Position: Data Analyst/science trainee
Category: Python
English level: upper
Years of experience: 0.5

Сoursework ‘Predicting the price of a house’
-	Brief description: The main task is to predict the price of a house based on various characteristics of houses. First, I did an exploratory analysis of the data. After processing the data, I tried a linear regression model with the default parameters and found an overfitting, so this model does not work for us. To reduce overfitting, I used L1, L2 regularization (ElasticNet). I also used cross-validation to select the optimal hyperparameters. As a result, I managed to improve the model's results. Then I tried other models such as polynomial regression, decision trees, random forests, and gradient boosting. The best models in my work comparing the rmse metric to the validation metric are ElasticNet, found by searching for hyperparameters, RandomForest, and GradientBoostingRegressor.

Covid 19 data exploration
- Brief description: "Used SQL Server to extract and analyze global COVID-19 data. Developed optimized queries to retrieve the necessary information and conducted data analysis using SQL queries. Visualized the analysis results using Tableau, creating interactive dashboards and charts to provide a visual representation of trends and patterns related to the COVID-19 pandemic. I also have a portfolio site(link) with these projects and a few others."
Passed a paid course Python online marathon from SoftServe.
Passed a paid course Python for Data Science from SoftServe.