01
R · RStudio · EDA · Visualization
Smartphone Use & Education Analytics
An RStudio study exploring and visualizing relationships between smartphone usage and education-related variables.
← All workDECISION MODELLINGMathematical problem formulation DATA RISK / 2025—2026Major data-breach research
GRADUATE STUDIES · BUSINESS INTELLIGENCE / DATA ANALYTICS
Data & Analytics Studies
Selected R and Python work across exploratory analysis, visualization, regression, classification, decision modelling and applied machine learning.
REPRESENTATIVE ANALYTICS VISUALR / PYTHON / TABULAR DATA
EXPLORATORY ANALYSISDATA → PATTERN → MODEL → EXPLANATION
A process visual — not a recreation of any study result.
RELATIONSHIP
MODEL SPACE
01 / OVERVIEW
Not one dataset. A practice of analytical thinking.
Across graduate study I worked on different problem types: health and education data, price estimation and classification, visualization and decision modelling. Rather than list every assignment, this page groups a few studies that show different analytical capabilities.
CONTENT PRINCIPLE
Showing the method without inventing findings.
Some detailed reports are not part of the portfolio source set today. For that reason, this page does not invent forgotten numerical outcomes or statistical conclusions; it only presents problem scope, tools and methods that can be supported.
02 / SELECTED STUDIES
02
Python · Pandas · SciPy · Matplotlib · Seaborn
Factors Affecting Stress Levels
An analysis of 2019–2020 Canadian Health Survey data examining relationships between demographic, health and stress-related variables, with code, charts, report and presentation outputs.
03
Python · Data Collection · Statistics · Estimation
Real Estate Market Price Estimation
A prototype that collected listing data for a selected city/region and derived market-level averages, minimums, maximums and estimated price ranges.
04
Regression · Logistic Regression · Random Forest · Classification
Applied Machine Learning Studies
A set of studies across automotive pricing, cinema-ticket data and other tabular datasets, comparing regression, classification and decision-support approaches.
03 / PROCESS
A common path from data to an interpretable output.
The subjects vary, but the working pattern is consistent: understand and clean the data, explore relationships, choose an appropriate statistical or machine-learning approach, visualize the output, and turn the analysis into a reportable result.
01UNDERSTAND→02CLEAN / EXPLORE→03CHOOSE METHOD→04MODEL / ANALYZE→05VISUALIZE→06REPORT
04 / METHODS
Tools & methods
R / RStudioPythonPandasSciPyMatplotlibSeabornJupyter / ColabRegressionLogistic RegressionRandom ForestClassificationData VisualizationExcel / Formulation
05 / RESEARCH
Research & decision modelling
Alongside applied analysis, I worked on mathematically formulated decision problems in Excel and prepared a comprehensive research presentation on the impact of major global data breaches during 2025–2026.
Excel-based logical / decision problems and formulation studies.
A roughly 56-page research presentation on the impact of major global data breaches.
Next: Selected Web / ProductUH/ · CASE STUDY 03