kelvinyelyen / naivebayes-spam-classifier

NaiveBayes-Spam-Classification

Implementation Details

1. Data Loading and Preprocessing:

   • The dataset, containing email messages labeled as 'ham' (not spam) or 'spam', is loaded from a TSV file.
   • Labels are converted into binary values (0 for 'ham' and 1 for 'spam') to facilitate training.

2. Feature Extraction:

   • CountVectorizer is employed to transform the text messages into a matrix of token counts. This matrix serves as the feature set (X) for the machine learning model.
   • The vectorizer is first fitted on the training data (X_train) and then used to transform both training and test datasets.

3. Model Training:

   • A Multinomial Naive Bayes model is trained on the transformed training data (X_train_transformed, y_train). This model is particularly effective for text classification problems, where the features are represented by word counts.

4. Prediction Function:

   • The predict_spam_or_ham function takes a user input string, transforms it using the previously fitted vectorizer, and predicts whether the message is 'Spam' or 'Not Spam (ham)' using the trained Naive Bayes model.

5. Streamlit Application:

   • The app's UI allows users to input a message and classify it as 'Spam' or 'Not Spam (ham)'.
   • The st.text_area() component captures user input, and the st.button() triggers the classification. The result is displayed using st.write().

Installation and Setup

1. Environment Setup:

   • Ensure Python 3.7+ is installed.
   • Install required libraries:

     pip install streamlit pandas scikit-learn

2. Running the Application:

   • Execute the Streamlit app with the following command:

     streamlit run app.py