Spam SMS Detector 🔎

This project aims to develop a machine learning model to detect spam messages in SMS text data. It utilizes Natural Language Processing (NLP) techniques to classify SMS messages as either Spam or Non-Spam (Ham).

Dataset 🗂️

The dataset used for this project is the "SMS Spam Collection" from the UCI Machine Learning Repository. It contains a collection of 5,574 SMS messages, labeled as spam or ham. The dataset can be downloaded from here .

The dataset file (Spam SMS Collection) contains two columns:

• label: Indicates whether the message is spam (1) or ham (0).
• text: The actual text content of the SMS message.

Requirements 🛠️

To run the project, you need the following dependencies:

• Python 3.x
• pandas
• numpy
• scikit-learn
• nltk (Natural Language Toolkit)
• matplotlib

You can install the required packages by running the following command:

pip install pandas numpy scikit-learn nltk matplotlib

Usage ⚙️

1. Clone the repository or download the project files.

2. Place the Spam SMS Collection file in the project directory.

3. Run the spam_sms_classification.py script to train and evaluate the spam detection model.

4. The script will load the dataset, preprocess the text data, and train a machine learning model using the TF-IDF (Term Frequency-Inverse Document Frequency) technique.

5. After training, the model will be evaluated on a holdout set and the performance metrics (such as accuracy, precision, recall, and F1-score) will be displayed.

6. Finally, you can use the trained model to predict the label (spam/ham) of new SMS messages by modifying the function in the script.

Results 📈

The trained model achieved an accuracy of 99.50 % and Precision is 100 % on the test set and performed well in terms of precision, recall, and F1-score.

Metric	Score
Accuracy	99.5 %
Precision	100 %
Recall	99.0 %
F1-score	99.0 %

Feel free to contribute, modify, or use the code according to the terms of the license.

Feedback and Contribution 🤝

It is publicly open for any contribution. Bugfixes, new features, and extra modules are welcome.

• To contribute to code: Fork the repo, push your changes to your fork, and submit a pull request.
• To report a bug: If something does not work, please report it using GitHub Issues.

Contact 📩

If you have any questions or feedback, feel free to reach out 🙂

• Email: [email protected]
• LinkedIn : @amankshetri
• Twitter : @iamamanchhetri

© 2024 Aman Kshetri 👨‍💻