I employed Graph Neural Networks (GCNs) using PyTorch Geometric (PyG) to develop a recommendation system based on user-book interactions, evaluating LightGCN and NGCF models to recommend books effe…

I analyzed inflation trends in Turkey by applying various time series and regression models, including ARIMA and dynamic factor models, using R programming to provide insights into future inflation…

I harmonized diverse speech emotion datasets and developed convolutional neural network (CNN) models, including Mel Spectrogram CNN, MFCCs CNN, and Mel Spectrogram CRNN, achieving a 72% accuracy in…

I build a model that categorizes news as fake and real news using deep learning tools using a text-based dataset.

Identify the variables affecting house prices,Create a linear model that quantitatively relates house prices with variables, and the accuracy of the model

A dashboard of interactive visualizations to help customers explore the data.

Python script for analyzing the financial records of the company. Analysis to the terminal and export a text file with the results.

Analyze the data to identify possible fraudulent transactions by using ERD, PostgreSQL, ERD and Python.

The time-series tools (Time Series Forecasting and Linear Regression Modeling ) in order to predict future movements in the value of the Japanese yen versus the U.S. dollar.

Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning

Natural language processing to understand the sentiment in the latest news articles featuring Bitcoin and Ethereum.

I created two financial analysis tools by using APIs as part of the technical solution. The first was a personal finance planner that allowed users to visualize their savings composed by investment…

Unsupervised learning and visualization

Quantitative analysis techniques with Python and Pandas to determine which portfolio is performing best across many areas: volatility, returns, risk, and Sharpe Ratios.

Deep learning recurrent neural networks to model bitcoin closing prices. use the FNG indicators to predict the closing price.

FinTech research skills by accessing reports, publications, and online resources that FinTech professionals use to evaluate the industry.