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).
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.
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
-
Clone the repository or download the project files.
-
Place the
Spam SMS Collection
file in the project directory. -
Run the
spam_sms_classification.py
script to train and evaluate the spam detection model. -
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.
-
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.
-
Finally, you can use the trained model to predict the label (spam/ham) of new SMS messages by modifying the function in the script.
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.
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.
If you have any questions or feedback, feel free to reach out 🙂
- Email: chhetryaman3@gmail.com
- LinkedIn : @amankshetri
- Twitter : @iamamanchhetri
© 2024 Aman Kshetri 👨💻