Hello, I'm
Data Scientist
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With a robust background in data science, data analysis, and geographic information systems, I have extensive experience as a Research Assistant at Akdeniz University. My academic journey includes a Ph.D. and MSc in Biology, and continuous learning through various courses and training programs. I am proficient in Python (numpy, pandas, matplotlib, seaborn, scikit-learn, TensorFlow, Keras), machine learning, deep learning, NLP, LLM, Tableau, PowerBI, ArcGIS, and QGIS.
What excites me most about this field is its constant evolution, dynamic challenges and the continuous learning it demands. I firmly believe in the transformative power of data to reshape industries and drive innovation, and I'm here to be a part of that transformation.
I am passionate about using Python and statistical analysis to uncover actionable insights from complex datasets. My continuous learning journey keeps me abreast of the latest advancements in machine learning and deep learning, enabling me to solve diverse challenges in data science. Fluent in English and Turkish, I thrive in diverse environments. Let's connect to explore impactful solutions through data science and research.
Here Are My
Developed and implemented prediction algorithms using machine learning models with BentoML for an early-stage startup focusing on integrating artificial intelligence into formal education, offering integrated RAG Chatbot solutions.
Led Agile/Scrum ceremonies (sprint planning, sprint retrospectives) for a cross-functional data science team. Managed data science projects, ensuring timely delivery and alignment with business goals. Utilised Clickup and Github to track progress and maintain documentation.
Developed and implemented machine learning models for prediction and classification tasks. Analyzed customer behaviour to create segmentation strategies. Regularly reported analysis results and model performance to stakeholders. Integrated RAG Chatbot solutions. Executed and managed project deployments
Organized and cleansed data sets by rectifying missing and erroneous data. Analyzed data, modelled relationships, and visualized findings using Tableau and PowerBI. Worked extensively on Amazon, eBay, Walmart, Keepa, and advertising data sets.
Contributed to interdisciplinary research in botany, climate change impact, conservation biology, ecological niche modeling using GIS applications, and medicinal plant studies, leading to multiple publications in peer-reviewed journals. Published and presented findings at national and international conferences, collaborating with leading researchers to address ecological and environmental challenges.
Here Are My
I can optimize your data for better results, informed decisions and cost savings. My proficiency in technical tools like SQL, Python, HTML, CSS, Power BI and Tableau, combined with strong soft skills, enables me to deliver impactful data solutions.
Uncover hidden insights and patterns within your data to identify trends, anomalies and opportunities for data-driven decision-making.
Harness the power of advanced analytics, including A/B testing, effect size analysis, predictive models and machine learning solutions to forecast outcomes, optimize processes and gain a competitive edge.
Transform complex data into clear and compelling visual narratives. Create custom dashboards and reports for enhanced data communication and understanding.
Translate intricate technical concepts into clear and concise documentation. From reports to comprehensive materials, I ensure your data projects are well-documented and readily accessible.
Craft intuitive web interfaces that deliver information in an accessible format. Design and build data-driven web solutions, seamlessly blending functionality and aesthetics to present data solutions that engage and inform.
Collaborate seamlessly with your team to integrate data solutions into your business processes. Whether it's short-term projects or ongoing data-driven initiatives, I'm here to support your success.
Browse Through My
In the "Academic Success Prediction" project, a predictive model was meticulously developed using LightGBM to forecast students' academic performance. This initiative entailed comprehensive data preprocessing, sophisticated feature engineering, and meticulous model tuning to address class imbalance and enhance prediction accuracy. Various evaluation metrics were employed to rigorously assess the model's performance, thereby ensuring the provision of reliable insights into factors influencing academic success. This project exemplifies proficiency in machine learning, particularly in addressing data challenges and optimizing models for superior predictive performance.
This project involved the development and comparison of multiple models for retina damage detection from OCT images. It covered model architecture design, transfer learning with ResNet152, VGG16, and fine-tuning techniques to improve model accuracy. Technologies and methods used included Python, TensorFlow, Keras, data preprocessing, visual data analysis, custom CNN architecture, ResNet152, VGG16, transfer learning, and model tuning and optimization.
This project aims to analyze customer data from a shopping mall to understand customer behavior and segment them based on various attributes using clustering techniques
The Credit Score Prediction project utilizes Artificial Neural Networks (ANN) to predict credit scores based on various financial and demographic factors. This project involves data preprocessing, feature engineering, model training, and evaluation using ANN to achieve accurate predictions. The implementation demonstrates the effectiveness of deep learning techniques in financial applications, showcasing skills in machine learning, data analysis, and model optimization.
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