Disaster Response Pipeline Project

Table of Contents

• Overview
• Project structure
• Requirements
• Components
• Instructions
• Acknowledgements

Overview

In this project, I applied my data engineering skills to processe data and build a model trained for an API that classify disaster text data. This project can use in disaster events so that the messages could be sent to an appropriate disaster relief agency. The project include a machine learning pipeline and a web interface for demo where emergency worker can input a messenge and get classification result.

Project structure

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

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

• README.md

Requirements

Requirements libraries for this project is listed below, which can be install via pip.

• pandas
• sqlalchemy
• nltk
• sklearn
• pickle
• flask
• json
• plotly
• joblib

Components

This project has three components:

1. ETL Pipeline

   • A Python script "process_data.py" to load data from csv file, preprocess data and store it in SQLite database.
   • Two data files: disaster_categories.csv, disaster_messages.csv

2. ML Pipeline

   • A Python script "train_classifier.py" writes a machine learning pipeline to load data from SQLite database, spit data into train and test dataset, train model and export model as pickel file.

3. Flask Web App

   • A web app to demo how messenge can be classification.

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 after access app directory (cd app). python run.py

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

Acknowledgements

Thanks Udacity for helping me complete this project.