Disaster Tweets Classifier 🚨🧠

This repository contains a machine learning project that classifies whether a tweet is about a real disaster or not. It uses natural language processing (NLP) techniques and a trained ML model to make predictions.

📌 Project Objective

To build a classifier that can accurately identify disaster-related tweets, helping authorities, responders, and organizations prioritize responses in real-time.

🔍 Dataset

• Source: Kaggle - Real or Not? NLP with Disaster Tweets
• Fields:
  • id: unique identifier for each tweet
  • text: the content of the tweet
  • target: 1 if the tweet refers to a real disaster, 0 if not

🧪 Features

• Text cleaning and preprocessing (removing URLs, stopwords, etc.)
• Tokenization and vectorization (TF-IDF or CountVectorizer)
• Machine Learning model (e.g., Logistic Regression, Random Forest, etc.)
• Streamlit web interface for real-time tweet classification

🛠️ Technologies Used

• Python
• Pandas, NumPy
• Scikit-learn
• NLTK / spaCy (for NLP)
• Streamlit (for frontend app)
• Jupyter Notebook (for model development and testing)

🚀 How to Run

1. Clone the repository:

git clone https://github.com/Yash-Lade/Disaster-Tweets-Classifier.git
cd Disaster-Tweets-Classifier

2. (Optional) Create a virtual environment and activate it:

python -m venv env
source env/bin/activate  # or env\Scripts\activate on Windows

3. Install dependencies:

pip install -r requirements.txt

4. Run the Streamlit app:

streamlit run app.py

🎯 Output

• Enter a tweet in the web interface.
• The model will classify whether it is Disaster or Not Disaster.

📊 Model Performance

The model is evaluated using accuracy, precision, recall, and F1-score.

🙌 Contributions

Feel free to fork the repo, make improvements, and create pull requests!

📄 License

This project is licensed under the MIT License.

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Author: Yash Lade