Spam Classification App

Built with Flask, powered by Scikit-Learn, styled via Bootstrap, and deployed through Heroku.

Spam Classifier uses a logistic regression model to classify messages (preferably SMS) as spam or not spam with 98.21% accuracy.

Prerequisites

Make sure that you have the following:

• Python 3+ and pip (which comes with Python 3+)
• A Unix command line (e.g. Git Bash).

Running the App

What each file does:

• server.py - runs the server and loads the user interface.
• templates/layout.html - contains the base HTML
• templates/index.html - contains the body of the HTML
• models/saved/ - contains all locally saved models and dataframes for future loading
• models/save_df.py - cleans the dataframe and saves the new dataframe to local file structure.
• models/model.py - builds and saves the logistic regression model.

To run the app, complete the following steps:

1. Make sure you have Python 3 and a text editor installed.
2. Install the virtualenv package using pip install virtualenv.
3. Install the required packages using pip install -r requirements.txt. You can manually install them as you come across them if need be, but this will install them all for you. Note that if you add more packages, run pip freeze > requirements.txt to save them to your requirements file.
4. Create a virtual environment using virtualenv <environment-name> and start it using source ./<environment-name>/Scripts/activate. Note that the activate script directory might change depending on your operating system.
5. Run the models/save_df.py file. This will save the cleaned dataframe as a csv under the models/saved directory.
6. Run the models/model.py file. This will build the model and save it as a .joblib file under the models/saved directory.
7. Run the server.py file. Make sure that the saved/ directory contains both the dataframe.csv and model.joblib files.

More Info

To learn how to build your own spam classifier, refer to the MLC@UVA tutorial