• Carla Feriche
• Pau Sancho
• Pol Serramalera Guerin
• Àlex Gómez Segura

• Project Description
• Questions & Hypotheses
• Dataset
• Workflow
• Organization
• Links

Cookie Flea is a newcomer distribution company in the cookie industry and they are loosing money. The main issues are that they have many cookies types from over 30 different countries, but they have a shortage of a 20% on the best selling cookies and a 45% overstock for the other cookies.

With the use of Machine Learning we want to optimize their supply chain by predicting which cookies will sell better based on their quality. For this analysis we´ve used a dataset with cookies measurements and quality for each cookie type.

The questions that we want to answer with this project are:

• Which are the cookies with the best quality?
• Should we reduce the number of cookies we sell and focus on those that have the best quality?

For this project we used a dataset with 16 columns with cookies measurements and the quality, with over 5000 rows of different types of cookies.

1-Define work 2-Review data set 3-Cleaned the data 4-Analyse data 5-ML Modeling 6-Prepare Presentation

To organize the work we created a Trello project where we listed all the items that we had to complete to deliver the project.

This repository contains a readme document with some basic information about the project, a .gitignore document and a folder with a jupyter noteboks with the code used for the ML project and the dataset.

Trello