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Month: November 2020

The Golden Cockerel: A Russian Fairy Tale

There are some things that we humans go to any lengths to protect. Family? Definitely. Reputation? Yup. A wealthy and extensive Tsardom? Of cour– wait, what?! Okay, fine. Most of us don’t carry the fate of an entire Tsardom on our shoulders. Tsar Dadon, on the other hand? Yeah, I don’t envy that guy. In this hilarious Pushkin tale, Tsar Dadon is so desperate to protect his Tsardom that he actually goes so far as to buy a magic golden cockerel from a sketchy eunuch. Highly recommend against this, folks. Yes, the cockerel might (keyword: might) be able to protect the Tsardom, but at what cost? What will Tsar Dadon lose in the process? Will it be family? Reputation? A wealthy and extensive Tsardom? All of the above and more? Bargains always have a way of coming back to you. The question is: will the mighty Tsar Dadon end up with the short end of the stick? This time, the answer might surprise you – listen in to find out!

WEBSITE ILLUSTRATION DESCRIPTIONS: A selection of Ivan Bilibin’s original set and costume sketches for Nikolai Rimsky-Korsakov’s corresponding opera (of the same name). Super cool stuff. See them here: https://rheasslavicadventures.com/2020/11/19/the-golden-cockerel-a-russian-fairy-tale

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Combining Russian Studies + Machine Learning: Price Classification on Sberbank Russian Housing Market Data with Python

Last year, in my Advanced Topics in Computer Science class, we were tasked with designing the best system for supervised classification (a machine learning approach to separating data into representative regions) of a dataset of choice. I’d been eyeing the Sberbank Russian Housing Market dataset for quite a while leading up; this final project turned out to be the perfect place to explore it. 

Some context on the dataset: Sberbank, the largest bank in Russia, sponsored a Kaggle competition to predict house prices in Russia’s turbulent economy to help them give more accurate real estate price predictions to their customers. Their given dataset consists of 6000+ housing data points from around Russia, where each point is the sale of a house. There are 278 features associated with each point, including: preschool count nearby, distance from the metro, cafe count nearby, mosque count nearby, etc.

Through scikit-learn, a Python machine learning library, I experimented with each of the four classifiers we learned about – Naive Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM) – and varied parameters (tree depth, number of neighbors, etc.) along the way to identify the best classification method. Resulting confusion matrices (for both the reclassification and leave-one-out methods – the latter shows the system generalizes to unseen data, as stated by this Stackoverflow answer) are included and analyzed in the conclusions of my attached report. Note that due to the sky-high number of features (278, and for each of 6042 points), PCA plots and corresponding decision region visualizations would have taken days to render and were therefore excluded from the report.

I hope you enjoy the writeup, and I’d love to hear what you think – let me know in the comments!

Sberbank Housing Data Classification – Rhea Kapur
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My Favorite Russian Music!

Hey all! Happy Saturday afternoon. Our re-imposed state of quarantine has me once again turning to music – specifically, Russian music. While listening, I noticed that one of my Russian playlists on Spotify just exceeded 300 followers (yay!), so I thought I would write a short post to commemorate the occasion and share my favorites. Here are three Russian playlists I’ve curated over the past year, all representing different types of Russian music. The first reflects the late 20th century voice, from before the Western influence took over (this is the one with 327 followers!). And the last two playlists are instrumental, dedicated to Russian classical and jazz music. Here they are:

I’ll update this post later with some thoughts on the different genres, themes I see, cool musical aspects, and more, but for now I figured I’d leave you with an unswayed ear. Happy listening, and let me know what you think!

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