gastrodon
feature-engineering-tutorials
gastrodon | feature-engineering-tutorials | |
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2 | 1 | |
131 | 266 | |
- | 1.1% | |
0.0 | 0.0 | |
about 2 years ago | about 1 month ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU Affero General Public License v3.0 |
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gastrodon
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LinkedDataHub: The Knowledge Graph Notebook
This package was designed to solve more problems than it creates
https://github.com/paulhoule/gastrodon
Overall I think of graph visualization as a problem, in particularly there are some people who just don't see that hairballs are incomprehensible
https://cambridge-intelligence.com/how-to-fix-hairballs/
- Gastrodon: Put RDF data on your fingerips with Jupyter, Pandas and Sphinx
feature-engineering-tutorials
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How to balance multiple time series data?
I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.
What are some alternatives?
LinkedDataHub - The low-code Knowledge Graph application platform. Apache license.
jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
graph-notebook - Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL.
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
py - Repository to store sample python programs for python learning
dtreeviz - A python library for decision tree visualization and model interpretation.
kgtk - Knowledge Graph Toolkit
ydata-quality - Data Quality assessment with one line of code
notion-auto-pull - Bash script to automatically download a notion workspace
PRML - PRML algorithms implemented in Python
datahub - The Metadata Platform for your Data Stack
desbordante-core - Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.