Status: Ongoing (Showcased as a current project)
Associated with: Thynk360
Unsolicited spam messages, whether through email or SMS, remain one of the most persistent problems in digital communication. To contribute to the fight against such intrusions, I am developing a Spam Detection Classifier that uses machine learning and NLP techniques to identify and filter out spam messages in real time. This project, developed under the Thynk360 initiative, demonstrates how classic NLP and lightweight deployment frameworks can combine to build highly responsive and functional applications.
Tools & Technologies Used
- Programming Language: Python
- Libraries: scikit-learn, NLTK, Pandas
- NLP Techniques: Tokenization, TF-IDF Vectorization, Stop Word Removal
- Machine Learning Algorithms: Multinomial Naive Bayes
- Interface: Streamlit for web-based interaction
Description
The project started with the SMS Spam Collection Dataset, which includes thousands of labeled messages categorized as “spam” or “ham” (non-spam). I cleaned the text data by removing special characters, stopwords, and converting all text to lowercase. After preprocessing, I used TF-IDF vectorization to convert the textual data into numerical features that a machine learning model could understand.
For the classification task, I chose the Multinomial Naive Bayes algorithm due to its effectiveness in text-based tasks. It performs well on high-dimensional data, is computationally efficient, and yields impressive accuracy when coupled with TF-IDF features.
To make the solution user-friendly, I deployed it using Streamlit, which provided an interactive interface where users can paste a message and instantly see whether it’s classified as spam or not. The application is lightweight, browser-accessible, and delivers predictions in under a second.
Key Highlights
- Achieved 97%+ accuracy on test data using Naive Bayes and TF-IDF combination.
- Built an intuitive Streamlit interface that allows real-time input and prediction.
- Implemented a clean pipeline for text preprocessing, including stopword removal, lemmatization, and vectorization.
- Designed with modularity, enabling future extensions like multilingual support or deep learning integration.
Learned/Achieved
This project allowed me to explore the entire lifecycle of a machine learning project—from data collection and preprocessing to model training and web deployment. I gained a stronger grasp of text classification fundamentals, especially the importance of feature extraction techniques like TF-IDF in improving model performance.
Using Streamlit for deployment taught me how to rapidly prototype machine learning apps and share them with others in a visual, user-friendly format. This was my first time integrating a classifier with a front-end in such an efficient way, and it highlighted how machine learning can become impactful when coupled with accessible interfaces.
I also improved my understanding of evaluation metrics—especially precision and recall, which are crucial in spam filtering where false positives (legitimate messages flagged as spam) must be minimized.
Future Plans
Moving forward, I plan to integrate the model with email clients or SMS APIs for real-time spam detection on incoming messages. Another enhancement will be the use of deep learning models, such as LSTM or BERT, to capture more complex language patterns. I’m also exploring adversarial training techniques to make the classifier robust against obfuscation methods used by spammers, like intentional misspellings or symbol replacements.
Ultimately, this classifier is a stepping stone toward building a full-fledged AI-based communication security suite under the Thynk360 banner.
