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---
license: mit
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
library_name: sklearn
tags:
- Email Spam Detection
- Naive Bayes
- TF-IDF
---
# 🧠 SpamDex - Spam Detection Model (v1.0)
A lightweight **Naive Bayes + TF-IDF** based spam detection model developed by **DarkNeuronAI**.
It classifies emails as **Spam (1)** or **Ham (0)** with high accuracy and fast performance — ideal for simple text classification tasks.
---
## 🚀 Features
- Fast and efficient — works on low-end systems
- Trained with real-world email dataset
- Perfect for **text classification** and **spam filtering** projects
- Easy to use and integrate
---
## 🚀 Model Overview
- **Algorithm:** Naive Bayes (MultinomialNB)
- **Vectorization:** TF-IDF (Term Frequency - Inverse Document Frequency)
- **Goal:** Classify email/text messages as Spam or Ham
- **Performance:** High accuracy on real-world datasets
---
## 🧩 Files Included
- `spam_detection_model.pkl` → Trained Naive Bayes model
- `spam_detection_vectorizer.pkl` → TF-IDF vectorizer for text preprocessing
- `example_usage.py` → Example code to use the model
- `requirements.txt` → Dependencies list
---
## 🏷️ Prediction Labels(Binary)
- **0:** Represent Ham(Not Spam) Prediction
- **1:** Represent Spam Prediction
## 💡 How to Use(Example Code)
```python
from huggingface_hub import hf_hub_download
import joblib
import string
import re
# Download and load the vectorizer
vectorizer_path = hf_hub_download("DarkNeuron-AI/darkneuron-spamdex-v1", "spam_detection_vectorizer.pkl")
vectorizer = joblib.load(vectorizer_path)
# Download and load the trained model
model_path = hf_hub_download("DarkNeuron-AI/darkneuron-spamdex-v1", "spam_detection_model.pkl")
model = joblib.load(model_path)
# Text cleaning function
def clean_text(text):
text = text.lower() # lowercase
text = re.sub(r'\d+', '', text) # remove digits
text = text.translate(str.maketrans('', '', string.punctuation)) # remove punctuation
return text.strip() # remove extra spaces
# Example usage
email_text = "Congratulations! You are the topper!"
cleaned_email = clean_text(email_text)
# Wrap text in a list for vectorizer
email_vector = vectorizer.transform([cleaned_email])
# Predict
prediction = model.predict(email_vector)
print("Prediction:", "🚨 Spam" if prediction[0] == 1 else "✅ Not Spam")
```
# Developed With ❤️ By DarkNeuronAI |