m0hamdan/Disaster-Response-Pipelines

A Flask web app to help emergency workers analyze incoming messages and sort them into specific categories to speed up aid and contribute to more efficient distribution of people and other resources.

Disaster Response Pipeline Project

Table of Contents

1. Dependencies
2. Project Motivation
3. Content
4. Instructions
5. Licensing
6. Acknowledgements

Dependencies

1. pandas
2. numpy
3. flask
4. sqlalchemy
5. plotly
6. NLTK
7. sklearn
8. joblib

Project Motivation:

The main goal of this project is to build a web app that can help emergency workers analyze incoming messages and classify them into specific categories like Medical Help/Water/Search And Rescue.

Content:

-** ETL pipeline (data directory):** *Merges messages and categories datasets *Cleans the data and save it into a SQLite database

-** Natural Language Processing and Machine Learning Pipeline (models directory):** *Split the dataset into training and test sets *Build a ML pipeline *Train the dataset using GridSearchCV *Save the model as a *.pickle file

-** Flask Web app (app directory):

Instructions:

1. Clone the repository: git clone https://github.com/m0hamdan/Disaster-Response-Pipelines.git

2. 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'

3. Run the following command in the app's directory to run your web app. 'python run.py'

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

Licensing

![License: GNU GENERAL PUBLIC LICENSE]

Acknowledgements

1. Figure Eight for providing the messages and categories datasets used to train the model