Spam Email Classifier

Overview

This Python script implements a Spam Email Classifier using natural language processing (NLP) techniques and machine learning. The code uses scikit-learn for data preprocessing, feature extraction, and model evaluation. The classifier is trained on a labeled dataset of emails to distinguish between spam and non-spam (ham) messages.

Getting Started

1. Clone the Repository:

   git clone https://github.com/omm-prog/SpamEmailClassifier/
   cd spam-email-classifier
   pip install pandas scikit-learn nltk
   python -m nltk.downloader stopwords
   python spam_classifier.py

Script Explanation

Data Preprocessing:

• Reads a CSV dataset:
  • Reads a CSV dataset containing 'text' and 'label' columns.

Text Preprocessing:

• Applies text preprocessing techniques, including:
  • Removing special characters.
  • Converting text to lowercase.
  • Eliminating stop words.

Feature Extraction:

• TF-IDF for Feature Extraction:
  • Utilizes TF-IDF (Term Frequency-Inverse Document Frequency) for feature extraction.
  • Converts text into numerical features.

Model Training:

• Multinomial Naive Bayes Classifier:
  • Uses the Multinomial Naive Bayes classifier as an example.
  • Splits the data into training and testing sets.
  • Trains the model on the training data.

Model Evaluation:

• Predictions and Metrics:

  • Predicts labels for the test set.
  • Calculates metrics:
    • Accuracy.
    • Precision.
    • Recall.
    • F1 score.

• Confusion Matrix:

  • Generates a confusion matrix for detailed evaluation.

Results

• Accuracy: 0.9507
• Precision: 0.9033
• Recall: 0.9249
• F1 Score: 0.9140
• Confusion Matrix: Where: [[713 29] [ 22 271]]