Data Scientist

	e-mail: dvkazakov @ gmail.com (remove spaces on both sides of @)	Phone/WhatsApp: +7-916-909-7864	Telegram: @denis_v_kazakov
GitHub	Skype: denis.v.kazakov		Русский

Study Projects

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

Contents:

Natural language processing

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.

Skills:

Data preparation and analysis with Pandas

Neural networks: model building, hyperparameter selection, evaluation of results (keras)

Statistical hypothesis testing

Power of statistical tests

Languages: Python, R

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Time series

Predicting unconventional oil and gas production

Study project with two parts:

Selecting an equation to describe production decline

Predicting production

Skills:

Exploratory data analysis
Visualization
Data preparation (Pandas)
Feature transformation
Linear regression
Curve fitting (Scypy)
Neural networks (keras, functional API)
Time series
Metric selection in line with the industry requirements
Domain knowledge (shale reserve development)

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Multivariate regression

Multivariate regression when there are more targets than predictors

Skills:

Gaining domain knowledge (laboratory analysis of rock samples)
Exploratory data analysis
Visualization
Data transformation
Industry-specific metric selection
Object-oriented programming
Regression analysis:

Linear regression
Regularization
K nearest neighbors
Random forest
Boosting (AdaBoost)

Hyperparameter optimization (Optuna)

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Uplift modeling

Uplift modeling — 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: 18^th place out of 177 contestants.

Skills:

Data preparation and analysis (pandas)

Feature selection

Uplift modeling (sklift library)

Gradient boosting (xgboost)

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Statistics

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.

Skills:

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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Python

Dictionary conversion

App for technical translators compiling their own glossaries.

Skills: Python.

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