Metadata-Version: 1.1
Name: retail_sales_prediction
Version: 0.1.0
Summary: Python project to predict the sales of retail stores with machine learning
Home-page: https://github.com/amjadraza/retail_sales_prediction
Author: MA RAZA
Author-email: amjadraza24@gmail.com
License: MIT license
Description: =======================
        retail-sales-prediction
        =======================
        
        
        .. image:: https://img.shields.io/pypi/v/retail_sales_prediction.svg
                :target: https://pypi.python.org/pypi/retail_sales_prediction
        
        .. image:: https://travis-ci.com/amjadraza/retail-sales-prediction.svg?branch=master
            :target: https://travis-ci.com/amjadraza/retail-sales-prediction
        
        .. image:: https://readthedocs.org/projects/retail-sales-prediction/badge/?version=latest
                :target: https://retail-sales-prediction.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        Python project to predict the sales of retail stores with machine learning. This project is
        based on the data provided in Kaggle Competition.
        
        Kaggle Link : https://www.kaggle.com/c/favorita-grocery-sales-forecasting/
        
        
        * Free software: MIT license
        * Documentation: https://retail-sales-prediction.readthedocs.io.
        
        Project Environment
        -------------------
        
        We create the project environment using below command.
        
        ``conda env create -f environment.yml -p ./venv``
        
        Update the existing conda environment
        
        ``conda env update -f environment.yml -p ./venv``
        
        Activate the environment
        
        ``conda activate ./venv``
        
        
        Features
        --------
        Machine learning pipeline to predict the sale forecasting. This project is the sand box
        and needs a bit of work to complete it.
        
        Currently it supports below features
        
        * Running the Light GBM Model with fixed training, validation and test sets.
        * Two variants of how unit_sales are filled NA. More can be added
        * Notebooks with Exploratory data analysis
        * Notebooks with Feature engineering and Model Training
        * Documentation using Sphnix
        
        
        TODO
        ----
        
        Project Slides
        ==============
        
        You can view the  project slides of my project at using this link_
        
        .. _link: https://www.slideshare.net/AmjadRaza3/a-presentation-for-retail-sales-projects
        
        
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        =======
        History
        =======
        
        0.1.0 (2019-08-29)
        ------------------
        
        * First release on PyPI.
        
Keywords: retail_sales_prediction
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
