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@nabeel-oz nabeel-oz released this 14 Sep 06:12
· 154 commits to master since this release
3.2
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Feature Release

This release adds functionality for unsupervised machine learning using scikit-learn.

This release includes:

  • Supervised Machine Learning : Implemented using scikit-learn, the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik.
  • Unsupervised Machine Learning: Also implemented using scikit-learn. This provides capabilities for dimensionality reduction and clustering.
  • Clustering : Implemented using HDBSCAN, a high performance algorithm that is great for exploratory data analysis.
  • Time series forecasting : Implemented using Facebook Prophet, a modern library for easily generating good quality forecasts.
  • Seasonality and holiday analysis : Also using Facebook Prophet.
  • Linear correlations : Implemented using Pandas.

Change Log v.3.2:

  • Added capabilities for clustering and matrix decomposition/ dimensionality reduction.
  • Additional feature preparation strategies: count vectorization and term frequency, inverse document frequency (TF-IDF).
  • Bundling of responses to allow for larger message sizes.
  • Fixed and enhanced debug outputs

qlik-py-tools-3.2.zip

This zip archive only contains the files needed to deploy the SSE. To get the sample apps download the full source code above or get them from the docs.

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