Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 1

Data Scientist with experience in statistical analysis and machine learning (scikit-learn, Pandas, NumPy). Background  in web development (full stack) with 10+ years of experience in relational database 
and web servers (CentOS with NGINX/Varnish/Apache/PHP/MySQL) management. Quality and results oriented. 
A team player who assumes responsibility and can work independently. Native Ukrainian speaker.
Bitcoin price prediction:
Tried to predict bitcoin stock price using its previous price time series.
Obtained data from Binance exchange for use in a time series prediction model.
Explored and enhanced the dataset through log and differencing transformations.
Created a hyperparameter-tuned models (ARIMA, LSTM) for bitcoin price through grid search.
Proved that forecasting the price of bitcoin stock can not be achieved using only its own previous price.  

Identifying pneumonia disease: 
Identified pneumonia disease on chest X-rays by image-based deep learning.
Downloaded images from Kaggle, explored and resampled data.
Built CNN models using grid search for hyperparameters tuning, including pretrained VGG19 models with data augmentation with 88% accuracy and 94% sensitivity.
Shows that this neural network has the potential to learn with a high degree of generalization.
Proved that the transfer learning approach can be used for the diagnosis of common human diseases.

Tanzanian water pumps status analysis: 
Tried to find ways to improve water pumps operation and consequently quality of life in Tanzania.
Collected water pumps data from Taarifa and the Tanzanian Ministry of Water.
Handling missing values in the dataset for continuous features with an iterative imputation strategy.
Modeled the data using strategies including Logistic Regression, Decision Tree, Random Forest, and XGBoost.
The random forest performed better than the ensemble method with accuracy 80,55% and 79.96% respectively.