Valentyn

Valentyn Lieshchuk Košice, Slovakia
I [відкрити контакти](див. вище в блоці «контактна інформація»)
Junior Data Scientist # [відкрити контакти](див. вище в блоці «контактна інформація»)

Profile
Curious and analytically minded junior data scientist with a strong foundation in machine
learning and data analysis. Skilled in Python, data preprocessing, and applying classical
ML algorithms to real-world problems. Developed practical experience with linear models,
Naive Bayes, Support Vector Machines, and Gradient Boosting during academic research
and personal projects. Flexible, quick to adapt to new technologies, and highly motivated
to continuously learn and explore modern data science tools and neural network methods.

Technical Skills
Languages Python, SQL, C# (basic)
Libraries pandas, NumPy, scikit-learn, matplotlib, seaborn
ML Linear Regression, Naive Bayes, SVM, Gradient Boosting, K-Means, Perceptron
Algorithms
Concepts Data preprocessing, model evaluation, feature engineering, train/test pipelines
Tools Jupyter Notebook, Visual Studio Code, Git, Azure SQL
Other Familiar with basic neural networks (TensorFlow / PyTorch fundamentals)

Education
2022 – 2025 Bachelor in Intelligent Systems, Technical University of Košice, Slovakia. Continuing
studies towards a Master’s degree in Intelligent Systems. Bachelor thesis topic: “Modeling
Student Knowledge and Skills” using Perceptron, SVM, Naive Bayes, and Gradient Boosting.

Projects
2024 Student Knowledge Modeling (Bachelor Thesis) — Linear Perceptron, SVM, Naive
Bayes, Gradient Boosting. Developed and compared several machine learning models to
predict students’ knowledge levels based on historical performance data.
2023 Diabetes Detection App — .NET MAUI, Python, Naive Bayes. Built a mobile application
for predicting early diabetes risk using a Naive Bayes model integrated with a .NET MAUI
frontend.
2023 House Price Prediction — Python, pandas, scikit-learn. Implemented linear regression
and feature engineering for real estate price forecasting based on open datasets.
2022 Customer Segmentation — K-Means, matplotlib, seaborn. Performed unsupervised
clustering analysis for customer segmentation using purchase behavior data visualization.

Languages

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 English A2–B1 (Intermediate)
Slovak B1–B2 (Upper Intermediate)
Ukrainian Native

Career Objective
Seeking a junior or internship-level position in Data Science or Machine Learning where I
can apply my analytical mindset, programming skills, and curiosity for data-driven problem
solving. Eager to learn from experienced professionals and contribute to real-world ML and
data analysis projects.

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