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Spam Detection using RNN

Detecting Spam with Deep Learning

Project Overview

In this project, I explore the application of Recurrent Neural Networks (RNN) in spam detection. My model is trained on a dataset of labeled messages(5572 sample) and achieves a high accuracy in distinguishing between spam and legal messages.

Key Features

  • Deep Learning: Utilizes RNN to learn patterns and sequences in data
  • High Accuracy: Achieves a high accuracy in spam detection(99.3% training accuracy and 95.52% testing accuracy)
  • Open-Source: Fully open-source and available for modification and improvement

Dataset

Data Preprocessing

  • Lowercasing
  • Punctuation Removal
  • Stop Words Removal
  • Url Handled
  • Whitespaces Removal
  • Tokenization
  • Lemmatization

Model Architecture

  • Embedding: [Ivocab size=9011, max words=50 , 32]
  • RNN: [SimpleRNN with 32 units]
  • Activation Functions: [relu]
  • Optimizer: [Adam]
  • Metrics: [Accuracy]
  • Dropout Layer: [After embedding(0.3) and after SimpleRNN(0.5)]

Results

  • Accuracy: [Training Accuracy:99.3% and Testing Accuracy:95.52%]
  • Loss: [Training Loss:0.02 and Testing Loss:0.19]

Usage

  1. Clone the repository: git clone https://github.com/Debopam-Pritam2014/Spam-Detection-Using-RNN.git

Contributing

Contributions are welcome! Feel free to fork the repository, make changes, and submit a pull request.

License

This project is licensed under the MIT License.