Posts Tagged ‘ Models ’

The get-rich-quick scheme of the English

March 31, 2017
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The get-rich-quick scheme of the English

The World Economic Forum published this chart: The "EF EPI Score" is a measure of English proficiency. So the evidence is clear as day: "Better English and Income Go Hand in Hand," as their headline blares. Last time I was in the New York subway, the panhandler spoke good English. What's a blogger to do? I pulled out the EPI scores from the EPI report, and downloaded the Gross National…

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Confused by machines, or spooked by the machine-makers

March 29, 2017
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This New York Times article draws attention to real trends in the financial investments industry but gets completely lost in the smoke around those pushing "machines" and "data". The trend most concerning to the investments industry is the sustained, large-scale outflow of money from "actively-managed" funds, mutual funds being the biggest category of such. The industry makes loads of money from management fees by promoting the idea that investors are…

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Reading Everything is Obvious by Duncan Watts

February 15, 2017
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Reading Everything is Obvious by Duncan Watts

In his book, Everything is Obvious (Once You Know the Answer): Why Common Sense Fails, Duncan Watts, a professor of sociology at Columbia, imparts urgent lessons that are as relevant to his students as to self-proclaimed data scientists. It takes only nominal effort to generate narrative structures that retrace the past, Watts contends, but developing lasting theory that produces valid predictions requires much more effort than common sense. Watts’s is…

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Deep thinking about your data

February 3, 2017
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Deep thinking about your data

In the on-going series of posts about the IMDB dataset, from Kaggle, I have so far looked at several of the scraped variables, including the number of faces on movie posters (1, 2), plot keywords (3), and movie rating by title year (4). In this post, I tackle the variables resulting from a data merge between IMDB and Facebook. These columns have names like "Director Facebook Likes", "Actor 1 Facebook…

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Pre-processing data is not just about correcting errors

January 30, 2017
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Pre-processing data is not just about correcting errors

Exploration of IMDB rating data, by Kaiser Fung, founder of Principal Analytics Prep

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Apparently Hollywood does not recycle action-movie plots. The data said so, so it must be right

January 25, 2017
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Apparently Hollywood does not recycle action-movie plots. The data said so, so it must be right

Today I continue to explore the movie dataset, found on Kaggle. To catch up with previous work, see the blog posts 1 and 2. One of the students came up with an interesting problem. Among the genre of action movies, are there particular plot elements that are correlated with box office? This problem is solvable because the dataset contains a variable called "plot keywords" lifted from IMDB. Plot keywords are…

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Counting is hard, especially when you don’t have theories

January 19, 2017
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Counting is hard, especially when you don’t have theories

Exploring the data about movies, uncovering data issues

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Good models + Bad data = Bad analysis

January 18, 2017
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Good models + Bad data = Bad analysis

Example showing how to diagnose bad data in data science models

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Inspired by water leaks

December 19, 2016
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Inspired by water leaks

For me, 2016 is a year of water leaks. I was forced to move apartments during the summer. (Blame my old landlord for the lower frequency of posts this year!) That old apartment was overrun by water issues. In the past four years, there were two big leaks in addition to annual visible "seepage" in the ceiling. The first big leak ruined my first night back from Hurricane Sandy-induced evacuation.…

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This election forecasting business

November 15, 2016
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This election forecasting business

If you live in the States, and particularly a blue state, in the last year or two, it has been drilled into your head that Hillary Clinton was the overwhelming favorite to win the Presidential election. On the day before the election, when all the major media outlets finalized their "election forecasting models," they unanimously pronounced Clinton the clear winner, with a probability of winning of 70% to 99%. One…

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