Spam Mail Classification Model

  • Category: Ai - Machine Learning
  • Client: Test purpose
  • Project date: 30-09-2024
  • Project URL: Click here

Project detail

Mail Spam Classification Model

Excited to share my latest project - Spam Mail Classification Using NLP! 🚀

This project is focused on classifying spam and ham messages using advanced Natural Language Processing (NLP) techniques and machine learning models. It uses the Naive Bayes algorithm and a comprehensive text preprocessing pipeline to effectively identify spam emails.

Below is a quick overview of the steps involved:

  • 🔍 Data Preprocessing:
    • Removing duplicates from the dataset.
    • Adding a message length feature for analysis.
    • Cleaning the messages using techniques like lowercasing, tokenization, stop word removal, and stemming.
  • 🧠 Feature Extraction:
    • Used TF-IDF vectorization to convert text into numerical features for the machine learning model.
  • 🤖 Model Training:
    • Employed the Multinomial Naive Bayes algorithm to classify the messages.
    • Achieved an accuracy score of 97% on the test dataset.
  • 📊 Data Visualization:
    • Visualized the distribution of message lengths for both spam and ham messages using Matplotlib and Seaborn.

The project demonstrates how to effectively build and evaluate an NLP model for spam detection. From data preprocessing and feature extraction to model training and evaluation, it covers all the essential steps.

Key Features:

  • 💡 Comprehensive text preprocessing including stemming and stop word removal.
  • 💡 TF-IDF vectorization for feature extraction.
  • 💡 High accuracy Naive Bayes classification model.

Check out the GitHub repository for all project files, resources, and images:

This project showcases the power of NLP and machine learning in building a robust spam classification system. It's a great example of how data preprocessing and model selection play a critical role in achieving high accuracy in text classification tasks!