Advantages of using R statistical software for predictive modeling

By Indra Giri & Priya Chetty on April 18, 2017

Predictive modeling is data-driven, induction-based modeling that is continuously used by big sized companies to gain useful insights into trends and risks budding in the future. The modeling on the basis of data extraction, cleansing and analysis help in predicting the value of a target variable (Fortuny, Martens, & Provost, 2013). Most of the analytical software developed is used to efficiently understand how things move for an organisation as per trends indicated by a relevant factor. One of the software that helps in prediction is R, summarization and estimation of the target variable with respect to different factors (Varian, 2014). The software holds a wide scope to develop predictive models.

Easy user interface

R is text-based programming by entering commands at the prompt and getting executed one by one. It is continuously evolving to create a more graphical interface where code editors interact with the package installed and present an image of the command through the interface (Valero-Mora & Ledesma, 2012).  Also, the development of R Studio, a code editor that interfaces with R for Windows, macOS and Linux platforms have become popular.  Kilburn (2015) cited that R studio is commercial software that is built on the basis of R and provides additional features with respect to predictive modeling, data analysis and others.

Using R for predictive modelling
The user interface of R Studio

The picture above represents four sections in R Studio. Firstly, the script section is the one where the data is imported. Secondly the next section, the R environment shows the number of variables present in the given set of data. Next, the R console is where all the commands run and lastly, the graphical output display as per commands run in the console.

There are other user interfaces of R software such as Rattle, Red-R and Rkward which makes it accessible for its users to enjoy free services.

Availability of different types of predictive modeling techniques

The relevance of prediction differs from one software to another. R was primarily built to run complex data science algorithms but holds a good package for predictive analytics. It helps in data visualization through graphs and diagrammatic representations. Usually, there are 3 types of predictive modeling in R:

  • Propensity modeling,
  • clustering modeling,
  • collaborative filtering (Strickland, 2015).

Firstly, propensity Models make predictions about customers’ future behaviour with a firm. Secondly, clustering modeling is used for customer segmentation and classification into different groups. Lastly, collaborative filtering is about implementing recommendations based on user feedback. It allows the development of User-User and Item-Item collaborative filtering algorithms.

Weather forecasting using predictive modeling of R
Weather forecasting using predictive modeling of R

Since the time of its inception, R software is evolving and trying to make it easier for users to predict their models. In order to see the response of analytical models, it is better to link them directly to the marketing execution systems.

Companies using R for predicting consumer behaviour

In conclusion, companies generating huge databases try to predict customers’ behaviour through statistical analysis and knowledge. Smith (2014) argued that use of R in marketing data analysis is becoming increasingly common as per customers’ habits and backgrounds.

Furthermore, financial and insurance industries are lead users of advanced statistical analysis where they develop new trading, pricing and optimization strategies (Mcneil, Martinez-miranda, Engelhardt, & Shanahan, 2013). In addition,  R also plays a strategic role in weather forecasting, detection of changes in climate, and estimates of war casualties in volatile regions (Fraley, Raftery, & Gneiting, 2011).

References

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