Description

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 events. The project aim is to build a Natural Language Processing (NLP) model to categorize messages on a real time basis.

This project is divided in the following key sections:

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

Installing

Clone this GIT repository:

git clone https://github.com/GuillaumeVerb/Disaster-Response-Pipelines.git

Executing Program:

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/messages.csv data/categories.csv data/DisasterResponse.db
To run ML pipeline that trains classifier and saves python models/train_classifer.py data/DisasterResponse.db models/classifier.pkl
Run the following command in the app's directory to run your web app. python run.py

Go to http://0.0.0.0:3001/

Disaster-Response-Pipelines

• app
  | - template
  | |- master.html # main page of web app
  | |- go.html # classification result page of web app
  |- run.py # Flask file that runs app
  

• data
  |- categories.csv # data to process
  |- messages.csv # data to process
  |- process_data.py
  |- DisasterResponse.db # database to save clean data to
  

• models
  |- train_classifer.py
  |- classifier.pkl # saved model
  

• README.md

Acknowledgements

Udacity for providing an amazing Data Science Nanodegree Program
Figure Eight for providing the relevant dataset to train the model