Classifying data from hybrid, fuel only and electric vehicles
Clone the repo
git clone https://github.com/lfunderburk/fuel-electric-hybrid-vehicle-ml.git
cd fuel-electric-hybrid-vehicle-ml
Ensure you have Docker installed
docker build -t my_pipeline .
docker run -it --rm -p 5000:5000 my_pipeline
Create and activate a virtual environment
conda create --name mlenv python==3.10
conda activate mlenv
Install dependencies
pip install -r requrements.txt
From command line at the project root directory level
ploomber build
This command will execute the following data pipeline
tasks:
- source: src/data/data_extraction.py
product:
nb: notebooks/data_extraction.ipynb
- source: src/models/train_model.py
product:
nb: notebooks/train_model.ipynb
- source: src/models/predict_model.py
product:
nb: notebooks/predict_model.ipynb
Sample output
name Ran? Elapsed (s) Percentage
--------------- ------ ------------- ------------
data_extraction True 13.0449 13.2142
train_model True 39.0849 39.5921
predict_model True 46.5889 47.1936
From command line at the project root directory level
pytest
-
This application consists of a Dash app with a dashboard that allows the user to visualize trends in different kinds of vehicles and consumer trends with a time component.
-
The data pipeline is scheduled to refresh and retrain the model in batches, and saves the model's results to a database/api for easier retrieval.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── 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.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── 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
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience