Skip to main content

Sales forecasting model based on date features

Project description

sales_forecasting

Sales forecasting model based on date features

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience 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 mkdocs project; see www.mkdocs.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`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         sales_prediction and configuration for tools like black
│
├── 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.cfg          <- Configuration file for flake8
│
└── sales_prediction   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes sales_prediction 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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sales_prediction-0.0.1.tar.gz (2.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sales_prediction-0.0.1-py3-none-any.whl (2.2 kB view details)

Uploaded Python 3

File details

Details for the file sales_prediction-0.0.1.tar.gz.

File metadata

  • Download URL: sales_prediction-0.0.1.tar.gz
  • Upload date:
  • Size: 2.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.2

File hashes

Hashes for sales_prediction-0.0.1.tar.gz
Algorithm Hash digest
SHA256 1735683e1225208edbfade9bb6f87f0034dfa0160c633052f1b63122d9f2e0aa
MD5 653e94bf421ddad8844867ca35facaf5
BLAKE2b-256 e026800bbd5f94c346eabe6873d86200607c6d425448b82e902daea230bd060a

See more details on using hashes here.

File details

Details for the file sales_prediction-0.0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for sales_prediction-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 4d68eedaaad93d93f92a362561c88b89c563eb90b6ae48a667e573ed325feaad
MD5 3b5f376badb1597bb57e3dfe2e9c6ddd
BLAKE2b-256 6adae89199d0b4efb60ae0174cc3f725d31ec1973ca3c12f0bbf0578218582c7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page