World Map Predictive Analysis

Our project, World Map Predictive Analysis, is a metric (GDP, population, etc) predictor for any given country for any given year. We plan on building a machine learning algorithm that can generate future yearly metrics using training data from 1956 - 2017. Our chosen metrics will be weighted into one easy to understand coefficient which will be used as a "heatmap" on our interactive website. We also plan to display this data for all countries in a GUI (website?) that is also interactive. Users will have the ability to manipulate the weights of the metrics they value more (ex. high population is important, thus more weight). The GUI will display the according coefficient depending on the inputted weights of the user, along with the specific predicted data for that year. The program will have two components: the machine learning aspect and the GUI/website aspect. The machine learning model will be responsible for using prior data to generate future data. The GUI will be responsible for displaying the outputs. The interactive user interface make changes to the model, which will then change the displayed metrics.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

None

Install

• Clone repository

$ git clone https://github.com/sd18fall/PredictiveCountryAnalytics.git PredictiveCountryAnalytics

• Open repository

$ cd PredictiveCountryAnalytics

To be continued

Using this project

Varying data used in project

This project was developed using World Map data for each country in the world. It is possible to choose other World Map data in the same format for each country to predict different data.

How to do this will be filled in at a later date.

GUI

• See website at ______________
• Using drop down menu, select variables that should be predicted and mapped
• Using sliders, vary the weight given to each variables
• Mouse over countries on the map for information

Contributing

This is a student project. Please reach out to us via email if you would like to contribute.

Versioning

Version 0.0.1

Authors

• Sara Ballantyne - Initial work
• Sampei Omichi - Initial work

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Franklin W. Olin College of Engineering - Software Design, Fall 2018