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Exploratory data analysis with PyCaret #311

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Feb 27, 2024
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Exploratory data analysis with PyCaret #311

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0811ab4
Exploratory data analysis with PyCaret
marijaselakovic Feb 20, 2024
01c3e98
few changes
marijaselakovic Feb 22, 2024
5def192
Update topic/timeseries/exploratory_data_analysis.ipynb
marijaselakovic Feb 22, 2024
fc45d60
Update topic/timeseries/exploratory_data_analysis.ipynb
marijaselakovic Feb 22, 2024
69753c7
Update topic/timeseries/exploratory_data_analysis.ipynb
marijaselakovic Feb 23, 2024
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Update topic/timeseries/exploratory_data_analysis.ipynb
marijaselakovic Feb 23, 2024
ae4189b
Update topic/timeseries/exploratory_data_analysis.ipynb
marijaselakovic Feb 23, 2024
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Update topic/timeseries/exploratory_data_analysis.ipynb
marijaselakovic Feb 23, 2024
1805a48
Full path to requirements.txt
marijaselakovic Feb 23, 2024
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Update topic/timeseries/exploratory_data_analysis.ipynb
marijaselakovic Feb 26, 2024
24a5d42
Update topic/timeseries/requirements.txt
marijaselakovic Feb 26, 2024
8df7157
Merge branch 'main' into marija/timeseries-analysis
marijaselakovic Feb 26, 2024
cece38c
time series decomposition notebook
marijaselakovic Feb 27, 2024
dbb39c8
Update topic/timeseries/time-series-decomposition.ipynb
marijaselakovic Feb 27, 2024
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Update to README
marijaselakovic Feb 27, 2024
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Merge branch 'main' into marija/timeseries-analysis
marijaselakovic Feb 27, 2024
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6 changes: 6 additions & 0 deletions topic/timeseries/README.md
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Expand Up @@ -28,5 +28,11 @@ repository, e.g. about machine learning, to see predictions and AutoML in action
window functions, interpolation of missing data, common table expressions,
moving averages, JOINs and the handling of JSON data.

- `exploratory_data_analysis.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](exploratory_data_analysis.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/timeseries/exploratory_data_analysis.ipynb)

This notebook explores how to access timeseries data from CrateDB via SQL,
and do the exploratory data analysis with PyCaret.

It also shows how you can generate various plots and charts for EDA, helping you understand data distributions, relationships between variables, and identify patterns.
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Time Series decomposition should be added to the readme file as well, incl. link to Colab.

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The last commit updates README file.


[CrateDB]: https://github.com/crate/crate
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