https://github.com/catcoder27/ai-portfolio
Reusable ML scaffold: notebooks, model cards, reports
https://github.com/catcoder27/ai-portfolio
data-science kaggle machine-learning pandas scikit-learn
Last synced: about 2 months ago
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Reusable ML scaffold: notebooks, model cards, reports
- Host: GitHub
- URL: https://github.com/catcoder27/ai-portfolio
- Owner: CatCoder27
- Created: 2025-08-29T15:40:55.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-29T15:42:30.000Z (10 months ago)
- Last Synced: 2025-08-29T18:38:02.280Z (10 months ago)
- Topics: data-science, kaggle, machine-learning, pandas, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI Portfolio (Starter Kit)
A clean starter scaffold to kick off your ML/AI portfolio quickly.
## Structure
```
ai-portfolio/
├── README.md
├── requirements.txt
├── .gitignore
├── notebooks/
│ ├── 00_colab_setup.ipynb
│ ├── 01_eda_template.ipynb
│ └── 02_ml_baseline_template.ipynb
├── src/
│ └── utils.py
├── data/ # put your CSVs here (kept out of git)
├── reports/ # exported figures, tables (kept out of git)
├── checklist/
│ └── phase0_checklist.md
├── model_cards/
│ └── project_model_card_template.md
└── prompts/
└── prompt_library.md
```
## Quickstart (Local)
1) Create a virtual env and install deps:
```
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
```
2) Launch Jupyter:
```
python -m pip install jupyter
jupyter notebook
```
3) Open `notebooks/01_eda_template.ipynb` and set your CSV path (e.g., `data/your_file.csv`).
## Quickstart (Google Colab)
- Open `notebooks/00_colab_setup.ipynb` and run all cells.
- Then open `01_eda_template.ipynb` or `02_ml_baseline_template.ipynb` via Colab (File → Upload).
- Or upload the whole repo to GitHub first, then open notebooks directly in Colab from GitHub.
## Project Template
Each project should include:
- Problem statement
- Data source & license
- Methods (split, metrics, models)
- Results (tables, plots)
- Error analysis & limits
- Next steps
- Model card (use the template in `model_cards/`)
## How to push to GitHub (basic)
```
git init
git add .
git commit -m "Init AI portfolio scaffold"
git branch -M main
git remote add origin
git push -u origin main
```