- PROJECT: Configure API pulls to various demographic (related) data sources, do some exploratory EDA, and trying to predict GDP using machine learning.
- PERSONAL: Get more confortable with ML and the project workflow.
- This project is not meant to actually predict GDP lol
- Predict t+1 GDP per Capita (quantitative)
- The data:
- large number of features
- small number of rows
get data.ipynb: download the datapoints for each country from WorldBank's API and export data to acsvpreprocess data.ipynb: format data and filter out some featuresmodel selection.ipynb: test various models' prediction error
- Part of ML is art, another part is just testing algos to see which works best and for what
- Two next projects:
- datasets for testing: downloaded data and synthetic data generation with varying types of data and distributions
- framework to plug in a model and test against various data types and distributions to evaluate strengths and limitations of models
- goal: have an easy to plug into framework for baseline testing models on datasets, which should be beneficial for future ML projects from a knowledge and model selection perspective