Project report on spam email classifier
| dc.contributor.author | Abhishek Kumar, U19KU23S0012 | |
| dc.date.accessioned | 2026-07-03T05:10:04Z | |
| dc.date.available | 2026-07-03T05:10:04Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | This project presents the design and development of an intelligent Spam Classifier system capable of detecting and filtering spam messages from both Email and SMS communications. With the exponential growth of digital communication, unwanted spam messages have become a major concern for users worldwide, leading to security threats, phishing attacks, and productivity loss. The system leverages Natural Language Processing (NLP) techniques, specifically Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, combined with machine learning algorithms from the Scikit-learn library to accurately classify messages as spam or ham (not spam). The trained model is deployed as a RESTful API using FastAPI, with an interactive HTML-based frontend allowing users to input messages and receive real-time classification results. The system achieves high accuracy on benchmark datasets, demonstrating the effectiveness of TF-IDF features paired with supervised learning for text classification tasks. This report documents the complete development lifecycle including requirement analysis, system design, implementation, testing, and future directions. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/21458 | |
| dc.language.iso | en | |
| dc.publisher | New Horizon College | |
| dc.title | Project report on spam email classifier | |
| dc.type | Learning Object |