Monitoring & Evaluation
Results frameworks, indicator design, evaluation questions, data sources and reporting structures.
Monitoring • Evaluation • Learning
Turning evidence into better programmes.
I am a monitoring, evaluation and project management professional with experience in results-based management, grant programmes, public-sector projects, gender equality and data-informed decision-making.
About
A concise overview of my professional perspective and the type of assignments I aim to support.
My work combines programme design, monitoring and evaluation, stakeholder coordination, quantitative analysis and learning. I have contributed to public grant programmes, regional development planning, project portfolio monitoring and civil-society initiatives. My current portfolio focuses on practical MEL products, including evaluation matrices, theories of change, indicator frameworks, technical papers and Power BI dashboards.
Core expertise
Areas in which I bring together analytical thinking, implementation experience and clear reporting.
Programme & Grant Management
Technical and financial monitoring, risk review, implementation follow-up and stakeholder coordination.
Gender & Social Inclusion
Intersectional analysis, women's employment, participation, inclusion and gender-responsive programming.
Selected portfolio
A writing sample is available below; the remaining items are in development and will link to project pages and interactive dashboards.
MEL System Assessment — Writing Sample
An abridged Phase 1 assessment of a volunteer-led urban research association's monitoring system: findings, diagnosis and a proposed two-tier redesign.
Read the PDF →Women-UP Desk Evaluation
A theory-based assessment of programme relevance, intervention logic and support for women's formal employment.
Project page coming soon →MEL Framework & Evaluation Matrix
A practical framework linking outcomes, indicators, evidence sources, methods and learning questions.
Framework coming soon →Power BI Dashboards
Visual reporting products for project performance, women's employment and programme monitoring.
Dashboard gallery coming soon →Applied Data Science & Machine Learning
Project work from the Mastering Applied Data Science with Deep Learning programme, covering exploratory analysis, machine learning and deep learning on real-world datasets. Three selected projects are shown below.
Loan Default Prediction
End-to-end credit-risk model on 257,000 loan records: deduplication, detection and removal of two target-leakage sources, ratio features (loan-to-income, debt-to-income, credit utilisation) and a comparison of seven classifiers. Logistic Regression is recommended for transparency (ROC AUC 0.70); at a 0.5 threshold it cuts the default rate among approved loans from 27.4% to 17.6%.
View project on GitHub →Customer Segmentation with RFM
Segments 3,054 e-commerce customers for targeted marketing. Raw order data is split into customer, product and order tables in a SQLite database, then summarised as Recency, Frequency and Monetary values and clustered with K-Means (elbow, silhouette and business judgement). Four segments emerge: a loyal group of under 3% of customers brings in a fifth of revenue, while lapsed big spenders hold over half of it, the clearest target for a win-back campaign.
View project on GitHub →Laptop Price Prediction
Predicts the price of 1,303 laptop models from their specifications. Raw text columns are turned into usable features: pixel density from screen size and resolution, separate SSD and HDD capacities, CPU groups and clock speed, GPU brand and touchscreen/IPS flags. Five models are compared on log price; XGBoost performs best (R² 0.90), ahead of a GridSearchCV-tuned Random Forest. RAM and SSD capacity emerge as the strongest price drivers.
View project on GitHub →Master's theses
Two graduate degrees behind the work: quantitative modelling and intersectional gender analysis. Full texts available as PDF.
Intersectionality Perspective in Examining Types of Social Inequality and Discrimination
Traces the conceptual history of intersectionality and how race-, class- and gender-based discrimination shape one another in patterns of social inequality. The thesis reviews three decades of empirical applications and the unresolved debate over whether intersectionality works as a theory, a methodology or an analytical tool, alongside critiques from feminist and classical Marxist positions.
Original title (TR): Toplumsal Eşitsizlik ve Ayrımcılık Türlerinin İncelenmesinde Kesişimsellik Perspektifi · Thesis in Turkish
Stochastic Volatility and Stochastic Interest Rate Model with Jump and Its Application on General Electric Data
Compares two approaches to pricing European call options under stochastic volatility, stochastic interest rates and jumps: closed-form solutions derived through characteristic functions and martingale methods, and solutions to the partial integro-differential equation. Four alternative models are then tested empirically on General Electric stock option data.
Original title (TR): Sıçramalı Stokastik Volatilite ve Stokastik Faiz Oranı Modeli ve Modelinin General Electric Verisi Üzerine Uygulaması · Thesis in English
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