Spam Email Classifier

Project Overview

The Spam Email Classifier is a Machine Learning project that detects whether an email or SMS message is Spam or Not Spam (Ham) based on its content.

The project uses Natural Language Processing (NLP) techniques and the Multinomial Naive Bayes algorithm for text classification.

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Features

• Classifies emails as Spam or Not Spam.
• Cleans and preprocesses text data.
• Converts text into numerical features using TF-IDF.
• Trains a Machine Learning model.
• Displays model accuracy and evaluation metrics.
• Allows users to test their own email messages.

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Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• TF-IDF Vectorizer
• Multinomial Naive Bayes

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Project Structure

Spam_Email_Classifier/
│── spam_classifier.py
│── spam.csv
│── README.md
│── requirements.txt

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Installation

Install the required libraries:

pip install pandas numpy scikit-learn

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How to Run

Run the following command:

python spam_classifier.py

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Sample Output

MODEL PERFORMANCE

Accuracy: 1.0

Enter an email message:
> Congratulations! You have won a free iPhone.

Result: SPAM EMAIL

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Machine Learning Algorithm

• Multinomial Naive Bayes

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Dataset

The project uses a CSV file (spam.csv) containing spam and ham email messages.

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Future Improvements

• Build a graphical user interface (GUI).
• Create a web application using Flask.
• Use a larger dataset for better accuracy.
• Compare Naive Bayes with Support Vector Machine (SVM).

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Author

Puja Patnabi Sri Kollu

B.Tech Student

Machine Learning Internship Project