intro-to-python
feature-engineering-tutorials
| intro-to-python | feature-engineering-tutorials | |
|---|---|---|
| 1 | 1 | |
| 871 | 266 | |
| - | 1.1% | |
| 0.0 | 0.0 | |
| over 3 years ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | GNU Affero General Public License v3.0 |
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intro-to-python
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Show HN: Intro to Python and Programming for non-CS majors (revisited)
Hi there,
I am the author of this Show HN post: https://news.ycombinator.com/item?id=22669084
Back then, I released the materials for my Intro to Python course "to the world". GitHub repo: https://github.com/webartifex/intro-to-python
I incorporated many of the constructive criticism and am currently recording a video lecture series on YouTube: https://www.youtube.com/playlist?list=PL-2JV1G3J10kRUPgP7EwLhyeN5lOZW2kH
I guess that a lot of people without a CS background would find these resources valuable and am open for further feedback.
If you have any "non-tech" friends who want to learn to code, please feel free to direct them to my course.
Stay healthy everybody!
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?
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
ipp - An intro to Python & programming for wanna-be data scientists [Moved to: https://github.com/webartifex/intro-to-python]
dtreeviz - A python library for decision tree visualization and model interpretation.
python-training - Python training for business analysts and traders
ydata-quality - Data Quality assessment with one line of code
z3_tutorial - Jupyter notebooks for tutorial on the Z3 SMT solver
gastrodon - Visualize RDF data in Jupyter with Pandas
JustEnoughScalaForSpark - A tutorial on the most important features and idioms of Scala that you need to use Spark's Scala APIs.
PRML - PRML algorithms implemented in Python
examples - Datapane Examples
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.