After studying Machine Studying for some time now, I made a decision it was time to dive into my very first mission. I selected a Spam Classifier as a result of it wasn’t as simple as merely getting the information and making use of a pre-existing mannequin to attain a outcome; it demanded a deep dive into pure language processing and addresses a real-world drawback all of us encounter every day.
This mission focuses on analyzing incoming textual content/e mail and classifying it as both reputable (“ham”) or undesirable (“spam”). It’s been an interesting journey into the sensible utility of NLP and machine studying.
Yow will discover all of the code for this mission on my GitHub: Spam Classifier
The core of any spam detector lies in its capacity to grasp and classify textual content. Right here’s how I tackled it:
1. Information Assortment 📚📊
The primary important step was gathering a various and consultant dataset, sourcing knowledge from two key public repositories to make sure an excellent mixture of each spam and ham examples. I developed a spam classifier able to dealing with each SMS textual content messages and emails. I downloaded SMS textual content messages from the SMS Spam Collection Dataset on Kaggle and e mail knowledge from the Apache…
