/Disaster-Response-Project-

build a model for an API that classifies disaster messages.

Primary LanguageJupyter Notebook

Disaster Response Pipeline Project (Udacity - Data Science Nanodegree Project)

Table of Contents

  1. Description
  2. Dependencies
  3. Installing
  4. Executing Program
  5. Authors
  6. License
  7. Acknowledgement

Description

This project is a join collaboration between Udacity and Figure Eight. This project revolves around messages received from real-life disaster events. The purpose of this project is to categorize these messages in real-time by building a Natural Language Processing model.

This project goes as follows:

  1. Processing data, building an ETL pipeline to extract data from source, clean the data and save them in a SQLite DB
  2. Build a machine learning pipeline to train the which can classify text message in various categories
  3. Run a web app which can show model results in real time

Dependencies

  • Python 3.5+
  • Machine Learning Libraries: NumPy, SciPy, Pandas, Sciki-Learn
  • Natural Language Process Libraries: NLTK
  • SQLlite Database Libraqries: SQLalchemy
  • Model Loading and Saving Library: Pickle
  • Web App and Data Visualization: Flask, Plotly

Installing

To clone the git repository:

git clone https://github.com/HunterKane/Disaster-Response-Project.git

Executing Program:

  1. You can run the following commands in the project's directory to set up the database, train model and save the model.

    • To run ETL pipeline to clean data and store the processed data in the database python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/disaster_response_db.db
    • To run the ML pipeline that loads data from DB, trains classifier and saves the classifier as a pickle file python models/train_classifier.py data/disaster_response_db.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/

Authors

License

License: MIT

Acknowledgements

This project could not have been completed without the collaboration of these two contributors: