real es-ta(s)te

it made me ‘think’ differently about restaurants after listening to this updated Planet Money podcas:

https://www.npr.org/2024/03/27/1197958536/roni-mazumdar-unapologetic-foods-adda-semma-new-york

i never thougtt about it like ‘renting a table, psychological design factors, and the metric of guest spend per minute’…

words: on linguistics, computing, data analysis, and trivia

i was not really into Text Analytics and Natural Language Processing (NLP). but recently, i’ve been asked by wife if i could do some Sentiment Analysis on Discussion Forum posts for some online learners. – i said i would try to build a workable model given i’ve been dabbling in data analysis recently, have a background in computing, and have always been ‘interested’ in words.

i was not really into Text Analytics and Natural Language Processing (NLP). but recently, i’ve been asked by wife if i could do some Sentiment Analysis on Discussion Forum posts for some online learners. – i said i would try to build a workable model given i’ve been dabbling in data analysis recently, have a background in computing, and have always been ‘interested’ in words.

the thing is: i play an app called HQ Trivia ‘regularly’ and have never won – at most getting 11 out of 12 questions. they recently reintroduced Words on Wednesdays (Thursdays here) ‘technically’ i’ve got a better chance of winning this as i know more answers but my disability has resulted in me ‘typing’ even so slower so i can never tap all the letters in time or mishit the ‘smaller on-screen touch-screen keyboard’ to solve the puzzle.

the ‘confluence’ of these fields make it interesting to me…

the walk: on endurance and motivation

i told one of my neurophysios i needed to “rest”/stop after 20 minutes of walking. bless her heart, she “estimated” my “effort” was equivalent to about three hours of walking for an able-bodied individual (i’m guessing it’s probably “closer”to about two hours). my daily exercises have increased it from about 10 (don’t quote me on that) to 20 minutes but what she said “shook” me: if i “lost” five minutes every two years: i’d be only able to manage five minutes in six years!

i asked for a “practical target” (as that’s seem to have worked for me in the past). she suggested that 60 minutes she thinks would cover most activities. using my “logic” to project out using a linear function (which is unrealistic since i’m not a robot and will tire): this is about six hours to another person. nonetheless, i need to work on building my endurance to “boost” my current capabilities.

like one of my neurophysios say, we need to work on what we can to prolong my “independence” for as long as possible. i have begun “slowly” by trying to remain standing whilst watching TV. Also, i am currently noting my speed/RPMs (like was suggested to me) and not just increase the duration on the stationary bike.

while motivation has been tied to “success” – it has recently been reiterated to me that it’s definition should be expanded to what’s personally important and not just the “narrow” criteria of what society dictates (as a migrant i’ve noticed some “variability” in certain aspects).

“transmogrification”: on (data) type conversion and Python

transmogrify is my favourite word i learned from the comic strip Calvin & Hobbes. i like it because there are sometimes muliple “layers”.

Python has some built-in functions to do some “basic” type conversion. however, i’ve learned “recently” that sometimes additional conversion is required to “prove mastery” (aside from logic) so my next discussions should involve these to be more pragmatic.

“unexplored” frontiers: on Exploratory Data Analysis using Python

generally, Exploratory Data Analysis (EDA) is done to answer a question. as i never practised statistics, it is not that “refined” but, hopefully, has the benefit of applied understanding from an “outsider” – it took me awhile to try and prepare this for a broader audience. that said, i’m always open to make the content more understandable to the “layperson”.

here’s the link to my updated GitHub repository:

https://github.com/LinsAbadia/Python/blob/master/Analyses/EDA.ipynb

learning to learn: on supervised and unsupervised learning in machine learning with Python

initially, i just planned on posting a “short” “blurb” on my blog and GitHub Python page as there seemed to me to be a “virtual triangle” among machine learning, statistics, and data visualisation. i’m still likely to make a brief GitHub “file” but upon serious reflection this post may not be a “cursory” post.

it took me awhile to come up with this post because i was partly busy with an online machine learning course, and, frankly, didn’t know what to write – and i’m finding it difficult to figure out how to do it – it didn’t help that there was a “time-consuming” upgrade of the Jupyter notebook environment that i use to store my ipynb files online.

my last experience started me thinking on how i learn- i still need to reflect more on it. i’ve done “ok” academically but i’ve discovered i can understand better if “alternatives” are provided for me to choose from. programming is, essentially, divergent: that is, sometimes there is more than a single way to arrive at an “acceptable solution”. why can’t “formal” education be that way? i know that the human brain can be easily overloaded by many things but perhaps offering a few choices might result in more students understanding. i’m realistic and pragmatic enough to understand that most teachers are overworked (at least those that care about the development of others) and that maybe there needs to be a more “active” open-source community like coding: sharing can make lighter work.

here is my initial attempt at my updated repository:

https://github.com/LinsAbadia/Python/blob/master/Machine%20Learning/Learning.ipynb

centre of attention: on the “3ms” in statistics and the “fundamental” descriptive ones in Python

i’m not a statistician so kindly bear with my “crudeness”. initially, i just planned on discussing the “3ms”: Mean, Median & Mode. however, aside from these appearing “too short” and after what the describe method returns, it seemed more sensible to cover all the outputs.

as a former educator, i’m open to content being improved : “iteration” is often necessary in endeavouring to present something simpler – so if you have an idea on how to do this “better”, kindly let me know.

here’s the updated GitHub repository:

https://github.com/LinsAbadia/Python/blob/master/Statistics/Descriptive.ipynb

serve and protect: on sepals of flowers

i’m not a botanist so this is unfamiliar to me – so naturally i googled it.

“serendipitously”, i ran across this “foreign” term in one of my data science courses in learning about a computer language. they are usually green and are the leaf-like vestiges that “protect” the petals of a flower in bud form and act as supports in the blooming process.

same difference: on red and green colour blindness

i’m currently taking a visualisation course in Python and it has reminded me of red and green colour blindness: both hues appear similar to them.

while they are still granted driver’s licenses as a “strong” convention for traffic lights exist, the position and not just the colour convey information.

this made me think of truly inclusive designs: where a “best effort” is placed that a design is accessible by default (or a “reasonable” alternative or accomodation is provided). this is “good” to know since coming up with a “universal” design can be “problematic” (as more effort can be required) but in media without guidelines this can invaluable.

lies, damn lies, and statistics: on “deceit” with Information Graphics

i was so hung up on words that i “overlooked” visualisations can deceive audiences. i’ve been recently exposed to the works of Edward Tufte and Alberto Cairo on Information Graphics (commonly known by its portmanteau, Infographics). Aside from the important role it can play in emphasising statistics, it also has the power to mislead “consumers” of the information (whether intentional or not). The main point is that they need to be designed carefully and not simply thrown in to break the “monotony” of words or “pretty” things up – they must only be included to serve a particular purpose.

here are a few guidelines to help make the figure you generate “better”:

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833