- The project develops a sentiment analysis model to classify text data into positive or negative sentiments, utilizing Python for preprocessing and model training.
- Survival rate (0-1, with 1 being the best chance of survival).
- This approach allows for capturing semantic relationships between words, improving the model's ability to interpret and analyze the sentiment of varied text inputs effectively.
- Python 3
- NLTK - Natural Language Toolkit
- Pandas
- NumPy
- TensorFlow