autoclass-lite

Create a simple yet powerful mini-application that leverages the 'autoclass-lite' package to classify emails into categories such as 'spam', 'promotion', 'social', and 'important'. The application should follow these steps:

1. **Data Collection**: Collect a dataset of emails labeled with their respective categories. This could be sourced from publicly available datasets or simulated data.
2. **Preprocessing**: Clean the collected email texts by removing stop words, punctuation, and performing stemming or lemmatization. Additionally, convert all text to lowercase and remove any HTML tags if present.
3. **Feature Extraction**: Utilize 'autoclass-lite' to automatically extract relevant features from the preprocessed text. This could include TF-IDF vectors or word embeddings.
4. **Model Training**: Train a classification model using 'autoclass-lite'. Ensure that the model selection and hyperparameter tuning are handled by 'autoclass-lite' to showcase its AutoML capabilities.
5. **Evaluation**: Evaluate the trained model on a separate validation set to determine its accuracy, precision, recall, and F1-score for each category.
6. **User Interface**: Develop a basic command-line interface where users can input an email text and receive a predicted category output from the model.
7. **Deployment**: Package the application as a standalone executable or deploy it as a web service using Flask or FastAPI for easy access.

Suggested Features:
- Incorporate real-time feedback mechanisms for user inputs during testing phases.
- Implement logging to track model performance metrics over time.
- Allow users to manually adjust model parameters via the UI for educational purposes.

The 'autoclass-lite' package will be crucial in automating feature extraction and model training processes, enabling developers to focus more on data preprocessing and application design.