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README.md

Titanic Survival

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Micro service and web app created for a Meetup demo.

This repo contains everything needed to build a machine learning model on the Kaggle Titanic dataset and implement that model as aweb app / API using AWS API Gateway and Lambda.

The deployment process is performed using Zappa.

Use

Web App:

https://hvom0tv9l8.execute-api.us-east-1.amazonaws.com/dev/

API:

Make a POST request to the /titanic resource with the following form-data:

    Pclass - Cabin Class - [1, 2, 3]
    Sex - [0: female, 1: male]
    Age - <Positive Integer>
    SibSp - <Positive Integer>
    Parch - <Positive Integer>
    Fare - <Float>
    Embarked - [0: Southampton, 1: Cherbourg, 2: Queenstown]
    Alone - [0: Not Alone, 1: Alone]

Definitions:

  • SibSp - Number of Siblings or Spouses Aboard
  • Parch - Number of Parents or Children Aboard
  • Fare - Amount Paid for Ticket
  • Embarked - Embarked Location

Development

If you'd like to develop on this, the initial workflow was using the environment.yml file to describe a Conda environment and build the requirements.txt file to build the virtualenv from there.

Note: The reason for two different environments is a combination of development ease and Zapp (at the time of making this) doesn't support conda environments.

Environments

Conda

To build the conda environment use the following from the root directory:

conda env update

To extract the dependencies from the conda environment use the following from the root directory (Only if they need to be updated):

make update-requirements

Virtualenv

To build the virtualenv for Zappa deployment (or development if you choose) use the following:

make init

Running App Locally

The web app and api can be run locally using the flask server. The following can be used to start, stop and restart the flask server, respectively:

make start, make stop, make restart

Note: If for any reason while attempting to start the flask server you receive an error stating that one is already running or the port is in use (and make stop doesn't work), you can identify the running process using make find-pid to find the running process for manual termination.

Testing

To run the tests, execute the following from the root directory:

make test

Model Training

To execute the model training script, use the following from the root directory (assuming the virtualenv has been created):

make train

Upon completion, the script will output a model to the models/ directory called titanic_model.pkl

Deployment

To deploy the wap/api to the web, first ensure you've setup Zappa and then run the following to deploy, update and destroy the stack, respectively:

make deploy, make redeploy, make remove

If you should see any errors, or are curious to view the AWS logs, Zappa allows you to do this and can be done by using the following from the root directory:

make logs

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