SMS Spam Detector/Classifier

My SMS Spam Classifier is a helpful tool for dealing with spam text messages. It uses Multinomial Naive Bayes model to figure out if a text message is spam or not. All you have to do is copy and paste or type in the text message, and the app will tell you if it’s spam.

Process

I began by setting up my development environment, choosing to work with Python and the powerful Streamlit framework. Streamlit allowed me to quickly build an interactive web application that would serve as the user interface for my spam classifier.

With the dataset in hand, I started by exploring and preprocessing the data. I cleaned the text messages, removing any unnecessary characters, and then used techniques like tokenization and vectorization to convert the text into a format that could be fed into a machine learning model.

After careful consideration, I decided to use a Naive Bayes classifier as the core of my spam detection algorithm. This model has proven to be effective in text classification tasks, and it was relatively straightforward to implement and train.

To train the model, I split the dataset into training and testing sets, ensuring that the model would be evaluated on unseen data. I then fine-tuned the hyperparameters of the Naive Bayes classifier, experimenting with different configurations to achieve the best possible performance.

Once the model was trained and tested, I integrated it into the Streamlit application.