Kaggle Titanic Flask App

Deployment of a machine learning model trained on Kaggle's Titanic dataset as a Flask app hosted on Heroku.

Overview

Dataset: https://www.kaggle.com/c/titanic

Installation

1. Clone the repository

git clone https://github.com/henrylao/kaggle-titanic.git

2. Create the virtual environment in project root directory
   

Windows:

python -m venv c:\path\to\myenv

MacOS/Linux:

python -m venv path/to/project/myenv

3. Activate the virtual environment
   

Windows:

path\to\project\myenv\Scripts\activate

MacOS/Linux

source path/to/project/myvenv/activate

4. Install package dependencies
   

Run one of the following:

pip install -r requirements.txt

pip3 install -r requirements.txt

REST API Server Startup

1. Ensure installation of dependencies from the installation section and activation of the virtual environment.
2. Navigate to the server directory.

cd path/to/project/kaggle-titanic/

3. Run the server driver app.py
   

Windows/Linux

python app.py

MacOS

python3 app.py

Milestones

Model API + Data Pipelining:

• loading data / existing model binaries
• preprocess data

REST API:

• Complete creation of REST API endpoint for LGBM model
  • Create POST method for handling a request
  • Create a method of configuring the model to be loaded (Ex. dzone, oos, etc)
• Complete migration of model application to be deployed using Docker
• Complete method for persisting configurations of models deployed
• Completed refactoring of codebase for OOP
• Complete testing for operating systems:
  • Windows 10
  • Linux
  • MacOS
• Refactor server.driver.py to be a controller stored in the server.controller package

Website

• Support for displaying different singular models
• Comparison of pycaret models vs self-configured
• Add footer routing to internet accounts + contact info

Documentation

• Model and data used
• Website design docs

Stretch

• Add