Dogugun Ozkaya

An industrialized B2B machine learning solution. In this engagement with Toptal, I was part of a team focused on developing the solution.

My role encompassed the creation of the ML model and an API to serve real-time needs within the organization. Additionally, I visualized coverage metrics within the data pipeline, optimizing the process. In the final stages of the project, I worked on visualizing the data model outputs and SHAP values. I contributed by building an MLOps pipeline and automating the training, deployment, and scoring processes of the ML application within a Dockerized environment on AWS.

An analysis project powered by machine learning solutions to estimate urban development. The initial step was to determine the KPIs to use as the target variable. I scraped websites like The Good Schools and Walk Score with a user-like and efficient approach. I also developed Jupyter notebooks and PowerBI dashboards to deliver model outputs.

A demo showcase project that imitates my work in the power generation industry to estimate the value of the sensor in a 12-hour prediction horizon from its and other related sensor values in a 6-hour time window.

In the original project, the goal was to estimate the vibration of the combustion fan. The input features were temperature, dust collection, and airstream sensors. The target value is the vibration frequency in 6-9-12 hours of prediction horizons.

The input data is collected in SQL Server from Osisoft PI's IoT data. The model is deployed to the on-premise server, and the outputs are visualized in Grafana, along with other input feature values.

In the original project, a GBM was used for retraining and retuning easiness. This demo version uses LSTM as an alternative approach.

Finally, the deployed model and predictive monitoring dashboard are used by supervisor shifts to plan their maintenance program proactively.

In this challenging task, I aimed to predict the market-clearing price for the energy market. The output of this model is used to plan the week ahead surplus and shortage energy price possibilities.

The model data consisted of renewable forecasts, demand forecasts, plant availability declarations, and natural gas prices. The data is collected in a DWH model in SQL Server from APIs, web scraping, and Excel file reports.

A model is developed with gradient gradient-boosting regressor from the scikit-learn library.

I developed a recommendation engine for mavi.com, an online store, using PySpark's ALS model. We used purchase data and access logs to create personalized product suggestions, aiming to boost the conversion rate and introduce cross-category recommendations. The project successfully improved user engagement and conversion rates, highlighting the power of data-driven strategies in eCommerce.

In my master's thesis, supervised by Prof. Albert Guvenis, I conducted a comparative analysis of blood glucose prediction models. We focused on replicating existing models using AIDA2 simulator data, categorizing them into two groups: non-parametric and neural networks.

Within the non-parametric category, we compared Random Forest Regression (RFR), Gradient Boosting Machines (GBM), and Support Vector Machines (SVM). We pitted Long Short-Term Memory Networks (LSTMs) against Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in the neural network category.

The study revealed that SVM outperformed in terms of Root Mean Square Error (RMSE) among the non-parametric models, while ANFIS demonstrated superior performance in neural networks, surpassing SVM.

This project uses a web scraper to capture profile and activity information from top-level social media accounts for compliance reporting.

The automatized scraper is triggered by corn jobs daily, and the collected data is saved on PostgreSQL DB in AWS.

In a personal finance project, I created a Python script to collect data from finance.yahoo.com for a selection of Turkish stock market companies. The script retrieves data on EPS, 52-week range, and quarterly earnings growth for each company, then ranks the data based on these criteria. The results are saved and emailed.

Technically, the script runs on my personal AWS EC2 cluster. A Lambda service initiates the EC2 instance, and once the script completes its run, it shuts down the instance. The resulting files are archived in an S3 bucket.