A machine learning project to classify SMS messages as spam or ham (not spam) using natural language processing (NLP) techniques.

• Utkarsh Vataliya
• Smit Soni

This project aims to build an SMS spam classifier using traditional NLP techniques and machine learning algorithms. The model learns to distinguish spam messages from legitimate ones using a labeled dataset of SMS messages.

• Source: UCI SMS Spam Collection Dataset
• Format: CSV file with two columns:
  • label: spam or ham
  • message: text content of the SMS

• Language: Python
• Libraries: pandas, scikit-learn, nltk, matplotlib, seaborn
• Modeling: Naive Bayes, Logistic Regression, SVM, etc.
• Notebook: Jupyter Notebook (sms_spam_detection.ipynb)

• Lowercasing
• Punctuation removal
• Stopword filtering
• Stemming

• Spam vs Ham distribution
• Common word frequencies

• Using TF-IDF and CountVectorizer

• Tested models: Naive Bayes, Logistic Regression, SVM

• Accuracy, Precision, Recall, F1-score
• Confusion Matrix

We're always open to feedback, suggestions, and collaboration on similar NLP or machine learning projects.

Connect with us on LinkedIn:

• Utkarsh Vataliya
• Smit Soni

Feel free to reach out — let’s build something cool together! 🚀