Email Spam Detection using Machine Learning

Project Overview

This project focuses on building a machine learning model to classify emails as Spam or Not Spam.
Using Natural Language Processing (NLP) techniques and machine learning algorithms, the model learns patterns from email text and predicts whether a given email is legitimate or spam.

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Tech Stack :

• Programming Language: Python
• Libraries:
  • scikit-learn (Machine Learning models)
  • pandas, numpy (Data handling)
  • matplotlib, seaborn (Visualization)
• Algorithms Used:
  • Logistic Regression
  • Naive Bayes (MultinomialNB)

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Project Workflow :

1. Data Preprocessing :

   • Clean and prepare email text
   • Remove stopwords, punctuations, and apply stemming

2. Feature Extraction :

   • Convert text into numerical vectors using CountVectorizer / TF-IDF

3. Model Training :

   • Train multiple ML models (Naive Bayes, Logistic Regression)

4. Evaluation :

   • Accuracy, Precision, Recall, F1-Score
   • Confusion Matrix
   • ROC Curve & AUC Score

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Results :

• Logistic Regression Accuracy: ~97%
• Naive Bayes Accuracy: ~97%

Both models performed well, with Logistic Regression showing slightly better performance.

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Dataset :

The dataset contains labeled email messages as Spam (1) or Not Spam (0).

• Text data is preprocessed (stopword removal, stemming, punctuation removal).
• Features extracted using Bag of Words / TF-IDF techniques.

Dataset Source : https://www.kaggle.com/datasets/suraj452/mail-data

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