bondxue / Disaster-Response-System

Disaster Response System

Introduction

In this project, I have built a machine learning system to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

Project Components

File structure of the project:

- 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 # ETL pipline 
|- DisasterResponse.db   # database to save clean data to

- models
|- train_classifier.py # ML pipline 
|- classifier.pkl  # saved model 

- notebooks
|- ETL Pipeline Preparation.ipynb # test ETL pipline 
|- ML Pipeline Preparation.ipynb # test ML pipline

- README.md

There are three main Python scripts this project:

1. process_data.py: contain ETL pipeline

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

2. train_classifier.py: contain ML pipeline

   • 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

3. run.py : Flask file that runs app

   • data visualizations using Plotly in the web app

Running Procedure

1. Run the following commands in the project's root directory to set up 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 the web app.

    python run.py

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

Demo Display