Disaster Response Pipeline Project

Information

This repository was written in HTML and Python , and requires the following Python packages: pandas, numpy, re, pickle, nltk, flask, json, plotly, sklearn, sqlalchemy, sys.

Project Overview

This code is designed to iniate a web app which an emergency operators could exploit during a disaster, to classify a disaster text messages into several categories which then can be transmited to the responsible entity

Files

• ETL Pipeline Preparation.ipynb: Description for workspace/data/process_data.py
• ML Pipeline Preparation.ipynb: Description for workspace/model/train_classifier.py
• workspace/data/process_data.py: A data cleaning pipeline that:
  • Loads the messages and categories datasets
  • Merges the two datasets
  • Cleans the data
  • Stores it in a SQLite database
• workspace/model/train_classifier.py: A machine learning pipeline that:
  • Loads data from the SQLite database
  • Splits the dataset into training and test sets
  • Builds a text processing and machine learning pipeline
  • Trains and tunes a model using GridSearchCV
  • Outputs results on the test set
  • Exports the final model as a pickle file

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/

Licensing, Authors, Acknowledgements

This app was completed as part of the Udacity Data Scientist Nanodegree