| --- |
| 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 |
| 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 |
| ``` |
|
|
