SMS Classifier
Introduction:
In an age where instant messaging is ubiquitous, SMS spam is a nuisance we all face. This, however, presents a fantastic learning opportunity for those new to data science. Let's dive into how to build a machine learning model for SMS spam detection, a perfect project for beginners in Natural Language Processing (NLP) and machine learning.
The Project Objective:
Our mission is to develop a machine learning model that accurately identifies SMS messages as either spam or ham (non-spam). This project serves as an excellent introduction to text classification and NLP.
Gathering the Data:
The "SMS Spam Collection Dataset," available on my github Repository, will be our starting point. This dataset is a compilation of SMS messages categorized as 'spam' or 'ham.'
Tools of the Trade:
We'll be using Python, along with libraries such as pandas for data handling, scikit-learn for machine learning, and NLTK for NLP.
Conclusion and Next Steps:
This project is a great way for beginners to get hands-on experience with machine learning and NLP. As you grow more comfortable, you can experiment with advanced techniques like text normalization (stemming, lemmatization) and TF-IDF vectorization. The world of data science is vast and exciting, and projects like these are your first step towards mastering it.