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📨 Spamnett

Spamnett is an NLP-powered machine learning model that helps identify spam emails and SMS messages with high accuracy.
It uses Natural Language Processing (NLP) techniques and classification algorithms to detect suspicious or unwanted text messages automatically.

🚀 Features

  • 🧠 Trained NLP model to classify messages as Spam or Ham (Not Spam)
  • 🔍 Text preprocessing pipeline (tokenization, stopword removal, lemmatization)
  • 📊 Model evaluation with accuracy, precision, recall, and confusion matrix
  • 💾 Model persistence for easy reuse (via pickle or joblib)
  • 🌐 Optional web interface (Flask/Streamlit) for live testing

🧰 Tech Stack

  • Python 3.x
  • Scikit-learn
  • Pandas
  • NumPy
  • NLTK / spaCy
  • Flask (optional for web UI)

🧠 How It Works

  1. Preprocessing: Cleans and prepares the dataset (removes punctuation, converts to lowercase, removes stopwords, etc.)
  2. Feature Extraction: Converts text into numerical features using TF-IDF or CountVectorizer.
  3. Model Training: Trains a classifier (e.g., Naive Bayes, Logistic Regression, or SVM).
  4. Prediction: Classifies new messages as Spam or Not Spam.

⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/<your-username>/Spamnett.git
cd Spamnett