My work is about making data meaningful

With advanced degrees from Koç and Boğaziçi Universities, I specialize in machine learning, statistical analysis, and data-driven problem-solving. I thrive on solving complex problems with data. From building predictive models to delivering clear recommendations, my approach is both analytical and results-driven.

My Web Applications

SafeStock AI Web App: Real-Time Stock Forecasting & Dashboard

Tech Stack: Python, LSTM(Long Short-Term Memory), Yahoo Finance API, Streamlit.
Overview: The app combines real-time stock data with AI-driven forecasting and dashboards to give users actionable insights.
Methodology: Built LSTM neural networks to predict 5-day stock price movements.
Created interactive dashboard with technical indicators(e.g., EMA, RSI, OBV) by utilizing Plotly.
Impact: Deployed an intuitive platform for investors to make data-driven decisions through predictive analytics and dynamic trend analysis.

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Justice Forecast App: Solvability Analysis for Homicide Cases

Tech Stack: Python, LightGBM, Pandas, MatplotLib, Streamlit.
Overview: Justice Forecast App, an end-to-end machine learning application, predicts homicide case's solvability.
The project emphasizes the importance of accounting for unsolved homicides and understanding the key factors impacting case solvability.
Methodology: Utilized LightGBM to analyze critical factors like the crime circumstances, homicide year, and victim's age for prediction.
Product: The interactive app allows users to explore how each factor affects outcomes and understand the predictions in real-time.
Impact: Designed and deployed machine learning model by transforming complex algorithms into a practical, user-friendly application.

Scientific Researcher, Koç University

  • Performed various wet-lab experiments to collect data
  • Processed 2708 microscopy images and quantitively analyzed 16070 data via Excel.
  • Applied statistical methods; hypothesis formulation, t-test and ANOVA.
  • Visualized processed data by 78 graphs, 76 figures, 12 representative images.
  • Reported and presented data to stakeholders at conferences and weekly group meetings.
  • Proved systematic effect of interested molecule on crucial cellular organelle for the first time.

BERT Mediated Sentiment Analysis on IMDb Reviews: Rule-Based Detection of Mixed Reviews Using Sentence Splitting

Natural Language Processing, Machine Learning, HuggingFace

Objective: Developed a model to classify IMDb movie reviews into positive, negative, and mixed categories.
Methodology: Fine-tuned pre-trained BERT model and implemented rule-based function using model logits to detect mixed reviews.
Tools/Technologies: Hugging Face Transformers, PyTorch for modeling, Scikit-Learn for evaluation, Matplotlib for visualization.
Impact: Achieved 92% classification accuracy, showcasing practical application of sentiment analysis.
Communication: Published results in Medium article to explain the approach to a broader audience.

Multivariate Time Series Forecast via Neural Networks: Apple and Google Stocks

GridSearchCV, Deep Learning, LSTM, Recurrent Neural Network (RNN)

Objective: Build deep learning model to forecast stock prices using historical multivariate time series data.
Methodology: Applied domain-based feature engineering, sliding windows function, normalization.
Built and fine-tuned LSTM neural networks.
Tools/Technologies: Used Pandas for data processing, Matplotlib/Seaborn for visualization, Keras for LSTM model development.
Impact: Achieved robust performance with 0.02% MAPE and 98% R2, reliable forecasting by deep learning in volatile market.
Communication: Published the project on GitHub for open-source collaboration and sharing.

SQL Database & Data Analysis: Tableau Dashboards for Covid-19 Burden on World throughout Years

SQL, Relational Database, Tableau, Dashboard

Objective: Conduct comprehensive analysis of Covid-19 data to uncover key trends in cases, deaths, and vaccination progress across different countries.
Methodology: Utilized SQL queries for ETL, data analysis and BI tool to explore data temporally and spatially.
Tools/Technologies: Used PostgreSQL for database management and querying, Tableau for data visualization
Impact: Generated interactive dashboards that provided findings on global Covid-19 trends, comparisons between countries, and tracking the pandemic progression.
Communication: Published the analysis results with an interactive dashboards on Tableau website, making the findings accessible for further exploration and decision-making.

Decision Tree and Random Forest Assisted Suggestions for Employee Retention

Employee Churn, Decision Trees, Ensemble Learning, Machine Learning

Objective: Analyze HR data to predict employee churn and identify employee leave/stay incentives.
Methodology: Applied decision trees, random forests, and logistic regression models.
Tools/Technologies: Used Pandas for data manipulation, Matplotlib for data visualization, Scikit-Learn for model implementation, GridSearch for hyperparameter tuning.
Impact: Produced random forest model with 94% F1-score and identified significant features influencing the target variable.
Communication: Visualized model performance through feature importance plots and confusion matrix for model interpretability.

Gradient Boosting Predictive Model for TikTok's Claim Classification: Hypothesis Testing, Logistic Regression, Tree-Based Models

XGBoost, GridSearchCV, Classification, Explanatory Data Analysis(EDA)

Objective: Develop machine learning model to assist in the classification of videos as either claims or opinions.
Methodology: Conducted EDA, hypothesis testing, logistic regression, and build tree-based classification models.
Tools/Technologies: Used Pandas for data manipulation, Matplotlib/Seaborn for data visualization, Scikit-Learn for regression and ML model implementation, GridSearch for hyperparameter tuning.
Impact: Achieved 99% F1-score classification accuracy and optimized user submissions workflows.
Communication: Documented the project as executive summaries and shared the project on GitHub.

Feature Engineering on Study Mobility: Dashboards on Students' Preferences

Python, Geographic Data Analysis, Tableau, Dashboards

Objective: Identification of study mobility patterns, anomalies, and trends over decade.
Methodology: Conducted data cleaning, data structuring, feature engineering, and build interactive graphs and dashboards.
Tools/Technologies: Used Pandas for data manipulation, Tableau for data visualization.
Impact: Explored global student mobility patterns and trends over time, provided valuable insights into educational preferences across different nations.
Communication: Published interactive dashboards on Tableau website, making the analysis intuitive and accessible.