- Data Analysis
- Data preprocessing
- Text Normalisation
- Lemmatization
- Stop-words Removal
- Tokenization
- Embedding words into vectors using FastText
- Trained Model using LSTM, LSTM-CNN
- Model Evaluation
- Achieved the best accuracy using LSTM
- Saved the tokenizer and the DL model.
- Developed backend and frontend using Python and Streamlit library, deployed the web app on localhost.
- Containerized the software using Docker and tested it on localhost.
- Hosted EC2 instance on AWS and installed Docker. I connected the EC2 to GitHub using GitHub actions for the CI/CD pipeline.
- Used Elastic Container Registry (ECR) to store the Docker image
- Acurracy : Train Data - 99.08% Test Data - 99.32%