feature-engineering-tutorials
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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.
jupytemplate
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how can I create a startup notebook in jupyterlab preloaded with some imports and other code?
This article from TowardsDataScience covers the basic extension for templates.
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[P] Jupiter Notebook templating for PyCharm
jupytemplate: This required installing an jupyter notebook extension and didn’t support chaining templates (as far as I could tell).
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!
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
BestPractices - Things that you should (and should not) do in your Materials Informatics research.
dtreeviz - A python library for decision tree visualization and model interpretation.
pycharm-jupyter-templates - Create templates for jupyter notebooks created through PyCharm
ydata-quality - Data Quality assessment with one line of code
jupyter-memgraph-tutorials - Learn to use Memgraph and GQLAlchemy quickly with the help of Jupyter Notebooks
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.