SMS Spam classification project with end to end deployment | end to end nlp project with deployment

Join us on an exciting journey as we build and deploy an end-to-end email spam or ham (non spam) or SMS spam and ham (non-spam) classification system, leveraging the power of machine learning and Streamlit. In this comprehensive project, we'll cover text preprocessing, feature engineering, and model training using popular algorithms like Support Vector Machines, Random forest classifier and more.

Learn how to optimize model performance and evaluate metrics like accuracy, precision, and recall. Then, experience the thrill of deploying our model into a user-friendly web application using Streamlit, making it accessible to anyone.

Follow along as we integrate our model with Streamlit, design a sleek and intuitive interface, and ensure seamless functionality. From data cleaning to app deployment, we'll guide you through each step, providing practical insights and best practices along the way.

Whether you're a novice enthusiast or an experienced developer, this project offers valuable learning opportunities in text classification, machine learning deployment, and web application development. Subscribe now to embark on this journey towards creating a smarter, safer messaging experience for all users.

Don't miss out on this opportunity to hone your skills and contribute to the fight against SMS spam – let's build, deploy, and interact with Streamlit for a more secure communication future!

dataset link : https://www.kaggle.com/datasets/uciml...