This project is a part of the Data Scientist Nanodegree Program and aims to develop a machine learning pipeline capable of classifying disaster messages in real-time during a disaster event. The model is trained to categorize messages into multi-labels to ensure their prompt delivery to the appropriate disaster response agency. Additionally, the project features a web application that enables disaster response workers to input messages and obtain classification results.

The project consists of three folders:

1. app: includes run.py to launch the app, and the templates folder containing HTML files.

2. data: contains the raw data files disaster_messages.csv and disaster_categories.csv, as well as process_data.py that stores the processed data in a SQLite database DisasterResponse.db.

3. models: contains the machine learning pipeline train_classifier.py that train the model using the processed data and store it as a pickle file classifier.pkl.

To run the project, you should have Python 3.5 or higher installed on your machine.

1. Run the following commands in the project's root directory to set up your database and model.

python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db

python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

2. Run the following command in the app's directory to run your web app.

python run.py

3. Go to http://0.0.0.0:3001/