NLP-Based Spam Message Classification Using Naive Bayes Algorithm

[BhoomiAgrawal12]

🔍 Problem Description:
Spam messages are a persistent issue in messaging platforms, email systems, and communication tools. It is crucial to automatically identify and filter spam messages to prevent users from receiving unsolicited, harmful, or irrelevant content. Traditional manual filtering is time-consuming and inefficient. An automated spam classification system using NLP can solve this problem effectively by classifying messages into spam or not spam.

🧠 Model Description:
The system will use the Naive Bayes algorithm, a probabilistic classifier, to classify messages as spam or non-spam based on their content. The model will be trained on a labeled dataset of spam messages using natural language processing (NLP) techniques, such as tokenization, stopword removal, and TF-IDF vectorization. The model will analyze message features and classify them with high accuracy. Streamlit will be used for the user interface, providing an easy-to-use platform for message input and classification.

⏲️ Estimated Time for Completion:
2 to 3 weeks for full implementation, testing, and integration with the Streamlit interface.

🎯 Expected Outcome:
After implementing the model, users will be able to input a message and instantly receive a classification of whether it is spam or not. This will significantly improve the efficiency of spam filtering and increase user satisfaction. Performance metrics, such as accuracy, precision, will be used to evaluate the model’s effectiveness.

📄 Additional Context:
Naive Bayes algorithm is well-suited for text classification tasks due to its simplicity, speed, and effectiveness when working with large datasets of textual data.
Streamlit will allow for real-time input and output, providing a user-friendly interface.

To be Mentioned while taking the issue:
Role: Contributor under the SWoC label.

Note:

• Please review the project documentation and ensure your code aligns with the project structure.
• Please ensure that either the predict.py file includes a properly implemented model_details() function or the notebook contains this function to print a detailed model report. The model will not be accepted without this function in place, as it is essential for generating the necessary model details.
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