notebook-examples
feature-engineering-tutorials
| notebook-examples | feature-engineering-tutorials | |
|---|---|---|
| 1 | 1 | |
| 166 | 266 | |
| 0.0% | 1.1% | |
| 4.2 | 0.0 | |
| about 1 year ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | GNU Affero General Public License v3.0 |
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notebook-examples
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Creating station model plots
I've found MetPy, but that would require a bit of training. ECMWF offers Magics examples, but it does not seem to be able to plot station models. Strangely, the program I was able to find that worked immediately was an old MSDOS program–Weathergraphix–linked to here. (I would be more than happy to use older MSDOS programs too, if any of you know of any others.)
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?
jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
dtreeviz - A python library for decision tree visualization and model interpretation.
ydata-quality - Data Quality assessment with one line of code
gastrodon - Visualize RDF data in Jupyter with Pandas
PRML - PRML algorithms implemented in Python
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.
FeatureHub - The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide
jupytemplate - Templates for jupyter notebooks
PyImpetus - PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features
pandas-profiling - Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling]
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.