SMS Spam Detection

This project involves building a spam detection model using the SMS Spam Collection dataset. The dataset contains 5,574 SMS messages in English, tagged as either 'ham' (legitimate) or 'spam'.

Dataset Overview

• Dataset Source: SMS Spam Collection Dataset on Kaggle
• Total Messages: 5,574
• Labels:
  • Ham (Legitimate): 4,825 messages
  • Spam: 747 messages

Project Steps

1. Data Preprocessing:

   • The raw text messages were preprocessed by:
     • Replacing email addresses, URLs, money symbols, and phone numbers with specific tokens (emailaddr, httpaddr, moneysymb, phonenumbr).
     • Converting all characters to lowercase.
     • Removing all punctuations.
     • Stemming the words using PorterStemmer.
     • Removing stopwords.
   • The resulting cleaned text was stored in a corpus for further analysis.

2. Feature Extraction:

   • The CountVectorizer was used to convert the text corpus into a matrix of token counts.

3. Label Encoding:

   • The labels ('ham' and 'spam') were encoded as 0 and 1, respectively.

4. Model Training and Evaluation:

   • Two different classifiers were used to train the model:

     1. Gaussian Naive Bayes

        • Accuracy: 87.09%

        • Confusion Matrix:

          [[824, 125], [ 19, 147]]

        • Classification Report:

                  precision    recall  f1-score   support
          
               0       0.98      0.87      0.92       949
               1       0.54      0.89      0.67       166
          

          accuracy 0.87 1115 macro avg 0.76 0.88 0.80 1115 weighted avg 0.91 0.87 0.88 1115

     2. Decision Tree Classifier

        • Accuracy: 97.13%

        • Confusion Matrix:

          [[944, 5], [ 27, 139]]

        • Classification Report:

                  precision    recall  f1-score   support
          
               0       0.97      0.99      0.98       949
               1       0.97      0.84      0.90       166
          

          accuracy 0.97 1115 macro avg 0.97 0.92 0.94 1115 weighted avg 0.97 0.97 0.97 1115

Conclusion

• The Decision Tree Classifier performed better with an accuracy of *97.13%, compared to *87.09% with Gaussian Naive Bayes.

Dependencies

• Python 3.x
• Libraries: pandas, numpy, nltk, sklearn, chardet, requests, io

How to Run

1. Clone the repository: bash git clone https://github.com/yourusername/sms-spam-detection.git cd sms-spam-detection

2. Install the required dependencies: bash pip install -r requirements.txt

3. Run the notebook or script to train the model and make predictions.