Spam Detection SVM Model

This repository features a highly accurate spam detection model trained using a Support Vector Machine (SVM). It includes a pre-trained model file and an IPython Notebook for execution.

Model Perfomance

• This SVM model achieves high accuracy in detecting spam emails.
• It is trained on a well-processed dataset to optimize performance.
• The model effectively reduces false positives and enhances precision in spam classification.

Model Usage: Spam Email Detection

This model is designed to classify emails as spam or not spam using an SVM classifier.

Steps to Use the Model:

1: Load the trained model and vectorizer

import joblib
tfidf = joblib.load('tfidf_vectorizer.pkl')
classifier = joblib.load('spam.model')

2: Prepare your input text

input_text = ["Congratulations! You won a prize."]
input_tfidf = tfidf.transform(input_text)

3: Make a prediction

prediction = classifier.predict(input_tfidf)
print(prediction)

Executing the Jupyter Notebook

The provided Jupyter Notebook (spam_detection.ipynb) contains step-by-step instructions to test the model. Follow these steps to execute it:

1: Open the notebook:

jupyter notebook spam_detection.ipynb

2: Run all cell

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Done