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# zero-dependency install (pandas optional for DataFrame output) !pip install emkaymoin
Data scientist · Seattle, WA · M.S. Data Science @ Seattle U (June 2026)
This portfolio is a Python package. pip install emkaymoin installs it locally, and every cell below calls a real emkay.X() function — so you can run the same commands in your own REPL.
# zero-dependency install (pandas optional for DataFrame output) !pip install emkaymoin
# warm up the kernel import emkaymoin as emkay emkay.whoami()
# load all projects as a dataframe projects = emkay.projects() projects.shape
# preview the projects table — click a row to jump to its case study
emkay.projects().sort_values("year", ascending=False)[["year","title","domain","model","metric","roles"]]The next ten cells each load one project via emkay.project('id'). Hover the chart, click the run button on the left to re-execute, or pass mode='short' for a one-liner.
# tacoma_pole_inspection_risk
emkay.project("tacoma")# safeanc_emergency_aware_anc
emkay.project("safeanc")# legal_clause_classifier
emkay.project("legalbert")# bird_species_audio_classification
emkay.project("bird")# youth_substance_use_risk
emkay.project("youth")# global_mortality_analysis
emkay.project("mortality")# seattle_smart_parking
emkay.project("parking")# svm_based_diabetes_risk_prediction
emkay.project("diabetes")# hotel_demand_forecasting
emkay.project("hotel")# supply_chain_ml_suite
emkay.project("supplychain")# self-rated proficiency on a 1–5 scale (drawn as horizontal bars) emkay.skills()
# degrees emkay.education()
The most reliable way to reach me is email. I usually respond within a day.
# how to reach me emkay.contact()
This page is the work. The package is the person — install it and the kernel will tell you the rest. Full API reference at /docs.
>>> pip install emkaymoin >>> import emkaymoin as emkay >>> emkay.origin() # age-14 math comp → ML pipelines >>> emkay.bgmi() # esports → data science >>> emkay.rubiks() # speed-solving nationals, in ASCII >>> emkay.loadout() # tech stack as a gaming loadout >>> emkay.fun_fact() # a random thing about emkay >>> emkay.roast() # ouch >>> emkay.puzzle() # solve a data riddle to unlock direct contact >>> emkay.gg() # closing message >>> emkay.help() # see all commands · or open /docs
End of notebook · last cell executed at runtime · thanks for scrolling. · pip install emkaymoin