ashmithamb/spamorham

Project Overview:

The project aims to build a spam or ham classification model using machine learning. The goal is to accurately identify incoming messages as either spam or non-spam, improving communication efficiency and reducing the risk of scams.

Project Statement:

The project addresses the problem of spam messages by developing a classification model trained on labeled data. The model will predict the class of new messages, allowing for real-time spam detection and filtering.

Tools and API Used:

Python, Pandas, NLTK (Natural Language Toolkit), Scikit-learn (CountVectorizer, TfidfTransformer, SVM, metrics).

Conclusion:

The project develops a spam or ham classification model using machine learning. By accurately filtering spam messages, it enhances communication efficiency and reduces the risk of fraudulent activities. The project showcases the effectiveness of machine learning and natural language processing in real-world spam detection.