/sms-spam-detection

The final project for the course "Data Science. NLP"

Primary LanguageJupyter Notebook

What's SMS Spam Detection

It is the final project for the course "Data Science. NLP". The main goal of the project was to build a detection system for a Ukrainian user. Presentation from the project defense can be accessed here and downloaded from here.

Table of Contens

Data Collection

The data was collected in multiple ways and labeled manually since there is no open dataset with labeled SMS messages from Ukrainian users. Here are the results:

  • 269 responses for Google Form with ~62% spam rate
  • ~5800 self-annotated SMS messages from multiple devices with ~18% spam rate

So, here is basic statistics about the final data set:

  • ~6100 SMS messages
  • ~20% spam rate (imbalancement)
  • Two classes (spam and ham)
  • Multiple languages - Ukrainian, Russian, translit (Cyrillic letters are encoded with Latin ones), English

Machine Learning Pipeline

ML Pipeline and Transformers were built on top of scikit's Pipeline/TransformerMixin classes. Transformers and Pipeline can be accessed here and here respectively. Pipeline consists of several steps:

  • Data loading and preparation
    • Text cleaning
    • Tokenization
  • Feature building (will be discussed in the next section)
  • Stratified CV splitting
  • Grid search with 5-fold CV

After determining the best model and parameters, it is fitted to a whole data set and dumped altogether with detailed metadata (performance on folds) here.

Model Description

Estimator

Logistic regression with L2 penalty was selected as the best performing model.

Features

Here is the final set of features (please see here):

  • TF-IDF character 4-grams with top 4000 features
  • Pattern based features:
    • dot included?
    • uppercased/lowercased word? (+ratios)
    • bunch of RegExes (phone number, custom spam words, currency symbols, dates, etc.)
    • emoji included?
  • Length based features:
    • No. of words
    • No. of chars
    • No. of N-grams

Evaluation Metrics

Averaged across folds metrics and confusion matrix:

  • ROC-AUC: 99.5%

  • Accuracy: 98.4%

  • Precision: 96.4%

  • F1: 96.03%

  • Recall: 95.6%

  • Confusion matrix:

    Ham Spam
    Pred Ham 968.8 10.6
    Pred Spam 8.6 232.6

Getting Started

  1. Clone from the git repository:

     $ git clone https://github.com/KarimLulu/sms-spam-detection.git

  2. Change directory to sms-spam-detection and start up the application:

     $ cd sms-spam-detection
 $ docker-compose up

  3. Go to http://localhost:8000 and submit text messages to test the system

  4. Enjoy!

License

SMS Spam Detector is released under the MIT License.