Spam Email Classification

This project demonstrates how to classify emails as Spam or Ham (Not Spam) using Natural Language Processing (NLP) and a Random Forest Classifier.

Features

• Preprocessing: Cleans and processes email text (removes punctuation, converts to lowercase, stems words, and removes stopwords).
• Vectorization: Converts text data into numerical format using CountVectorizer.
• Model Training: Uses a Random Forest Classifier for prediction.
• Prediction: Classifies new emails as Spam or Ham.

Requirements

• Python 3.7 or higher
• Libraries:
  • numpy
  • pandas
  • nltk
  • scikit-learn

Install required libraries:

pip install numpy pandas nltk scikit-learn

Dataset

The dataset used for this project:

• Columns:
  • text: The email content.
  • label_num: The label (0 for Ham, 1 for Spam).

Replace 'spam_ham_dataset.csv' with your dataset file.

How It Works

1. Data Preprocessing:

   • Converts text to lowercase.
   • Removes punctuation.
   • Applies stemming to reduce words to their root forms.
   • Removes stopwords (e.g., "the", "is", "in").

2. Feature Extraction:

   • Text is converted to a bag-of-words representation using CountVectorizer.

3. Model Training:

   • Splits data into training and testing sets.
   • Trains a Random Forest Classifier on the training data.

4. Email Prediction:

   • Takes an example email, preprocesses it, and predicts if it's Spam or Ham.

Usage

1. Load the dataset:

   data = pd.read_csv('spam_ham_dataset.csv')

2. Run the code to train the model and evaluate accuracy:

   cl.score(X_test, y_test)

3. Predict an email:

   prediction = cl.predict(x_email)
   print(f"Prediction: {'Spam' if prediction[0] == 1 else 'Ham'}")

Output

• Prints the model's prediction (Spam or Ham) for a sample email.
• Displays the actual label from the dataset for comparison.

Notes

• Ensure the dataset is in the correct format before running the notebook.
• The nltk library requires downloading stopwords:

  nltk.download('stopwords')

License

Feel free to use and modify this project for learning purposes.