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@nabeel-oz nabeel-oz released this 15 Oct 03:53
· 140 commits to master since this release
3.5
f7b1ce2

Feature Release

New functionality to perform k-fold cross validation.

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. In addition, models can be interpreted using Skater.
  • 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.3.5:

  • Added functionality for k-fold cross validation
  • New sample apps and documentation for k-fold cross validation
  • Updates for key driver analysis using Skater.

qlik-py-tools-3.5.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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