic Regression).
• Y-axis: Corresponding accuracies.
Email Prediction
Examples:
• Sample test spam and ham emails
used for prediction.
• Predictions made by both Naive
Bayes and Logistic Regression
classifiers.
• Display predicted labels for the test
spam and ham emails.

9. SUMMARY
■ In this project, we successfully developed machine learning models for email
spam classification using the Naive Bayes and logistic regression algorithms.
The models exhibit high accuracy and effectively differentiate between spam
and ham email messages. These results validate the efficacy of the
implemented algorithms in addressing the email spam problem. We can
further enhance the classifiers by incorporating advanced techniques or
exploring ensemble methods.

10. THANK
YOU