AI 221: Classical Machine Learning

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── README.md          <- The top-level README for developers using this project
├── data
│   ├── external       <- Data from third-party sources
│   ├── interim        <- Intermediate data that has been transformed
│   ├── processed      <- The final, canonical data sets for modeling
│   └── raw            <- The original, immutable data dump
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
└── src                         <- Source code for this project
    │
    ├── __init__.py             <- Makes src a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    │
    ├── modeling
    │   ├── __init__.py
    │   ├── predict.py          <- Code to run model inference with trained models
    │   └── train.py            <- Code to train models
    │
    ├── plots.py                <- Code to create visualizations
    │
    └── services                <- Service classes to connect with external platforms, tools, or APIs
        └── __init__.py

Setup & Reproduction

To ensure consistency and reproducibility, this project uses a dedicated Python virtual environment. Follow these steps to set it up:

1. Clone the repository

git clone https://github.com/rosalierazonable/PhD-DS-AI-Classical-Machine-Learning.git
cd PhD-DS-AI-Classical-Machine-Learning

2. Create and activate a Conda environment:

conda create -n trafficAccident-env python=3.12 -y
conda activate trafficAccident-env

3. Install Required Packages:

pip install -r requirements.txt

4. Register the kernel with Jupyter:

    python -m ipykernel install --user --name trafficAccident-env --display-name "Python (trafficAccident-env)"

5. Open and run the notebooks

Open the project folder in VS Code Open Machine_Exercise_1a.ipynb or Machine_Exercise_1b.ipynb Select the Python (trafficAccident-env) kernel from the kernel picker (top-right) Run via Kernel → Restart & Run All

Note: No manual data download required. Both notebooks fetch datasets automatically via ucimlrepo:

Machine_Exercise_1a.ipynb — Energy Efficiency dataset (UCI id=242)
Machine_Exercise_1b.ipynb — Wisconsin Breast Cancer dataset (UCI id=17)