/Disaster-Response-Pipelines

Code for Udacity-DataScientistTerm02-Project02

Primary LanguagePython

Disaster Response Pipeline

Table of Contents

1. Project Overview

In this project , I have built pipelines of ETL and Machine Learning Model to categorize disaster data from Figure Eight , and developed a web application to visualize the results and classified disaster messages .

Below are a few screenshots of the web app.

2. Project Components

There are three components of this project:

2.1. ETL Pipeline

File data/process_data.py contains data cleaning pipeline that:

  • Loads the messages and categories dataset
  • Merges the two datasets
  • Cleans the data
  • Stores it in a SQLite database

2.2. ML Pipeline

File models/train_classifier.py contains machine learning pipeline that:

  • Loads data from the SQLite database
  • Splits the data into training and testing sets
  • Builds a text processing and machine learning pipeline
  • Trains and tunes a model using GridSearchCV
  • Outputs result on the test set
  • Exports the final model as a pickle file

2.3. Flask Web App

This web application designed with bootstrap to:

  • Process and visualize the cleaned data using Pyecharts
  • Classifier message from user input, and display the predicted disaster categories

3. Quick Start

  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://127.0.0.1:5000/

4. Files

├── app/
│   ├── run.py------------------------# FLASK FILE THAT RUNS APP
│   ├── static
│   │   └── favicon.ico---------------# FAVICON FOR THE WEB APP
│   └── templates
│       ├── go.html-------------------# CLASSIFICATION RESULT PAGE OF WEB APP
│       └── master.html---------------# MAIN PAGE OF WEB APP
├── data/
│   ├── disaster_categories.csv-------# DATA TO PROCESS
│   ├── disaster_messages.csv---------# DATA TO PROCESS
│   └── process_data.py---------------# ETL PIPELINE
├── imgs------------------------------# SCREENSHOTS OF THE WEB APP
├── models/
│   └── train_classifier.py-----------# MACHINE LEARNING PIPELINE

5. Licensing/ Acknowledgements

Must give credit to Figure-eight for the data and Udacity for creating a beautiful learning experience.

Creative Commons