Pharmaceutical Sales prediction across multiple stores

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Table of Contents

1. About The Project
   • Introduction
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Contributing
4. License
5. Contact
6. Acknowledgements

About The Project | Introduction

This project aims to forecast sales in all their stores across several cities six weeks ahead of time. It is identified that factors such as promotions, competition, school and state holidays, seasonality, and locality as necessary for predicting the sales across the various stores. Previously recorded data was provided and future sales prediction is made using that data.

Built With

• PYTHON
• Sklearn pipelines
• Deep learning | Tensorflow

Getting Started

This project aims to address the following

• Creation of new features
• Predictive pipeline
• MLOps Techniques
• web app

Prerequisites

The following should be included in the installation

• Pandas
• Matplotlib
• Numpy
• Tensorflow
• Scikit-learn

This is an example of how to list things you need to use the software and how to install them.

• npm

  npm install "package name" -g

Installation

1. Free API, comming soon
2. Clone the repo

   git clone https://github.com/your_username_/Project-Name.git

3. Install NPM packages

   npm install

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Binyam Sisay - binasisayet8790@gmail.com

Project Link: https://github.com/Bina-man/Pharmaceutical-Sales-Prediction

Acknowledgements

• https://github.com/Davidelvis
• https://github.com/daniEL2371/
• Img Shields