Convolutional Neural Networks for Medical Images Diagnosis
Udemy
A creative and disciplined individual with a strong background in the arts, having graduated after seven years of study at an art school. I am passionate about continuous self-improvement, regularly attending industry conferences, seminars, and events related to the IT world. Outside of my professional interests, I enjoy strategic thinking through chess, as well as exploring the rich history of Ancient Egyptian mythology. I maintain an active lifestyle, regularly going to the gym, and bring a combination of creativity, problem-solving skills, and dedication to any project or team I am part of.
Used technologies: Android Studio | Flutter | Kotlin | Tensorflow - TFLite | Python
The main idea of the project was to create a mobile application that would help determine which bin to throw garbage in. As well as spread awareness of the importance of proper garbage sorting and caring for the environment. The program allows to take a photo and, using a trained classifier, identify which trash can to throw the object in. In addition, the application provides information about trash cans, what you can/cannot throw in them. There is also a quiz available to help you test your knowledge.
Used technologies: Python | Jupyter | VS Code
Used libraries: pandas | numpy | matplotlib | cv2 | random | time | PIL | Tensorflow | Keras | sklearn | splitfolders | os | seaborn | imutils
This project aimed to investigate the effectiveness of various CNNs in detecting a rare brain pathology - Fahr's disease, using CT images. As part of the study, a new unique dataset was created, which will soon be available on Kaggle.
Used technologies: Power BI
This project aimed to show ability to create Power BI reports.
Visualization is the final stage of the project, the purpose of which was to carry out comprehensive work on the data
(SQL Normalization, Warehouse Architecture and ETL Process (SSIS), Multi-Level Reporting).
During data processing, errors present in the original data set were identified and eliminated:
• Missing data correction: Empty store names (NULL) were replaced
with the value "Unknown Store" using advanced JOINs with support for unspecified values.
• Financial correction: Anomalies in the form of negative FinalAmount values
(discounts higher than price) were detected. Business logic was implemented to correct these
values to 0, ensuring the reliability of financial reports.
• Integration: Foreign keys (FKs) were defined in the warehouse, allowing for the correct
joining of tables and the creation of a relational star model.
Used technologies: Python | Jupyter | VS Code
Used libraries: pandas | numpy | matplotlib | statsmodels
This project aimed to show how the government's "Safe Credit 2%" programme has affected housing prices in Poland. The analysis used data on housing prices from 2010 to 2022 in the 5 largest cities of Poland, taken from the GUS website, as well as data for 2023 on housing prices from Kaggle. The research used multiple regression analysis and repeated measures analysis.
Used technologies: Tableau
This project aimed to show ability to use Tableau for creating dashbords.
Udemy
NVIDIA
NVIDIA
Google and SGH
Google Analytics Academy
Google Analytics Academy
MongoDB
MongoDB
The University of Nottingham
EF SET Certificate
Sprawny Marketing
UAM
Women in Tech Summit
Polish Biometrical Society
KNM and UAM
KNM and UAM
Poznański Festiwal Nauki i Sztuki
European Parliament in Strasbourg
DDN
PBEC and PP