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Achievement: Pull Shark

Achievements

Achievement: Pull Shark

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magdalena-ivanova-ds/README.md

About me

I'm an informatics student with a strong focus on data science and understanding real-world behavior through data. I enjoy exploring datasets, visualizing trends, running analyses and models, and turning raw data into insights that help explain what’s happening and why.

Contact me at: magdalena.ivanova.ds@gmail.com

Skills

Data & Analytics
SQL · PostgreSQL · Advanced SQL (window functions, CTEs, ranking, analytical joins)
Exploratory Data Analysis · Data Cleaning · Feature Engineering · Data Visualization

Machine Learning
scikit-learn · Time-Series Forecasting · Model Evaluation · Regression & Classification Models

Programming
Python · R · JavaScript

Tools
Git · Docker · Jupyter · VS Code · AWS

Featured Work

  • Ride-Hailing Operations Analytics - Advanced SQL analysis of a simulated Uber-style marketplace (~200k trips): demand patterns, driver performance, cancellations, retention cohorts, and query optimization.
  • NYC Ride Demand Forecasting - End-to-end hourly demand forecasting using feature engineering and XGBoost.
  • Social Habit Tracker - Full-stack habit tracking app with analytics on streaks and user engagement.

See pinned repositories for full project details.

Links

Pinned Loading

  1. NYC-Ride-Demand-Forecasting NYC-Ride-Demand-Forecasting Public

    End-to-end forecasting of hourly NYC ride demand with time-series features and XGBoost.

    Jupyter Notebook

  2. Social-Habit-Tracker Social-Habit-Tracker Public

    Full-stack habit tracker with built-in behavior analytics, visualized streak and consistency metrics, and data-driven insights into user engagement.

    Python

  3. Ride-Hailing-Operations-Analytics Ride-Hailing-Operations-Analytics Public

    Advanced SQL project analyzing a ride-hailing marketplace like Uber - looking at demand, driver performance, cancellations, retention and query optimization using a ~200k-trip synthetic dataset.

    Python