Disaster Response Pipeline Project

Instructions:

I've gained and developed my data engineering abilities in this course, which will increase my possibilities and potential as a data scientist. In order to create a model for an API that categorizes catastrophe messages, I'll use these abilities in this project to evaluate disaster data from Appen.

File in the repository

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
|- InsertDatabaseName.db # database to save clean data to
models
|- train_classifier.py
|- classifier.pkl # saved model
README.md

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. Go to app directory: cd app

3. Run your web app: python run.py

4. Go to http://0.0.0.0:3000/