Python with R Studio

RStudio Connect helps teams of all sizes operationalize their data science work, and provides a single point of access to data products for decision makers. In this release, we have emphasized features that will help address maturing DevOps requirements within organizations seeking to deploy and scale data science. This release of RStudio Connect builds on the existing Server API, making experimental endpoints officially supported and introducing a brand new slate of API improvements based on feedback we’ve received from the community.

RStudio Connect helps teams of all sizes operationalize their data science work, and provides a single point of access to data products for decision makers. In this release, we have emphasized features that will help address maturing DevOps requirements within organizations seeking to deploy and scale data science. This release of RStudio Connect builds on the existing Server API, making experimental endpoints officially supported and introducing a brand new slate of API improvements based on feedback we’ve received from the community.

A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column. ... The data stored in a data frame can be of numeric, factor or character type. Each column should contain same number of data items.

If you are importing data into Python then you must be aware of Data Frames. A DataFrame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Subsetting a data frame is the process of selecting a set of desired rows and columns from the data frame.

One essential feature offered by Pandas is its high-performance, in-memory join and merge operations.

The most important first step in any machine learning project is to obtain good quality data. As a data scientist, you will often have to use a variety of different methods to extract data sets. You might be using publically available data, data available via an API, data found in a database or in many cases a combination of these methods. In the following post, I am going to give a brief introduction to three different methods in python for extracting data. For the purpose of this post, I am going to be covering how to extract data whilst working in a Jupyter Notebook. I previously covered how to use some of these methods from the command line in an earlier

An xts object extends the S3 class zoo from the package of the same name. Similar to zoo objects, xts objects must have an ordered index. While zoo indexes cannot contain duplicate values, xts objects have optionally supported duplicate index elements since version 0.5-0.

Machine Learning, as the name suggests, is the science of programming a computer by which they are able to learn from different kinds of data. A more general definition given by Arthur Samuel is – “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” They are typically used to solve various types of life problems.

Decision Tree is one of the most powerful and popular algorithm. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.

Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, input costs, and utility. Decision-tree algorithm falls under the category of supervised learning algorithms. It works for both continuous as well as categorical output variables.

Every decision tree has high variance, but when we combine all of them together in parallel then the resultant variance is low as each decision tree gets perfectly trained on that particular sample data and hence the output doesnt depend on one decision tree but multiple decision trees.

Pre-requisite: Getting started with machine learning scikit-learn is an open-source Python library that implements a range of machine learning, pre-processing, cross-validation, and visualization algorithms using a unified interface.