---
language:
- en
license: mit
tags:
- text-classification
- naive-bayes
- tf-idf
- english-nlp
- spam-detection
pipeline_tag: text-classification
metrics:
- accuracy
model-index:
- name: Spam Detection — English (Naive Bayes)
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: English Spam Dataset
      type: custom
    metrics:
    - type: accuracy
      value: 0.994
      name: Accuracy
---

# Spam Detection — English (Naive Bayes)

A lightweight spam/ham text classifier for English messages, built with a 
custom preprocessing pipeline (tokenization, stopword removal, lemmatization) 
and TF-IDF features feeding into a Multinomial Naive Bayes classifier.

## Model Details
- **Architecture:** TF-IDF + Multinomial Naive Bayes (scikit-learn Pipeline)
- **Preprocessing:** Custom transformer — hashtag/punctuation removal, 
  tokenization (NLTK), stopword removal, lemmatization (WordNet)
- **Hyperparameters:** Tuned via GridSearchCV (alpha smoothing)
- **Accuracy:** 99.4% on held-out test set

## Intended Use
Binary spam classification for English text messages/emails. Part of a 
multilingual spam detection system that automatically routes text to a 
language-specific model (English or Arabic) based on detected language.

## How to Use
```python
import joblib

model = joblib.load("spam_eng_nb.joblib")
prediction = model.predict(["Congratulations! You've won a free prize, click here now"])
print(prediction)  # 1 = spam, 0 = ham