Building a Research Portfolio

# Create a repository named exactly as your username
# Example: if username is "janesmith", create "janesmith" repo

# Hi, I'm Jane Smith! 👋

## About Me
🎓 PhD Student in Data Science at University XYZ  
🔬 Researching machine learning applications in healthcare  
💻 Python | R | SQL | TensorFlow  
📊 Passionate about making data tell stories

## Current Projects
- 🏥 Predicting patient readmission risk using EHR data
- 🌡️ Climate change impact analysis using satellite imagery
- 📈 Open-source library for time series anomaly detection

## Recent Publications
- Smith, J. et al. (2025). "ML Approaches to Healthcare." *Journal of Medical AI*
- Smith, J. & Doe, A. (2024). "Climate Data Analysis." *Earth Sciences Review*

## Skills
**Languages:** Python, R, SQL, Julia  
**ML/DL:** scikit-learn, TensorFlow, PyTorch  
**Data:** pandas, NumPy, Apache Spark  
**Viz:** matplotlib, ggplot2, Plotly  

## Featured Projects
### 🏥 Hospital Readmission Predictor
[![Repo](https://img.shields.io/badge/GitHub-Repo-blue)](https://github.com/janesmith/readmission-pred)
Predicts 30-day readmission risk with 85% accuracy. Built with Python and scikit-learn.

### 📊 Climate Analyzer
[![Repo](https://img.shields.io/badge/GitHub-Repo-green)](https://github.com/janesmith/climate-analysis)
Analyzes 20 years of climate data to identify trends. Interactive dashboards with Plotly.

## 📫 Get in Touch
- Email: jane.smith@university.edu
- LinkedIn: [linkedin.com/in/janesmith](https://linkedin.com/in/janesmith)
- Twitter: [@janesmith_data](https://twitter.com/janesmith_data)
- Website: [janesmith-research.github.io](https://janesmith-research.github.io)

## GitHub Stats
![Jane's GitHub stats](https://github-readme-stats.vercel.app/api?username=janesmith&show_icons=true&theme=radical)

# Hospital Readmission Prediction

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)

## Overview
Predicts 30-day hospital readmission risk using electronic health records (EHR) and machine learning.

**Key Results:**
- 85% accuracy on validation set
- Identified top 5 risk factors
- Reduced false negatives by 20% vs. baseline

## Quick Start
```bash
# Clone the repository
git clone https://github.com/yourusername/readmission-pred.git
cd readmission-pred

# Install dependencies
pip install -r requirements.txt

# Run the model
python train.py --data data/patients.csv

├── data/               # Data files (not tracked in git)
├── notebooks/          # Jupyter notebooks for exploration
├── src/                # Source code
│   ├── preprocessing.py
│   ├── models.py
│   └── evaluation.py
├── tests/              # Unit tests
├── results/            # Figures and tables
├── requirements.txt    # Dependencies
└── README.md          # This file

@article{smith2025readmission,
  title={Predicting Hospital Readmission Risk},
  author={Smith, Jane},
  journal={Journal of Medical AI},
  year={2025}
}

---

## Platform 2: Personal Website

A personal website gives you full control over your presentation.

### Option 1: GitHub Pages (Free & Easy)

**Using Jekyll:**

```bash
# Install Jekyll
gem install bundler jekyll

# Create new site
jekyll new my-research-site
cd my-research-site

# Serve locally
bundle exec jekyll serve

# Visit http://localhost:4000

my-research-site/
├── _config.yml         # Site configuration
├── _posts/             # Blog posts
├── _projects/          # Project pages
├── index.md            # Homepage
├── about.md            # About page
├── publications.md     # Publications list
└── assets/             # Images, CSS, JS
    └── images/

title: Jane Smith - Data Science Researcher
email: jane.smith@university.edu
description: >-
  PhD student researching machine learning applications in healthcare.
  Passionate about reproducible research and open science.  

baseurl: ""
url: "https://janesmith.github.io"

# Build settings
theme: minima
plugins:
  - jekyll-feed
  - jekyll-seo-tag

# Social links
github_username: janesmith
twitter_username: janesmith_data
linkedin_username: janesmith

# Google Analytics (optional)
google_analytics: UA-XXXXXXXX-X

# Install Hugo
brew install hugo  # macOS
# or download from https://gohugo.io

# Create new site
hugo new site my-portfolio
cd my-portfolio

# Add a theme
git init
git submodule add https://github.com/hugo-academic/hugo-academic.git themes/academic

# Configure
echo 'theme = "academic"' >> config.toml

# Create content
hugo new projects/my-project.md

# Serve locally
hugo server -D

# Build for production
hugo

---
title: "Climate Change Impact Analysis"
date: 2025-01-15
tags: ["climate", "satellite-data", "python", "machine-learning"]
featured_image: "/images/climate-project-header.png"
---

## Overview
This project analyzes 20 years of satellite imagery to quantify climate change 
impacts on global vegetation patterns.

## Motivation
Climate change is affecting ecosystems worldwide, but comprehensive analysis 
of vegetation changes at scale has been limited. This project fills that gap.

## Data
- **Source:** NASA MODIS satellite data (2000-2020)
- **Coverage:** Global, 250m resolution
- **Size:** 500GB of imagery
- **Variables:** NDVI, EVI, surface temperature, precipitation

## Methods
### 1. Data Collection
Downloaded MODIS tiles using Google Earth Engine API.

### 2. Preprocessing
```python
import ee
import geemap

# Initialize Earth Engine
ee.Initialize()

# Load MODIS data
modis = ee.ImageCollection('MODIS/006/MOD13Q1') \
    .filterDate('2000-01-01', '2020-12-31') \
    .select(['NDVI', 'EVI'])

# Calculate annual means
annual_means = modis.map(lambda img: img.reduceRegions(
    collection=regions,
    reducer=ee.Reducer.mean(),
    scale=250
))

---

## Showcasing Your Work

### Write Blog Posts

Share your research process:

```markdown
# How I Predicted Hospital Readmissions with 85% Accuracy

## The Problem
Hospitals need to identify patients at high risk of readmission...

## The Data
I started with the MIMIC-III dataset, which contains...

## The Process
### Week 1: Exploration
I spent the first week just looking at the data...

[Include code snippets, visualizations, challenges you faced]

### Week 2: Feature Engineering
The breakthrough came when I realized...

### Week 3: Modeling
I tried three different approaches...

## Results
Here's what worked and what didn't...

## Lessons Learned
1. Always validate your assumptions
2. Domain knowledge matters more than fancy algorithms
3. Spend time on feature engineering

## Code
Full code available at: [GitHub link]

<meta name="description" content="Jane Smith - Data Science Researcher specializing in machine learning for healthcare">
<meta name="keywords" content="data science, machine learning, healthcare AI, research">
<meta name="author" content="Jane Smith">