---
language:
  - en
license: mit
tags:
  - text-classification
  - spam-detection
  - machine-learning
  - scikit-learn
  - tfidf
  - nlp
pipeline_tag: text-classification
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: Email-Spam-Detector
    results:
      - task:
          type: text-classification
          name: Spam Detection
        dataset:
          type: email-spam-dataset
          name: Email Spam Dataset
        metrics:
          - type: accuracy
            value: 0.98 # Replace with your actual validation accuracy score (e.g., 0.98 for 98%)
            name: Accuracy
---

# 📧 Email Spam Detection Model

This repository hosts an optimized Machine Learning model designed to classify incoming emails into **Spam** (unwanted/fraudulent) or **Ham** (legitimate/safe). The model leverages classical Natural Language Processing (NLP) techniques coupled with a robust Scikit-Learn pipeline for efficient classification.

---

## 🚀 Model Details

- **Model Type:** Text Classification (Binary Classification)
- **Algorithm:** Multinomial Naive Bayes / Logistic Regression (Scikit-Learn)
- **Feature Extraction:** TF-IDF (Term Frequency-Inverse Document Frequency) Vectorizer
- **Language:** English (en)
- **License:** MIT

---

## 🛠️ How to Use (Inference)

You can load and test this model locally on your machine using the Python code snippet provided below.

### Requirements

Ensure you have the necessary dependencies installed:

```bash
pip install scikit-learn joblib pandas
```