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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
```
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