Spam Detection System

Lite Model

Introduction

Features

• Text Preprocessing: Lemmatization, removal of stop words and punctuation.
• Feature Extraction: Text length, word count, unique word count, uppercase count, special character count.
• Model Creation: Ensemble model using SVC, MultinomialNB, and ExtraTreesClassifier.
• Visualization: Generates graphs for dataset insights, word clouds, and performance metrics.
• Metrics Saving: Accuracy, precision, and F1 score.

How to Run

1. Train the Model:
   python training/train_model_lite.py
2. Use the Model:
   python

   1import joblib 2model = joblib.load('models/model.pkl') 3vectorizer = joblib.load('models/vectorizer.pkl')

Legacy Model

Introduction

Features

• Text Preprocessing: Porter Stemming, removal of stop words and punctuation.
• Model Creation: Ensemble model using SVC, MultinomialNB, and ExtraTreesClassifier with original parameters.
• Visualization: Generates graphs for dataset insights, word clouds, and performance metrics.
• Metrics Saving: Accuracy and precision.

How to Run

1. Train the Model:
   python training/train_model_legacy.py
2. Use the Model:
   python

   1import joblib 2model = joblib.load('models/model.pkl') 3vectorizer = joblib.load('models/vectorizer.pkl')

Additional Information

• Dependencies: Python 3.6 or higher, pip, and required packages listed in requirements.txt.
• Dataset: The dataset used for training is spam.csv.
• Contact and Support: For questions or support, please contact the project maintainers.