Disaster Response Pipeline Project

I applied the data pipeline skills learned to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. I built a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.

Table of Contents

1. Instructions
2. File Descriptions
3. Licensing, Authors, and Acknowledgements

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/

File Descriptions:

process_data.py: A ETL Pipeline that

• loads the messages and categories datasets
• merges the two datasets
• cleans the data
• stores it in a SQLite database

train_classifier.py: A Machine Learning Pipeline that

• 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

run.py: A Flask Web App that visualizes the results

Licensing, Authors, Acknowledgements

Must give credit to Figure Eight for the data.