Metadata-Version: 2.4
Name: emkaymoin
Version: 0.5.0
Summary: Khaja Moinuddin Mohammed's data science portfolio as a Python package
Home-page: https://github.com/kmohammedsu/emkaymoin
Author: Khaja Moinuddin Mohammed
Author-email: Khaja Moinuddin Mohammed <emkaymoin@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/kmohammedsu/emkaymoin
Project-URL: Repository, https://github.com/kmohammedsu/emkaymoin
Project-URL: Issues, https://github.com/kmohammedsu/emkaymoin/issues
Keywords: portfolio,data-science,resume,cv
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# emkaymoin

> **Khaja Moinuddin Mohammed's data science portfolio — as a Python package.**

```bash
pip install emkaymoin
```

```python
import emkaymoin as emkay

emkay.whoami()        # who is emkay?
emkay.summary()       # full snapshot in one command
emkay.roast()         # go on, you deserve it
emkay.puzzle()        # solve a data riddle to unlock contact info
emkay.gg()            # good game
```

---

## Portfolio

| Function | |
|---|---|
| `emkay.whoami()` | # Bio card — name, role, availability |
| `emkay.summary()` | # Terminal resume — full snapshot |
| `emkay.pitch()` | # 30-second elevator pitch |
| `emkay.hire_me()` | # Why hire emkay |
| `emkay.contact()` | # Email, phone, GitHub, LinkedIn |

## Projects

| Function | |
|---|---|
| `emkay.projects()` | # All projects as DataFrame |
| `emkay.projects(role='MLE')` | # Filter by role tag |
| `emkay.projects(domain='audio')` | # Filter by domain keyword |
| `emkay.projects(stack='XGBoost')` | # Filter by tech |
| `emkay.projects(sort='year', asc=True)` | # Sort ascending |
| `emkay.project('tacoma')` | # Full case study |
| `emkay.project('tacoma', mode='short')` | # One-liner summary |
| `emkay.project('tacoma', mode='json')` | # Raw dict |
| `emkay.top(n=3)` | # Most recent n projects |
| `emkay.random()` | # Random project |
| `emkay.search('CNN')` | # Search across all fields |
| `emkay.shap('tacoma')` | # SHAP feature importance chart |

## Skills & Background

| Function | |
|---|---|
| `emkay.skills()` | # Skill proficiency bar chart |
| `emkay.stack()` | # All technologies used |
| `emkay.domains()` | # All project domains |
| `emkay.education()` | # Degrees and GPA |
| `emkay.achievements()` | # Awards, leadership, distinctions |
| `emkay.learning()` | # Currently learning |

## Export & Resume

| Function | |
|---|---|
| `emkay.resume()` | # Full text resume in terminal |
| `emkay.resume(open=True)` | # Text resume + opens emkaymoin.com |
| `emkay.export('json')` | # Export full portfolio as JSON |
| `emkay.export('txt')` | # Export full portfolio as text file |

## Easter Eggs

| Function | |
|---|---|
| `emkay.origin()` | # The story of how emkay became a data scientist |
| `emkay.loadout()` | # Tech stack as a gaming loadout |
| `emkay.bgmi()` | # Esports career and what it taught about data science |
| `emkay.rubiks()` | # ASCII Rubik's cube and the cube story |
| `emkay.fun_fact()` | # Random fun fact about emkay |
| `emkay.puzzle()` | # Solve a data riddle to unlock contact info |
| `emkay.solve('your answer')` | # Submit your puzzle answer |
| `emkay.gg()` | # Good game — closing message |

## Meta

| Function | |
|---|---|
| `emkay.roast()` | # Random roast — self, recruiter, or tech person |
| `emkay.version()` | # Package version and changelog |
| `emkay.changelog()` | # Alias for version() |
| `emkay.star()` | # GitHub repo link |
| `emkay.credits()` | # End credits screen |
| `emkay.timeline()` | # ASCII project timeline |
| `emkay.help()` | # All commands |

---

## Projects

| Year | Title | Domain | Key Result |
|---|---|---|---|
| 2026 | Tacoma Pole Inspection Risk | Utilities · ML | 18% est. risk ↓ |
| 2026 | SafeANC — Emergency-Aware ANC | Audio AI · DL | Concept · <50ms target |
| 2026 | Legal Clause Classifier | NLP · DL | 87% F1 |
| 2025 | Bird Species Audio Classification | Audio · DL | 100% binary · 71.9% multiclass |
| 2025 | Youth Substance Use Risk | Health · ML | 81% accuracy |
| 2025 | Global Mortality Analysis | Health · Research | 75–80% variance explained |
| 2024 | Seattle Smart Parking Demand | ML · Geospatial | R² = 0.86 |
| 2024 | SVM-Based Diabetes Risk Prediction | Health · ML | 84% accuracy · ROC-AUC 0.91 |

---

## Zero dependencies

emkaymoin works out of the box with no required dependencies.
If pandas is installed, emkay.projects() returns a DataFrame.
If not, it prints a clean text list. Either way it works.

    pip install emkaymoin           # zero dependencies
    pip install emkaymoin[full]     # with pandas

---

## Contact

- 📧 emkaymoin@gmail.com
- 🔗 linkedin.com/in/emkaymoin
- 🐙 github.com/kmohammedsu
- 🌐 emkaymoin.com

---

Data Scientist · M.S. Data Science · Seattle University · June 2026 · Available full-time
