README.md

# Disaster Tweet Classification Model

## Description

This project involves developing a machine learning model to classify tweets as indicating a disaster or not. Utilizing Deep Learning techniques, specifically a fine-tuned model from the Hugging Face library, the system is trained on the disaster tweet dataset from Kaggle. The goal is to predict whether a given tweet refers to a disaster event based on its content.

By analyzing critical components of tweets, such as content and context, the BERT model leverages its deep understanding of language to accurately classify whether a tweet indicates a disaster. The model is trained on a comprehensive dataset of disaster-related tweets, enabling it to effectively differentiate between disaster and non-disaster tweets across various contexts.

This classification system can be utilized by emergency responders, news organizations, and social media analysts to quickly identify and respond to disaster-related events or to monitor trends in disaster-related communications.

## Technologies Used

### Dataset

- **Source:** [Kaggle Disaster Tweets Dataset](https://www.kaggle.com/datasets/vstepanenko/disaster-tweets)
- **Purpose:** Contains tweets labeled to indicate whether they refer to a disaster.

### Model

- **Base Model:** BERT (`bert-base-uncased`)
- **Library:** Hugging Face `transformers`
- **Task:** Binary text classification

### Approach

1. **Preprocessing:**
   - Load and preprocess the disaster tweet dataset.
   - Tokenize the tweet texts.

2. **Fine-Tuning:**
   - Fine-tune the BERT model on the preprocessed disaster tweet dataset.

3. **Training:**
   - Train the model to distinguish between disaster and non-disaster tweets.

### Key Technologies

- **Deep Learning (BERT):** For advanced text classification and contextual understanding.
- **Natural Language Processing (NLP):** For text preprocessing and analysis.
- **Machine Learning Algorithms:** For model training and prediction tasks.

## Google Colab Notebook

You can view and run the Google Colab notebook for this project [here](https://colab.research.google.com/drive/1Tl1lVcrGMyKZpwrqXKF7lxqL2444GFHo).

## Acknowledgements

- [Hugging Face](https://huggingface.co/) for transformer models.
- [NLTK](https://www.nltk.org/) for natural language processing.
- [Streamlit](https://streamlit.io/) for creating the interactive web interface.

## Author

[@AdilHayat](https://github.com/AdilHayat21173)

## Feedback

If you have any feedback, please reach out to us at [hayatadil300@gmail.com](mailto:hayatadil300@gmail.com).