Predict the poverty of households in Costa Rica using automated feature engineering
Social programs have a difficult time determining that the right people are given enough aid. Using a dataset of Costa Rican household characteristics, we'd like to be able to predict the poverty of households.
The Tutorial notebook from this repository exists on Kaggle. If you would prefer to work in that environment, you can fork the existing kernel to use as a starting point.
- Automatically generate 2000 features
- Learn how to write your own primitive to be applied to the data
Running the tutorial
If you would like to work on Kaggle, the Tutorial notebook has been uploaded as a kernel. You can fork that notebook to use as a starting point. If you prefer to work locally:
Clone the repo
git clone https://github.com/Featuretools/predict-household-poverty.git
Install the requirements
pip install -r requirements.txt
You will also need to install graphviz for this demo. Please install graphviz according to the instructions in the Featuretools Documentation
Download the data
You can download the data from Kaggle or create a kernel and use Featuretools there. After downloading, save the CSV to a directory called
datain the root of this repository.
Run the Tutorial notebook
Featuretools is an open source project created by Feature Labs. To see the other open source projects we're working on visit Feature Labs Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.
Any questions can be directed to email@example.com