metadata
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:
pip install scikit-learn joblib pandas