e-mail: dvkazakov @ gmail.com
(remove spaces on both sides of @)

Phone/WhatsApp: +7-916-909-7864

Telegram: @denis_v_kazakov


Skype: denis.v.kazakov



Study Projects

Pet projects completed before I got my first job in data science.


Natural language processing

Machine translation with transformers

Set phrase extraction from corpora

I proposed my own method using normalized pointwise mutual information

Google Translate detected!

"Google Translate detected!" is the battle cry of translators seeing that a translation was done by a computer rather than a human translator (implying that this is obvious and the translation is poor).

The purpose of this study project was to train a neural network to tell the difference between human and machine translation.


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Time series

Predicting unconventional oil and gas production

Study project with two parts:


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Multivariate regression

Multivariate regression when there are more targets than predictors


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Uplift modeling

Uplift modeling Ч predicting which customers will buy a product if and only if they receive an SMS, i.e. those who won't buy unless they receive an SMS.

Kaggle competition. Rank: 18th place out of 177 contestants.


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Checking Zipf's law validity

According to Zipf's law, the most frequent word in a language or a large body of texts will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, etc.

Project purpose: check Zipf's law validity on English and Russian texts as well as the Frequency Dictionary of the Russian language.

  • Data analysis with Pandas
  • Feature transformation to enable linear regression
  • Linear regression (Statsmodels)
  • Python class definition
  • Natural language processing, frequency estimation

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Dictionary conversion

App for technical translators compiling their own glossaries.

Skills: Python.

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