Disaster Response Pipeline Project

Project Overview:

In this project, data engineering methods was applied to analyze disaster data and to build a model for an API that classifies disaster messages.

Files in repository:

1. a data set which are real messages and corresponding categories: disaster_messages.csv and disaster_categories.csv
2. a ETL pipeline python file: process_data.py and corresponding output processed database by raw dataset: DisasterResponse.db
3. a ML pipeline to do classification: train_classifier.py and corresponding classification result: classifier.pkl
4. a web app displaying visualization: run.py

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/