Check out my application on SMS Spam Detection

https://sms-spam-detection-pfr0xnyizaf.streamlit.app/

SMS Spam Detection

This project aims to develop a machine-learning model to detect spam messages in SMS text data. It utilizes natural language processing (NLP) techniques and a supervised learning algorithm 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 [link to dataset] (https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection).

The dataset file (sms_spam_dataset.csv) 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 sms_spam_dataset.csv file in the project directory.

3. Run the sms_spam_detection.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 predict function in the script.

Results

The trained model achieved an accuracy of 97.10 % and Precision is 100 % on the test set and performed well in terms of precision, recall, and F1-score.

Metric	Score
Accuracy	97.10 %
Precision	100 %
Recall	76.19 %
F1-score	86.49 %

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

Contact

If you have any questions or suggestions regarding the project, feel free to contact Kishan Patel at [email protected].