In this demo I will show how to predict if the closing price of Apple, General Electric and Samsung Electronics is moving up or down. We do this with the help of a Random Forest Classifier. I tried to replicate the result from a research paper authored by Luckyson Khaidem, Snehanshu Read more…
In this blog we show how to forecast the closing price of AAPL using a LSTM RRN network. We use the open, closing, high and low rates and the volume of the current day as input in order to predict the subsequent closing price. This demo has been implemented in Read more…
A Long short-term memory (LSTM) network is a special type of a recurrent neural network (RNN). It remembers values over arbitrary time intervals which makes it suited to be used for forecasting of time series. I was struggling with the DL4J implementation of the LSTM to be used to forecast stock data and I Read more…
In this Blog we demonstrate how Investor can be used together with DL4j and Spark’s MLlib. In general the idea is to use the stock history data in order to predict an increase or decrease of the stock value with the help of Machine Learning. We will demonstrate how we can Read more…
The H2O MOJO and POJO models can be deployed to production w/o the need of any access to a running H2O or Spark Instance. I am providing some examples using Jupyter with different BeakerX kernels. Groovy Scala Kotlin
H2O is a machine learning framework which has been implemented in Java and provides an API for Scala, Python and R. This framework has the following unique features: – It has a interactive web GUI (Flow) so that you can work w/o any programming – The generated trained models can Read more…
If you are interested in Machine Learning in the JVM you should not forget about the good old Weka. It has basically been desinged to be used by a Swing GUI but it can also be used as an API. In terms of documentation I can recommend this manual and Read more…
Each machine learning framework has its own basic data model and it is part of the ‘data preparation’ to convert the existing data to the required input format of the specific framework. Most of these frameworks also provide a direct access to some predefined data-sets (e.g iris, mnist etc) I Read more…
MLlib is Spark’s machine learning (ML) library. It’s goal is to make practical machine learning scalable and easy. I tried to make a complete step by step classification example using the Iris flower data set using the BeakerX Jupyter kernel which covers the following steps Setup Data Preparation Testing and Prediction Validation The example Read more…
Shogun is and open-source machine learning library that offers a wide range of efficient and unified machine learning methods. It is implemented in C++ and provides the necessary java integration so that it can be used in any language which is based on the JVM: – Java – Scala – Read more…