• Project Motivation
• File Descriptions
• Components
• Instructions of How to Interact With Project
• Licensing, Authors, Acknowledgements, etc.

In this project, I used data provided by Figure Eight to build a model for an API that classifies disaster messages. I have created a machine learning pipeline to categorize real messages that were sent during disaster events so that the messages could be sent to an appropriate disaster relief agency. The project includes a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data.

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 # data cleaning pipeline
|- InsertDatabaseName.db # database to save clean data to

models

|- train_classifier.py # machine learning pipeline
|- classifier.pkl # saved model

README.md

There are three components I completed for this project.

A Python script, process_data.py, writes a data cleaning pipeline that:

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

A jupyter notebook ETL Pipeline Preparation was used to do EDA to prepare the process_data.py python script.

A Python script, train_classifier.py, writes 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
• Uses a custom Transformer Class to create new features
• Trains and tunes a model using GridSearchCV
• Outputs results on the test set
• Exports the final model as a pickle file

A jupyter notebook ML Pipeline Preparation was used to do EDA to prepare the train_classifier.py python script.

The project includes a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data. The outputs are shown below:

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 process_data.py disaster_messages.csv disaster_categories.csv DisasterResponse.db
   • To run ML pipeline that trains classifier and saves python train_classifier.py ../data/DisasterResponse.db classifier.pkl

2. Run the following command in the app's directory to run your web app. python run.py

3. Open a new terminal, leaving "run.py" running, and input. env|grep WORK

4. Go to Enter: http://SPACEDID-3001.udacity-student-workspaces.com

If the server is running the project can be viewd here: http://view6914b2f4-3001.udacity-student-workspaces.com

Thanks to Udacity for starter code for the web app.