{"id":25441926,"url":"https://github.com/danielwohlr/delivery_time_series","last_synced_at":"2026-05-04T07:32:56.407Z","repository":{"id":277846205,"uuid":"930187035","full_name":"Danielwohlr/delivery_time_series","owner":"Danielwohlr","description":"Time series forecasting of food delivery service 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food_delivery_ts\n\n\u003ca target=\"_blank\" href=\"https://cookiecutter-data-science.drivendata.org/\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter\" /\u003e\n\u003c/a\u003e\n\n\nPresentation is in reports/presentation.pdf.\n\nInstallation instructions: \nBeing in the root of this project, run\n```conda env create -f environment.yml``` in your terminal to create a conda environment.\n\nThen activate it and run, e.g. src/modeling/evaluate.py or src/modeling/qual_analysis.py, for running the experiments.\n\n\n\n## Project Organization\n\n```\n├── README.md          \u003c- The top-level README for developers using this project.\n├── data                \u003c- The original, immutable data dump.\n├── pyproject.toml     \u003c- Project configuration file with package metadata for \n│                         food_delivery_ts and configuration for tools like black\n│\n├── reports            \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n│   └── figures        \u003c- Generated graphics and figures to be used in reporting\n│\n└── src                \u003c- Source code for use in this project.\n    │\n    ├── config.py               \u003c- Store useful variables and configuration\n    │\n    ├── features.py             \u003c- Code to create features for modeling\n    └── plots.py                \u003c- Code to create some visualizations\n    ├── modeling                \n    │   ├── __init__.py \n    │   ├── evaluate.py          \u003c- Compute CrossValidation Losses\n    │   └── qual_analysis.py            \u003c- Qualitative Analysis, e.g. Analysis predicted values, Residual analysis, Feature Importance\n    │\n    ├── models                  \u003c- Modules with all Prediction models definitions                \n        ├── ewm.py              \u003c- benchmark ewm\n        ├── hgbr.py             \u003c- Gradient Boosting Regression Tree\n        ├── regression.py        \u003c- All Implementations of Linear Regression Pipelines\n```\n\n--------\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanielwohlr%2Fdelivery_time_series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanielwohlr%2Fdelivery_time_series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanielwohlr%2Fdelivery_time_series/lists"}