atwahsz/disaster-response-pipeline

Udacity-DSND-PROJECT

Disaster Response Pipeline Project

Installation

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, warnings.

Project Overview

This code is designed to iniate a web app which an emergency operators could exploit during a disaster (e.g. an earthquake or Tsunami), to classify a disaster text messages into several categories which then can be transmited to the responsible entity

The app built to have an ML model to categorize every message received

File Description:

• process_data.py: This python excutuble code takes as its input csv files containing message data and message categories (labels), and then creates a SQL database
• train_classifier.py: This code trains the ML model with the SQL data base
• ETL Pipeline Preparation.ipynb: process_data.py development procces
• ML Pipeline Preparation.ipynb: train_classifier.py. development procces
• data: This folder contains sample messages and categories datasets in csv format.
• app: cointains the run.py to iniate the web app.

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/ Or Go to http://localhost:3001/

Screenshots

Screenshot 1: App analysis of the data base

Screenshot 2: App word search Page

Licensing, Authors, Acknowledgements

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