Žiga Vaupot's Blog

This is the 3rd part of the blog post series on Telco Churn Prediction, which I'm revisiting after 2+ years from my original blog post  Getting Started with Machine Learning in Oracle Data Visualization .  In my two previous posts in this series, I am talking about Data Analysis and Data Preparation . Both steps are mandatory steps before any machine learning is applied. My plan in this blog is to demonstrate how to create a machine learning model, how to improve it by setting parameters or simply by replacing an algorithm, and finally, how to create a project in Oracle Analytics in order to compare all created machine learning models among each other in order to decide which is the best model for my prediction. Creating a new Machine Learning model A new machine learning model can be created by using (again) Data Flows functionality in Oracle Analytics. This hasn't change much since my initial blog from two years ago. Basically, this is a 3-step process in which you need to:...

Data preparation in our example basically means bringing all four training datasets together (and new data datasets too).  Final result of this exercise should be a table with 5298 rows, one row per customer. The data should be ready for training the model, which includes performing a series of data transformations before we start eventually building the model. In my original post from 2+ years ago , I simply brought the four files together, did some minor transformations (for example Onehot transformation for Services file), but other transformations were not done. In this respect, I am revisiting this process with goal to automate the whole preparation cycle including model training and deployment. As title of this post suggest, we will look into data preparation  first. And we try to use the most of the Data Flow functionality. We have analysed data source files in the previous post  Telecom Churn Prediction Case Revisited: Data Analysis .  The starting point for...

Forword In the beginning of February, Oracle has announced the availability of Oracle Cloud Infrastructure Data Science Service. Very simplified, one could say that Oracle Data Science Service is yet another Jupyter notebook for writing machine learning script in Python. And they would be right. However, it is also more. Oracle has stated that its mission is to Bring together the right Infrastructure, Data Management, and Data Science Tools to make data science more collaborative, scalable, and powerful for every enterprise.  And indeed, with OCI Data Science, you get a fully managed platform that has been built to meet the needs of teams of a modern enterprise. It provides users and development teams a project driven collaborative environment, which enables teams to work together on an end-to-end modelling workflow with self-service resources and data access. OCI Data Science is using Jupyter notebooks to support the lates open source tools such as Python. Tensorflo...