Ml patch 2

[PaskalSunari]

No description provided.

[leestott]

@PaskalSunari please ensure you complete the pull request template so we can validate what the request changes are?

[Copilot]

Pull Request Overview

This PR implements a comprehensive machine learning curriculum covering cuisine classification, UFO sighting analysis, and pumpkin price regression. The changes focus on developing practical ML models and web applications to demonstrate real-world applications of classification and regression techniques.

• Adds complete cuisine classifier with ONNX model export for web deployment
• Implements UFO sighting prediction system using logistic regression with pickle serialization
• Creates pumpkin price regression analysis with data visualization and preparation

Reviewed Changes

Copilot reviewed 11 out of 18 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
4-Classification/4-Applied/notebook.ipynb	Complete notebook implementing SVM cuisine classifier with ONNX model export for web deployment
4-Classification/4-Applied/index.html	Web interface for cuisine recommendation with ingredient selection checkboxes
4-Classification/4-Applied/CouisineMatcher.js	JavaScript implementation handling user input and ONNX model inference for cuisine prediction
4-Classification/4-Applied/styles.css	CSS styling for the web application interface
4-Classification/3-Classifiers-2/notebook.ipynb	Comparative analysis of multiple classification algorithms (SVM, KNN, Random Forest, AdaBoost)
4-Classification/2-Classifiers-1/notebook.ipynb	Logistic regression implementation for cuisine classification with probability analysis
3-Web-App/1-Web-App/notebook.ipynb	UFO sighting prediction model using logistic regression with confusion matrix analysis
3-Web-App/1-Web-App/solution/notebook.ipynb	Enhanced UFO prediction model with improved accuracy and pickle model serialization
2-Regression/2-Data/notebook.ipynb	Pumpkin price dataset preparation and exploratory data analysis with visualization
2-Regression/1-Tools/notebook.ipynb	Introduction to NumPy arrays and machine learning tools setup

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[Copilot]

Variable name should be 'cuisines_features_df' to correctly spell 'cuisines'.

[leestott]

@PaskalSunari please fix

[Copilot]

Pull Request Overview

Copilot reviewed 11 out of 18 changed files in this pull request and generated 3 comments.

Comments suppressed due to low confidence (2)

3-Web-App/1-Web-App/solution/notebook.ipynb:1

• There's a stray 'z' character at the end of the comment.

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2-Regression/2-Data/notebook.ipynb:1

• Variable name contains a typo: 'pumkinns' should be 'pumpkins'.

{

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[Copilot]

The number 380 should be defined as a named constant (e.g., const TOTAL_INGREDIENTS = 380) to improve code maintainability and make the purpose clearer.

[leestott]

@PaskalSunari please fix

[Copilot]

Pull Request Overview

Copilot reviewed 11 out of 18 changed files in this pull request and generated 2 comments.

Comments suppressed due to low confidence (1)

2-Regression/2-Data/notebook.ipynb:1

• Variable name 'new_pumkinns' appears to be a typo, should likely be 'new_pumpkins' to match the DataFrame created earlier.

{

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[Copilot]

Corrected spelling of 'Couisine' to 'Cuisine' in filename.

[leestott]

@PaskalSunari please fix