Spam Email Classification

Description

This project uses Natural Language Processing (NLP) and Machine Learning techniques to classify emails as Spam or Ham. It includes data preprocessing, model training, evaluation, and a web-based deployment using Streamlit.

---

Features

• Preprocesses email content for classification.
• Classifies emails as spam or ham with a trained machine learning model.
• Provides a user-friendly web interface for real-time email classification.

---

How It Works

Step 1: Data Collection and Preprocessing

• Load the dataset and clean the data by removing unnecessary columns and handling null values.
• Map labels (ham and spam) to numerical values for machine learning.

Step 2: Feature Engineering

• Convert email text into numerical features using CountVectorizer (bag-of-words approach).

Step 3: Model Selection

• The Multinomial Naive Bayes algorithm is used for its efficiency in text classification tasks.

Step 4: Model Training

• Train the Naive Bayes model using the preprocessed and vectorized data.

Step 5: Evaluation

• Evaluate the model's accuracy on a test dataset.

Step 6: Deployment

• Save the trained model and vectorizer using Pickle.
• Build and deploy the classification interface using Streamlit.

Run the app with the following command:

streamlit run SpamDetect.py