Disaster Response Pipeline Project

Installation:

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• scikit-learn
• Jupyter-Notbook
• NLTL
• SQLAlchemy
• Flask

Project Introduction

This Project is part of data science nanodegree program by Udacity in collaboration with Figure Eight. The dataset contains pre-labelled tweet and messages from real-life disaster. In this project, messages are categorized so that you can send the messages to an appropriate disaster relief agency.
This project is divided into three section:

1. ETL Pipeline: Extract data from source, transform the data to be used in alnalysis, then load the data to SQLite database.
2. Machine learning pipeline: train a model to classifiy disaster messages.
3. Web app: that can be used by disaster relief agency to categorize messages.

Instructions:

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

   • To run ETL pipeline that cleans data and stores in database:
     python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db
   • To run ML pipeline that trains classifier and saves:
     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/

Screenshots

Screenshots of the app interface:

• Bar chart that shows distribution of message genres:

• Bar chart that shows distribution of message categories:

License

This project is licensed under the MIT License - see the LICENSE file for details