This project includes a simple machine learning model to classify text messages as spam or not spam (ham). It utilizes Python's data analysis library pandas, numerical library numpy, and several components from the machine learning library scikit-learn.
The dataset used for training the classifier is a collection of text messages that have been labeled as 'ham' or 'spam'. The data is read from a CSV file and preprocessed for the classification task.
- pandas
- numpy
- scikit-learn
You can install these dependencies via pip:
pip install pandas numpy scikit-learn
The messages from the dataset are converted into a format that can be fed into a machine learning model, using a CountVectorizer for text vectorization, which turns the text messages into a matrix of token counts.
A MultinomialNB (Multinomial Naive Bayes) classifier is trained on the processed data. This classifier is particularly suited for classification with discrete features (like word counts for text classification).
After training, the model can predict whether a new message is spam. An example usage is provided that allows a user to enter a message and receive a classification result.