https://github.com/dibahk/culinaryvision-global-dish-classifier-predictor

Mini Project for ECS7020P module.
https://github.com/dibahk/culinaryvision-global-dish-classifier-predictor

Last synced: about 1 month ago
JSON representation

Mini Project for ECS7020P module.

• Host: GitHub
• URL: https://github.com/dibahk/culinaryvision-global-dish-classifier-predictor
• Owner: dibahk
• Created: 2023-12-24T09:35:09.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-01T10:57:36.000Z (11 months ago)
• Last Synced: 2024-02-02T11:59:21.588Z (11 months ago)
• Language: Jupyter Notebook
• Size: 1.71 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# CulinaryVision | Global Dish Classifier & Predictor

Mini Project for ECS7020P module.

In this project, the yummy dataset provided by ECS7020P students has been used, and it consists of two parts where each tackles a different problem. 

## Basic Component

Demonstrated skills in image classification and predictive modeling within the culinary context by implementing algorithms such as Decision tree, Gradient Boosting, Random Forest Classifier, Balanced SVM, and SVM, and in conclusion, Random Forest Classifier has a test accuracy of 84%.

## Advanced Component

In the advanced problem, it was predicted whether a dish is Italian, American, or Chinese based on the ingredients used in it.

Implementing Gradient Boosting tree, SVM, Linear SVC, Random Forest Classifier, KNN, and logistic regression and coming to the conclusion to use KNN with 86.43% test accuracy.