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Göksu Demirci
Konya, Turkey
ML engineer building RAG, forecasting and full-stack apps
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ML engineer building RAG, forecasting and full-stack apps
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Bosch production-line quality classifier. An end-to-end binary classifier predicting component quality failures, reducing 1,183,747 rows and 970 raw features to a modelling set of 450,519 samples and 158 features. Handled a 1:228 class imbalance with scale-weighted XGBoost and SMOTE, engineered 24 aggregate features, and raised defect recall by 29% over the baseline with threshold optimisation and stratified K-fold validation. Shipped as a containerised service: FastAPI prediction and batch endpoints, a Streamlit interface for interactive sampling, Docker Compose orchestration and SHAP-based feature attribution.
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CAD automation that cut a two-day drawing job to five minutes
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Real-time mobile app for Black Pine Media
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Screening 21,379 papers with a hybrid RAG pipeline. My M.Sc. thesis was a PRISMA 2020 meta-analysis of feature-extraction methods for EEG-based sleep staging, but the harder part was the screening. I built a hybrid RAG pipeline with an LLM referee layer for content filtering, MIDO-framework structured extraction and a two-stage quality check using the Newcastle-Ottawa Scale and PROBAST+AI, narrowing 21,379 records to a 140-paper methodological pool. Applied DerSimonian-Laird random-effects meta-analysis on logit-transformed F1 scores across W, N1, N2, N3 and REM stages, quantified between-study heterogeneity and mapped feature families to stage-level performance. Published at the 4th International Sarajevo Scientific Research and Innovation Congress, 2026.
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