Logistic regression in R, also popularly referred to as ‘R Logit Model’, is often regarded as the best technique to develop any advanced machine learning algorithm for predictive analytics and semi-supervised / supervised modeling. In the years of training with Logit models, we have understood one thing – every machine learning algorithm works efficiently only within established criteria associated with dependent and independent variables. These are expanded in Logistic Regression in R with GANs, Decision Trees, and so on.
In our last article on Logit Modelling, we covered the basic definition of this technique along with the various applications brought from the real world scenario.
In this article, we will go a step further and discuss how R based Logistic Regression can be used to transform the business world’s top domains.
One of the biggest outcomes of using the R Logit Model has been applied to advancing techniques in text processing and analysis. This market is popularly referred to as “Text Analytics.” By 2025, the text analytics market would grow to a $17 Billion industry, employing millions of business analysis professionals, IT and AI engineers.
Every day we produced tons of content and a large volume of this content is in the form of written text. Other common content types are audio, video, text, and emojis / GIFs. Text is static content and hence, it is hard to truly evaluate its impact on the reader’s behavior and how it influences sentiments. Thus, we apply R with Logistic Regression models.
Regression models for text analytics are used to measure the impact of content shared via emails, SMS, blogs, tweets, social media posts, and product descriptions and reviews. All this information is mined and analyzed using text mining, content discovery, and NLP techniques. Automated content tracking and mining tools are available in the market that is capable of picking keywords, their frequency in an article or platform (like Facebook or Twitter), and feelings analysis on reader’s behavior to drive better engagement across channels. Booking reviews, product rankings, and other such order-based e-commerce interactions are very well understood using Logit Models.
“In general, we can plainly say that R programming language is the backbone of text analytics industry as more than 75 percent of all text processing and analysis projects are carried out on R.”
Advanced R based Regression models are used by Facebook and Apple to track malicious content posted on and shared through their platforms. Content related to terrorism, hate, body shaming, threats, and other such mal-intent content is filtered out as soon as detected by text analytics platforms. This keeps the channels clean and safe for all age groups.
Popular text analytics platforms include SAS, OpenText, Knime, Lexalytics, Google, and Amazon Comprehend.
Have you ever wondered how Google is able to detect what you are going to types in the “search” bar? How YouTube always shows up a video thumbnail link on “How to clean your car?” as soon as you walked out of a car showroom? Or, a dating app throwing up profiles of people who are close to your neighborhood or share similar interests as that mentioned by you in the app? All these work more or less on the same collaborative filtering and regression models – called Recommendation System. Now, these are either working on memory based or supervision based techniques, and hence, enter R programming language for AI ML training.
The Machine Learning Recommendation system (MLRS) is the biggest money earner for data scientists in the R domain today. A majority of MLRS use Logit Model in R that offers relevant and contextual suggestions to the subscribers online based on their previous interaction with the software and also based on content-discovery systems.
If you are planning to develop an MLRS for a movie screening app, you would have to frame your project around techniques related to Generalized Linear Model (GLM), Mixed Logit, Multinomial Probit, and Ordered Logit.
Now, if you understand Text analysis and MLRS, you can direct your efforts in designing powerful software applications for patient monitoring systems, shopping experience management, contact center automation, RPA, and ML-based Call mining based on R Logistic Regression techniques.