![]() ![]() The following Python code follows the same flow as what was shown in the R sample code in the first article, beginning with loading the data set into a panda in our Python environment. To install these packages run the following commands in a command line shell/window. ![]() ![]() ![]() These include scikit-Learn (the main machine learning package currently available), along with some of the more typical data processing packages encountered in data science projects. There are lots of reasons for this: Python is easy to learn, there are lots of new packages available for it (particularly for data science), it can be used to develop production applications, and it is easy to integrate into existing production systems.īefore you can start using Random Forest in Python, there are a number of packages/libraries you need to install. Python has become one of the most popular languages for machine learning over the past year. This will allow you to easily understand all components and to be able to migrate between the languages. The same data set used in the first article will be used in this article, for the Python language and Oracle 18cDatabase. This article builds upon what was covered in the first part, giving examples of building and using Random Forest models using Python and Oracle 18c Database. Examples of using Random Forest were given using the R language. In the first article, Random Forest was introduced, with details of how it works. This is the second part of a two-article series on using Random Forest in R, Python and SQL. ![]()
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