Spam Filter: RNN-Powered Message Classification

Description

This is a Flask web application that uses RNN to filter messages into spam or not-spam. Users input the message, and the trained model outputs if the message is spam.

Technologies

• Python 3.9+
• Flask (web application framework)
• pandas, numpy, nltk (data handling)
• tensorflow, keras-tuner (RNN)
• matplotlib, seaborn, wordcloud (for data analysis)
• dill (for saving)
• kaggle (for dataset download)

Features

• Data Ingestion - load and split dataset
• Data Transformation - preprocessing and feature engineering
• Model Training - train and evaluate RNN models
• Prediction Pipeline - reusable pipeline for inference
• Flask Web App - interactive UI for predictions

Installation and Running

1. Clone the repository:

git clone https://github.com/Hanna-hk/Spam_Filter.git
cd Spam_Filter

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Run the Flask server:

python app.py

5. Open in your browser:

http://127.0.0.1:5000/

Usage

1. Enter your message in the text box
2. Click the "Classify" button
3. View the results (Spam or Not Spam)
4. See the confidence level of the prediction

Model Performance

The RNN model achieves high accuracy in classifying messages with:

• Loss: 12.17%
• Accuracy: 98.58%
• Precision: 95.70%
• Recall: 92.71%