PROJECTS

Python & Data Projects

Projects that mirror work I’d do as a software engineer, data engineer, or data scientist: data pipelines, analytics, ML models, and service backends.

Java Python AI/ML Data

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More Python & data projects on GitHub

I keep extra experiments and smaller scripts in my GitHub repositories (CLI tools, notebooks, and one-off utilities).

Simple Calculator with Tkinter — Desktop calculator built with Python/Tkinter. Supports basic arithmetic (+), (−), (×), (÷).

Live / Demo
Unit Converter with Tkinter — Multiple converters built with Tkinter (e.g., BMI, temperature, and other unit conversions).

Live / Demo
Other small utilities & experiments

Popular Baby Names Dashboard

End-to-end analysis of NYC’s Popular Baby Names. A Python prep script labels New / Rising / Falling / Stable trends, and the results are visualized in Looker Studio (or optionally a Plotly/Dash view) with interactive filters and KPI rollups.

PyCharm Pandas Looker Studio Google Sheets Plotly/Dash

Live Dashboard

Pandas data prep and year-over-year trend labeling.
Export CSV → Google Sheets → Looker Studio for reporting.
Live dashboard: Looker Studio report.
Re-runnable script: python prepare_baby_names.py.
Source: Data.gov — Popular Baby Names (NYC)

Customer Metrics Pipeline & API

End-to-end churn-style pipeline: generate synthetic customer CSVs, engineer metrics, train a simple model, and expose a /score_customer FastAPI endpoint with interactive docs at /docs.

Python FastAPI Pandas scikit-learn

Feature pipeline over synthetic CSV customer metrics with Pandas.
Churn-style model trained with scikit-learn and saved for inference.
FastAPI service exposing /score_customer with Swagger UI and JSON responses.

Scientific Calculator

Extended Tkinter calculator with improved keyboard handling, clear-entry, allow-listed evaluation, and scientific functions like x², 1/x, sin/cos/tan (+ inverses), and log₁₀.

Python Tkinter GUI

Live / Demo

Keyboard shortcuts and clear-entry/backspace behavior.
Regex allow-list before evaluation for safety.
Modular methods for unary operations and percent logic.

Customer Churn (Cars)

Cost-aware churn model for car owners using synthetic data. TF-IDF text features plus numeric & categorical signals feed a tuned Logistic Regression classifier. Includes CLI tools (train.py, predict.py) to train and score CSV files.

Python Pandas scikit-learn TF-IDF

Synthetic churn dataset for car models.
TF-IDF vectorization and Logistic Regression baseline (GridSearchCV).
Cost-aware threshold selection for business-focused metrics.
predict.py appends label and probability to output CSVs.

Python Foundations Toolkit

Small, portfolio-style toolkit showing solid Python foundations: a reusable core package, a CLI with subcommands, a FastAPI service that reuses the same logic, and a tiny SQLite demo database seeded with synthetic rows and sample CSV files for learning and testing.

Python FastAPI SQLite CLI pytest

Core modules for CSV, text, and SQLite: toolkit/core/*.py.
CLI entry point with subcommands: python -m toolkit.cli ....
FastAPI app (toolkit/api.py) exposing /csv-summary and /text-summary.
SQLite demo DB (data/demo.db) and synthetic/sample files in data/ for practice runs.
pytest tests validating CSV/text helpers and core utilities.