Spam-Detection-Naive-Bayes

Spam Detection Model - Naïve Bayes 📌 Model for classifying emails as spam or not spam using Naïve Bayes.

📝 Model Description This is a machine learning-based email spam classifier built using Multinomial Naïve Bayes. It analyzes the text content of emails and predicts whether they belong to the spam or not spam category. The model is trained on a dataset of labeled emails and uses TF-IDF Vectorization for feature extraction.

📂 Dataset The model is trained on a publicly available spam email dataset. Each email is labeled as Spam (1) or Not Spam (0). Preprocessing includes tokenization, stopword removal, and TF-IDF transformation. 🛠️ How It Works Preprocessing: Converts email text into a numerical format using TF-IDF Vectorization. Training: Uses Multinomial Naïve Bayes to learn spam patterns. Prediction: Classifies new emails as Spam or Not Spam based on learned patterns. 📊 Performance Metrics Accuracy: XX% (Replace with your model's accuracy) Precision: XX% Recall: XX% F1-score: XX% (The model is evaluated using Precision, Recall, and F1-score for better handling of imbalanced data.) 🔹 Usage You can use this model to classify emails as spam or not spam. Example usage in Python:

python Copy Edit from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB import pickle

Load pre-trained model and vectorizer

model = pickle.load(open("spam_model.pkl", "rb")) vectorizer = pickle.load(open("tfidf_vectorizer.pkl", "rb"))

Example email

email_text = ["Congratulations! You won a lottery. Click here to claim."] email_vector = vectorizer.transform(email_text) prediction = model.predict(email_vector)

print("Spam" if prediction[0] == 1 else "Not Spam") 🛑 Limitations The model is trained on a specific dataset and may not generalize well to new email formats. It does not analyze attachments, images, or metadata—only text. Performance can be affected by highly sophisticated spam techniques (e.g., adversarial spam). 📜 License This project is open-source under the MIT License. MIT License