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This project is under making and will adapt machine learning to stock market data. Right now several technologies are being evaluated.
- How to clean and transform the datasets
- Explicitly what features should be used
- Try several models, perhaps neural network?
- Choose best result
Main language: Python
- ETL process will be done in PySpark
- ML will be done in either Scikit-learn or spark-learn
Interesting articles within the topic
https://mapr.com/blog/tensorflow-mxnet-caffe-h2o-which-ml-best/ https://databricks.com/blog/2016/01/25/deep-learning-with-apache-spark-and-tensorflow.html
MIT License
Copyright (c) [2018] [Simon Thelin]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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