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Designing better algorithms: 5 case studies
In this article, using a few examples and solutions, I show that the "best" algorithm is many times not what data scientists or management think it is. As a result, too many times, misfit algorithms are implemented. Not that there are bad or simplistic. To the contrary, they are usually too complicated, but the biggest drawback is that they do not address the key problems. Sometimes they lack robustness, sometimes they are not properly maintained (for instance they rely on outdated lookup tables), sometimes they are unstable (they rely on a multi-million rule system), sometimes the data is not properly filtered or inaccurate, and sometimes they are based on poor metrics that are easy to manipulate by a third party seeking some advantage (for instance, click counts are easy to fake.) The solution usually consists in choosing a different approach and a very different, simple algorithm - or no algorithm at all in some cases. Click here to read more.
Price Optimisation Using Decision Tree (Regression Tree) - Machine Learning
The research was conducted to find out what price maximises profit without sacrificing the high demand for the product due to the price being too high nor sacrificing the margins on the product due to the price being too low. The goal is to experiment with different price levels for the same product in one market place and country to see how sales volumes change with prices and which volume level of products we can be sold for that optimal price range. As a data scientist it is my responsibility to identify the optimum prices of products so the items can be sold for maximum profit. Click here to read more.
Sentiment Analysis of Movie Reviews - Part 3: doc2vec
This is the last – for now – installment of my mini-series on sentiment analysis of the Stanford collection of IMDB reviews. So far, we’ve had a look at classical bag-of-words models and word vectors (word2vec). We saw that from the classifiers used, logistic regression performed best, be it in combination with bag-of-words or word2vec. Click here to read more.
The Case for a New “Final Frontier” in Data Analytics
This article has been contributed by Alain Louchez (Georgia Tech Research Institute)
The Internet of Things already integrates a new phase beyond prescriptive analytics.
There is no shortage of attention lately on the “Internet of Things”. As a case in point, see the “Developing Innovation and Growing the Internet of Things Act” or “DIGIT Act”, i.e., S. 2607, a bill introduced in the Senate on March 1, 2016 and amended on September 28, 2016, “to ensure appropriate spectrum planning and inter-agency coordination to support the Internet of Things” – A companion bill, H.R. 5117, was introduced in the House of Representatives on April 28, 2016. However, since there is no “internet” dedicated to “things”, it is fair to state that the Internet of Things does not exist as such. Click here to read more.
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