tzhern / COMP30027-Project-2

The goal of this project is to build and critically analyse supervised Machine Learning methods to predict the cooking time for recipes based on their steps, ingredients and other features.

The University of Melbourne

COMP30027 (Machine Learning) Project 2

Note: This is just the copy of the original project repository, the original project repository is kept private and is available upon request.

• Student Name: Zhi Hern Tom
• Due Date: Wednesday 26th of May 2021 5:00:00 pm(AEST)
• Report link: Click here to view

Table of contents

• Introduction
• Technologies
• Instruction

Introduction

The goal of this project is to build and critically analyse supervised Machine Learning methods, to predict the cooking time for recipes based on their steps, ingredients and other features. The cooking time of a recipe has been categorised into three classes, corresponding to quick, medium and slow. The datasets can be found in the folder "datasets". The data has been collected from Food.com (formerly GeniusKitchen). The "test" folder is just for drafting and should be ignored.

Technologies

1. pandas
2. numpy
3. sk-learn
4. matplotlib

Instruction

1. Open and run project-2.ipynb file
2. After running the file, you can find the prediction under the folder "output"