v.5.1
Maintenance Release
Exciting new capabilities for Named Entity Recognition and Association Rules Analysis.
Changes for issues #29 and #34.
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.
- Named Entity Recognition : Implemented using spaCy, an excellent Natural Language Processing library that comes with pre-trained Neural Networks. This SSE allows you to use spaCy's models for NER or retrain them with your data for even better results.
- Association rules : Implemented using Efficient-Apriori. Association Rules Analysis is a data mining technique to uncover how items are associated to each other. This technique is best known for Market Basket Analysis, but can be used more generally for finding interesting associations between sets of items that occur together, for example, in a transaction, a paragraph, or a diagnosis.
- 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.
For more information refer to the usage section.
Change Log v.5.1:
- Additional parameters for Prophet as listed in updated Prophet.md
- Fix for machine learning when none of the features require scaling
- New docker image published
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.