Cafe-Insomnia-Prediction-Models

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Installation
3. Usage
4. Contributing
5. License
6. Contact
7. Acknowledgments

About The Project:

• This project deals with a hypothetical case study of a café named Insomnia that is located on the University of Sydney campus and that only opens at night.
• The café owner assigned a consultant to conduct an EDA analysis to investigate the possibility of establishing predictive models for this data.
• Our goal in this case study was to improve on the consultant’s work and then to create prediction models to predict coffee sales on an hourly basis.
• The “Graphs” folder contains all the figures that were used in the main report in a png format.
• The “data” folder contains the consultant’s code named “code.ipynb”, the data dictionary named “data_dictionary.txt” and finally the CSV file that contains the raw data which is named “transactions.csv”.
• The main report file is presented in two formats, a pdf in “Café Insomnia Report.pdf” and a Jupyter Notebook format in “Café Insomnia Code.ipynb”.

Built With:

This report was built using Python language through Jupyter Notebook and the extracted data comes from a CSV file.

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Getting Started:

Installation:

To get the report up and running, please follow these simple steps:

1. First, fork the project to your repository.
2. Open the PDF file named “Café Insomnia Report.pdf”.
3. To open the code, download the file “Café Insomnia Code.ipynb”.

To view the excel sheet that contains the data, please follow the instructions:

1. First, fork the project to your repository.
2. Download the excel file named “transactions.csv “provided in the “data” folder.
3. Open the file using excel or another applicable program.

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Usage:

This project could be useful for beginners who are looking for a guide to exploratory data analysis or predictive modelling. This project make use of two types of predictive modelling, in specific, they are, multiple linear regression models and polynomial regression models.

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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. If you have a suggestion that would make this better, please fork the repo and create a pull request. Don't forget to give the project a star! Thanks again!

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License:

Distributed under the Apache-2.0 license. See LICENSE.txt for more information.

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Contact:

My Name – Shehab Shahin

Email: shahin.shehab21@gmail.com

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Acknowledgments:

• Choose an Open Source License
• GitHub Emoji Cheat Sheet

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