molinari135
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Added YAML in README file
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README.md
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# Product Return Prediction
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<a target="_blank" href="https://cookiecutter-data-science.drivendata.org/">
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<img src="https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter" />
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</a>
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---
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title: Product Return Prediction API
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emoji: π
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colorFrom: purple
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colorTo: red
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sdk: static
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pinned: false
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---
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This repository contains a project designed for Armani to analyze past orders and returns, predicting which products are likely to be returned and when. The system supports logistics, product management, and marketing teams by providing actionable insights to reduce return rates, optimize inventory management, and improve customer satisfaction.
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## Project Organization
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```
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βββ LICENSE <- Open-source license if one is chosen
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βββ Makefile <- Makefile with convenience commands like `make data` or `make train`
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βββ README.md <- The top-level README for developers using this project.
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βββ data
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β βββ external <- Data from third party sources.
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β βββ interim <- Intermediate data that has been transformed.
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β βββ processed <- The final, canonical data sets for modeling.
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β βββ raw <- The original, immutable data dump.
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β
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βββ docs <- A default mkdocs project; see www.mkdocs.org for details
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β
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βββ models <- Trained and serialized models, model predictions, or model summaries
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β
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βββ notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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β the creator's initials, and a short `-` delimited description, e.g.
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β `1.0-jqp-initial-data-exploration`.
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β
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βββ pyproject.toml <- Project configuration file with package metadata for
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β product_return_prediction and configuration for tools like black
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β
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βββ references <- Data dictionaries, manuals, and all other explanatory materials.
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β
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βββ reports <- Generated analysis as HTML, PDF, LaTeX, etc.
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β βββ figures <- Generated graphics and figures to be used in reporting
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β
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βββ requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
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β generated with `pip freeze > requirements.txt`
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β
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βββ setup.cfg <- Configuration file for flake8
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β
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βββ product_return_prediction <- Source code for use in this project.
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β
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βββ __init__.py <- Makes product_return_prediction a Python module
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β
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βββ config.py <- Store useful variables and configuration
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β
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βββ dataset.py <- Scripts to download or generate data
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β
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βββ features.py <- Code to create features for modeling
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β
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βββ modeling
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β βββ __init__.py
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β βββ predict.py <- Code to run model inference with trained models
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β βββ train.py <- Code to train models
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β
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βββ plots.py <- Code to create visualizations
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```
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--------
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