Initialize repo, add video, update ReadMe, Adjust submodule 5 excersise2 to include altered instructions to changed instance

Author: LoganColeNIH

.github/PULL_REQUEST_TEMPLATE.md

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+## Summary
+
+<!-- Provide a brief description of the changes you are making. Explain the context and the problem that this PR is solving. -->
+
+## Related Issues
+
+<!-- List any related issues, tickets, or Jira tasks that are addressed by this PR. 
+Use keywords like "Fixes", "Closes", or "Resolves" to automatically link and close issues, e.g., "Fixes #123". -->
+
+## Changes
+
+<!-- Describe the changes in detail. If there are multiple commits, explain each one if necessary.
+Consider breaking this section down into smaller parts, such as "Features Added", "Bugs Fixed", "Technical Debt", etc. -->
+
+- **Feature 1**: Added the ability to do X.
+- **Bug Fix**: Corrected the issue where Y would fail under condition Z.
+- **Refactoring**: Improved the structure of component A without changing its functionality.
+
+## Testing
+
+<!-- Explain how you tested the changes and what steps you took to verify the correctness. 
+Include instructions for others to test if applicable, such as command-line scripts, UI steps, etc. -->
+
+- [ ] Unit tests
+- [ ] Integration tests
+- [ ] Manual testing
+
+### How to test
+
+1. Step 1: [Instruction]
+2. Step 2: [Instruction]
+3. Step 3: [Instruction]
+
+## Screenshots (if applicable)
+
+<!-- Add any relevant screenshots or GIFs to illustrate the changes. This is particularly useful for UI/UX changes. -->
+
+## Checklist
+
+<!-- Ensure that you have completed the following tasks before submitting the PR. -->
+
+- [ ] My code follows the code style of this project.
+- [ ] I have performed a self-review of my code.
+- [ ] I have commented my code, particularly in hard-to-understand areas.
+- [ ] I have made corresponding changes to the documentation.
+- [ ] My changes generate no new warnings or errors.
+- [ ] I have added tests that prove my fix is effective or that my feature works.
+- [ ] New and existing unit tests pass locally with my changes.
+
+## Notes for Reviewers
+
+<!-- Add any additional notes for the reviewers. This could include areas of the code that you would like reviewers to focus on, known issues, or challenges you faced while implementing the changes. -->

.github/workflows/check-links.yaml

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+name: 'Check Links'
+on:
+  push:
+  pull_request:
+  workflow_dispatch:
+
+
+jobs:
+  link_check:
+    name: 'Link Check'
+    uses: NIGMS/NIGMS-Sandbox/.github/workflows/check-links.yaml@main
+    with:
+      repo_link_ignore_list: ""

.github/workflows/notebook-lint.yaml

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+name: 'Lint Notebook'
+on:
+  push:
+  workflow_dispatch:
+permissions:
+  contents: write
+  id-token: write
+
+jobs:
+  lint:
+    name: 'Linting'
+    uses: NIGMS/NIGMS-Sandbox/.github/workflows/notebook-lint.yaml@main
+    with:
+      directory: .

Data/breast-cancer.csv

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+Citation Request:
+   This breast cancer domain was obtained from the University Medical Centre,
+   Institute of Oncology, Ljubljana, Yugoslavia.  Thanks go to M. Zwitter and
+   M. Soklic for providing the data.  Please include this citation if you plan
+   to use this database.
+
+1. Title: Breast cancer data (Michalski has used this)
+
+2. Sources:
+   -- Matjaz Zwitter & Milan Soklic (physicians)
+      Institute of Oncology
+      University Medical Center
+      Ljubljana, Yugoslavia
+   -- Donors: Ming Tan and Jeff Schlimmer ([email protected])
+   -- Date: 11 July 1988
+
+3. Past Usage: (Several: here are some)
+     -- Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The
+        Multi-Purpose Incremental Learning System AQ15 and its Testing
+        Application to Three Medical Domains.  In Proceedings of the
+        Fifth National Conference on Artificial Intelligence, 1041-1045,
+        Philadelphia, PA: Morgan Kaufmann.
+        -- accuracy range: 66%-72%
+     -- Clark,P. & Niblett,T. (1987). Induction in Noisy Domains.  In
+        Progress in Machine Learning (from the Proceedings of the 2nd
+        European Working Session on Learning), 11-30, Bled,
+        Yugoslavia: Sigma Press.
+        -- 8 test results given: 65%-72% accuracy range
+     -- Tan, M., & Eshelman, L. (1988). Using weighted networks to
+        represent classification knowledge in noisy domains.  Proceedings
+        of the Fifth International Conference on Machine Learning, 121-134,
+        Ann Arbor, MI.
+        -- 4 systems tested: accuracy range was 68%-73.5%
+    -- Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A
+       Knowledge-Elicitation Tool for Sophisticated Users.  In I.Bratko
+       & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.
+       -- Assistant-86: 78% accuracy
+
+4. Relevant Information:
+     This is one of three domains provided by the Oncology Institute
+     that has repeatedly appeared in the machine learning literature.
+     (See also lymphography and primary-tumor.)
+
+     This data set includes 201 instances of one class and 85 instances of
+     another class.  The instances are described by 9 attributes, some of
+     which are linear and some are nominal.
+
+5. Number of Instances: 286
+
+6. Number of Attributes: 9 + the class attribute
+
+7. Attribute Information:
+   1. Class: no-recurrence-events, recurrence-events
+   2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
+   3. menopause: lt40, ge40, premeno.
+   4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
+                  45-49, 50-54, 55-59.
+   5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26,
+                 27-29, 30-32, 33-35, 36-39.
+   6. node-caps: yes, no.
+   7. deg-malig: 1, 2, 3.
+   8. breast: left, right.
+   9. breast-quad: left-up, left-low, right-up,	right-low, central.
+  10. irradiat:	yes, no.
+
+8. Missing Attribute Values: (denoted by "?")
+   Attribute #:  Number of instances with missing values:
+   6.             8
+   9.             1.
+
+9. Class Distribution:
+    1. no-recurrence-events: 201 instances
+    2. recurrence-events: 85 instances