Deniz Yener

A comprehensive AI Adoption Readiness Assessment designed for Coresbond to evaluate the organization's preparedness for integrating AI technologies. This assessment covers key areas such as data infrastructure, talent capabilities, ethical considerations, and strategic alignment to evaluate the organization's readiness to ensure successful AI implementation.

Key achievements:

• The 95% MIT report shows that the "vast majority" of organizations, including SMEs, "remain stuck with no measurable P&L impact" from their AI initiatives (Challapally et al., 2025).
• A structured questionnaire was employed to elicit feedback from leading professionals regarding the tool's efficacy in assessing AI adoption and usage efficiency.
• By providing a systematic assessment of strategic maturity and regulatory viability, the tool equips SMEs to transition from exploratory, peripheral AI use to targeted, core operational integration, thereby maximising the potential for measurable financial returns and ensuring legal operational resilience in a regulated environment.

Technologies used: TypeScript, CSS, HTML, Json

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Kartta is a dashboard that helps urban planners, city governments, and policymakers understand the environmental and social risks associated with the construction and operation of data centers in cities.

Key achievements:

• We are a data-driven platform that uses a wide range of environmental and urban datasets to generate a comprehensive overview of cities and human settlements.
• By combining NASA satellite observations with local information (water resources, energy supply, population density, and household consumption patterns), Kartta creates an integrated picture of how urban areas function and evolve.
• This data fusion allows planners and decision-makers to make more accurate, evidence-based decisions about where to locate new data centers and how to improve existing urban areas affected by current ones.
• In short, we turn complex, multi-source data into clear insights for sustainable urban planning related to data center constructions and maintenance.

Technologies used: Python, Streamlit, QGIS, Figma, Scikit-learn, Pandas, NumPy

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Developed a machine learning model to predict heart disease risk using patient data. This project focuses on creating a reliable and accurate prediction system that can assist healthcare professionals in early diagnosis.

Key achievements:

• Achieved 92% accuracy in heart disease prediction
• Implemented multiple ML algorithms for comparison
• Created an intuitive user interface for predictions
• Optimized model for high recall to minimize false negatives
• Conducted thorough feature importance analysis

Technologies used: Python, Scikit-learn, Pandas, NumPy, Matplotlib

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Developed a machine learning model to predict PM2.5 air quality levels in Beijing using historical data from 2010-2014. After forecasting PM2.5 air pollution levels in Beijing, evaluated whether meteorological variables alone could predict pollutant concentrations effectively without engineered lag and rolling features.

Key achievements:

• Built predictive models achieving 95% accuracy using advanced ML algorithms
• Implemented interactive data visualizations for trend analysis
• Analyzed 5 years of environmental data with 438,000+ data points
• Identified key correlations between weather conditions and PM2.5 levels
• Developed a robust data preprocessing pipeline for handling missing values

Technologies used: Python, Pandas, Scikit-learn, Plotly, Winsorizer, Streamlit

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