Photo by Nicolas Hoizey on Unsplash

by Josh Barua, Westlake High School, Austin, TX.

The Changing Nature of Competition

Not too long ago, competition in any product category was limited to a few well-known brands, with the rest being labeled as niche products. However, the scenario has changed dramatically in recent years; there are numerous products today, many of which have features that are comparable to those of their big-brand counterparts, and generally at much more favorable price points. Recommender systems have been developed to match products to consumer needs, which ease the burden of finding products that may not be the most popular or visible…

Photo by Morning Brew on Unsplash

Josh Barua, Westlake High School, Austin, TX.

Consumers face a huge challenge today in choosing from numerous alternatives available in any product category. I developed and tested a system that matches a shopper’s preferences regarding the features of a product to recommendations using online reviews. I also performed feature-level sentiment analysis to make sure that the recommended products have high customer satisfaction with preferred features. This recommender system is quite different from the most common approach, which takes a product such as a movie as input, and finds similar products as recommendations. …

Photo by visuals on Unsplash

Aadit Barua and Josh Barua, Westlake High School, Austin, TX.

The low down on 3rd November, 2020

What are the top issues on Twitter users’ minds for the 2020 U.S. presidential election? How do they associate the democratic nominee, former Vice President Biden, and President Trump with these issues? Is there any difference between the battleground states — Michigan, Pennsylvania and Wisconsin — and the rest of the country in terms of what is important to voters, and how they feel about the candidates? How does this election differ from the one in 2016? What should each candidate focus on between now and 3rd November? These are…

Photo by Kevin Grieve on Unsplash

Are word embeddings always the best choice?

If you can challenge a well-accepted view in data science with data, that’s pretty cool, right? After all, “in data we trust”, or so we profess! Word embeddings have caused a revolution in the world of natural language processing, as a result of which we are much closer to understanding the meaning and context of text and transcribed speech today. It is a world apart from the good old bag-of-words (BoW) models, which rely on frequencies of words under the unrealistic assumption that each word occurs independently of all others. The results have been nothing short of spectacular with word…

Josh Barua

Freshman (Fall 2021), UC Berkeley. Interested in business applications of NLP & ML. Writer for The Startup, DDI, Towards Data Science & Towards AI.

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