Disaster Response Pipeline Project

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/

Project Organization

├── README.md                       <- The top-level README for developers using this project
├── app
│   ├── run.py                      <- Flask file that runs app
│   └── templates                   <- HTML templates for Flask app
│       ├── 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
│   ├── DisasterResponse.db         <- database to save clean data to
│   └── process_data.py             <- Python file that loads, cleans and saves data
│
├── models
│   └── train_classifier.py         <- Python file that loads, trains and explorts the final model
│
└── requirements.txt                <- The requirements file for reproducing the analysis environment

---

Homepage Screenshot