THE PORTFOLIO / ALEX OLERIMI
A collection of explorations in data science, machine learning, and development. Real questions. Thoughtful approaches.
Developed a predictive model to classify tinnitus presence using extracted features from patient data. Built and evaluated multiple ML algorithms (SVM, Random Forest, Logistic Regression), implemented SMOTE, and achieved strong performance.
Applied ML techniques to classify arrhythmia patterns on medical data. Developed automated preprocessing pipeline for signal segmentation and tested models including CNN-based approaches.
Processed and analysed multi-million-row datasets (65M+ Rows) using PySpark and Azure Databricks. Built automated ETL pipeline and delivered insight dashboards for regional crime trends.
Constructed EER diagrams, conceptual models, and SQL-based analytical solutions. Delivered optimised queries and a working data warehouse structure.