https://github.com/tempehs/nesa_course_specifications_linear_regression

A Jupyter Notebook collection designed to support students' understanding of the Linear Regression model defined in the NESA Software Engineering Course Specifications pg 28.
https://github.com/tempehs/nesa_course_specifications_linear_regression

jupyter-notebook linear-regression python

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A Jupyter Notebook collection designed to support students' understanding of the Linear Regression model defined in the NESA Software Engineering Course Specifications pg 28.

• Host: GitHub
• URL: https://github.com/tempehs/nesa_course_specifications_linear_regression
• Owner: TempeHS
• License: other
• Created: 2024-12-05T09:09:31.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-01-26T10:02:58.000Z (26 days ago)
• Last Synced: 2025-01-26T11:18:36.714Z (26 days ago)
• Topics: jupyter-notebook, linear-regression, python
• Language: Jupyter Notebook
• Homepage:
• Size: 712 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# NESA Course Specifications Linear Regression

This Jupyter Notebook collection is designed to support students understand the Linear Regression model defined in the [NESA Software Engineering Course Specifications](https://library.curriculum.nsw.edu.au/341419dc-8ec2-0289-7225-6db7f2d751ef/94e1eb0a-0df7-4dbe-9b72-5d5e0d17143a/software-engineering-11-12-higher-school-certificate-course-specifications.PDF) pg 28.

> [!Note]

> There are some variations from the NESA course specifications to address syntax errors, missing methods and readability.

Several versions have been provided to support students understand the specification and apply it in different contexts. Open these Jupyter Notebooks in [Jupyter Notebook](https://jupyter.org/try-jupyter/lab/), VSCode or Codespaces to modify the code/data and run the code blocks.

> [!Important]

> The configuration for VSCode and Codespaces have been built into this repository.

---

## Demonstrations

0. An introduction to [scikit-learn basics with a focus on the Object Oriented Paradigm](/examples/0.OOP_in_scikit-learn_ML.ipynb).

1. The [Raw Demonstration](/examples/1.raw_course_specification.ipynb) of the course specification provides a direct application of each step of the algorithm.

2. The [Graphical Demonstration](/examples/2.graphical_course_specification.ipynb) of the course specifications provides graphs visualising each step of the algorithm.

3. The [CSV Demonstration](/examples/3.CSV_course_specification.ipynb) of the course specifications uses a CSV upload of the data so larger model training data sets can be used.

4. The [SQL Demonstration](/examples/4.SQL_course_specification.ipynb) of the course specifications imports the data from a SQL database so the data can be managed in a database.

5. The [Model Testing and Validation Demonstration](/examples/5.model_test_and_validate.ipynb) provides a number of ways to evaluate, test and validate your model using a second set of test data.

6. The [Export/Import Demonstration](/examples/6.export_import_course_specification.ipynb) of the course specifications exports the model so a separate Python implementation can use it to make predictions without the training data or dependencies. The demonstration also includes how to save a Matplotlib image so it can be used in a UI.

NESA Course Specifications Linear Regression by Ben Jones is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International