elliott34

But if you're setting up bokeh server for the first time...bring a friend and some coffee

Correct. That's why those businesses get the enterprise version and it's all hosted behind a firewall.

plotly is awesome. A lot of companies spending $50k + a year on BI software can do better (and save a ton of money) by throwing plotly iframes on some simple static webpage and update them at any interval using cron jobs and python.

Does the author have something backwards or do I?

P(RateOfSuccess|Successes) = Beta(α,β)

"In Bayesian terms, C3PO's estimate of the true rate of success given observed data is referred to as the likelihood."

BUT we know likelihood(rateofsuccess|data) = probability(data|rate of success).

I am confused.

It's only CA at this point.

Car insurance for the major players in the U.S. has historically been about capturing users in the funnel, and making pricing information extremely opaque. This is very different from how the UK operates (see https://www.google.co.uk/compare/).

Price transparency like this is the death knell for the likes of geico, state farm...etc.

Checkout BigML.com, it has a ton of visualization

As far as creating a model and deploying it to production, I cannot think of anything simpler and more wonderful to use than bigml. Outputs the decision trees /randomforests (via python api) in actual textual code. Hook that up to a Flask API and you've got your predictive web service.

0xdata.com is another one for more r/python users, can output gradient boosting models to straight up java objects (although that feature is pricey last time I looked into it), which of course is java so it's going to be pretty fast.

Really feeling for everyone out there still using PMML...

This is fantastically well written but goes over my head in a major way. Where can I read about the 101 version of thi article

This is crazy and awesome. Can't wait to see what happens.

Like most apartment/condo city dwellers I can't receive packages unless I am home.

I would pay 30/month for some kind of external lockbox or some kind of capability to order anything I wanted to that box (outside my apartment, or would glady let amazon or google inside my place). Also , all/any pick ups or returns would happen from this drop off box.

The psychological benefit of paying off my student loans early was immense. While mathematically illogical and not in my best interest, I sleep better at night knowing that if I wanted to drop everything I was doing and do something else, I wouldn't have to worry about loans hanging over my head.

Now if we can only get sublime text 2 within an ipython notebook on the cloud.....

For me, R was my first language, and then I learned Python, and beautiful things like list comprehensions, and it just clicks with my brain a bit more.

In pandas, a group by operation is beautiful

dataset2=dataset1.groupby([stuff], as_index=False).mean()

Same with pivot table...

When I did this with dplyr2 my work flow would be a few more steps, creating the "summarise" object and so forth and so on, which seems like more steps.

Great commentary. Encoding high-level categorical variables as unordered integers, or even frequency count maps, is pretty common practice, but really only valid (as the author says) in tree based methods. Given deep enough trees (or enough iterations in the boosting scenario), it is my anecdotal experience that trees are able to parse out relevant values. The problem with indicator variables in general is the enormous potential loss in computational time and increase in complexity. My models have hundreds of vehicle/car types, U.S. state, etc., and so this type of coding helps quite a bit in performance without sacrificing (and sometimes improving due to curse-of-dimensionality) model performance (IMHO YMMV)

I love pandas much more than R but GOD I love Rstudio. Such a great IDE. Rstudio server, actually. My equivalent is running ipython notebook on an ec2 instance, which....is fine, but is a lot of scrolling.

Excuse my ignorance here: so are these functional programming paradigms?

Definitely see what you're saying with the prepare_data1()...prepare_data(2). Maybe a more functional approach would be suitable. At the end of the day, data goes into a tunnel and comes out in a different form, into a machine learning model. So perhaps I should look at functional programming styles in python. Thanks!

Sure! I'd love to take a look. I've actually written a mysqldb library in python that makes "data objects"

For example mydata=dataobject(query, connectionstring()) mydata.query() # get data mydata.data # pandas data frame mydata.write_to_db() # write to db method

etc.

Sounds similar to what youre doing here, although I don't know Java...

Do you have any links to articles on the current benchmarks that you mention? Not being snide just curious to read more.

Considering enterprise BI software/platforms these days can costs upwards of 4k per month, this is roundoff error.

we've shifted to plot.ly for visualization and moved away from expensive BI tools. We set up python/pandas scripts on CRON to output realtime data to our local plot.ly web server, which makes a local copy of the data and updates the chart. You simply embed the chart as an iframe where ever want internally, and BOOM, you've got a real time chart (beautiful I may add).

www.plot.ly

OOOOOO awesome. Thank you!

I work in pandas 95% of my day doing data automation tasks, manipulating sql queries, and data-munging for machine learning. It is literally life changing for someone like me.

I used to program solely in R, but after discovering pandas I really have no need to go back to R. My project workflow consists of several IPython notebooks+pandas+sklearn.

Works extremely well in production, as in, on a flask web server, as well.

Why is that panda's problem? If you have scipy and numpy installed, it's just sudo pip install pandas....

Do you work from home?

R is an alien ray gun, full of crazy features but all the buttons are in a bizarre language and require 10 fingers

I have hashimotos and celiac disease. So there's n=1. The worst part about having celiac disease is that I do not have any symptoms whatsoever but am forced to eat this way so that ten years down the road, maybe kinda sorta, I wont get cancer. Also having to explain it to people sucks. Eating meat rice and potatoes is fine by me, explaining why I don't want a beer sucks.

This pairs well with the recent AirBNB fraud detection article. Airbnb has a lot of people to construct a robust machine learning architecture. For small start ups trying to make a recommendation engine, why not do sciencebox+ yhat cloud/enterprise? Especially for data science people that are don't have great dev skills.

does anyone know if Java can port a gradient boosting model from R

Venmo is the first app to actually change my life in a major way (beyond google maps, text messaging, the basics). It Is the single handedly most valuable app on my phone by 10 fold.

It basically removes all transaction costs involved with spending money and going out with friends.

So it's not just me! The stuff people do in Kaggle competitions is mind blowing. They do models within models within models, do weird interaction terms with their features and transform them with different functions, although most of that is to go from 94% accuracy to 97% accuracy...