Clean up README by removing old sections

Removed outdated submission checklist and final notes sections.

Author: letsbegincode

README.md

@@ -103,35 +103,3 @@ This table doubles as a progress tracker; each module will produce intermediate
 4. Montiel, J., Read, J., Bifet, A., & Abdessalem, T. (2021). *River: Machine learning for streaming data in Python*. JMLR.
 
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-> This README will evolve alongside the project. Upcoming additions include experiment trackers, visual dashboards, and links to evaluation reports once submitted.
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-## 8. Submission Checklist & Final Notes
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-### Completion Snapshot
-- **Innovation documented** – Sections 2 and 4 capture the novelty, architectural flow, and justification for fusing iCaRL with ADWIN.
-- **Execution evidence** – Sections 3 and 5 outline the semester roadmap and technical modules delivered, demonstrating sustained work across the term.
-- **Evaluation coverage** – Section 6 itemises mid- and end-semester artefacts so reviewers can trace how outcomes map to assessment criteria.
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-### Self-Audit Before Marking Complete
-| Item | Status | Evidence & Next Actions |
-| --- | --- | --- |
-| Innovation statement & literature synthesis | ✅ Complete | Sections 2 & 7 summarise the novelty and references backing the hybrid approach. |
-| Architecture & workflow documentation | ✅ Complete | Section 4 diagrams the adaptive rehearsal flow used in code proofs. |
-| Implementation roadmap & progress log | ✅ Complete | Section 5 lists each module with planned completion windows to show semester-long effort. |
-| Experimental artefacts (metrics, plots, energy logs) | ⚠️ Attach | Ensure the final notebooks, tables, and detector alarm statistics are included in the repo/report bundle. |
-| Final report & presentation package | ⚠️ Attach | Link the polished PDF/slide deck once uploaded so evaluators can access them directly. |
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-Once the ⚠️ items are uploaded, you can confidently mark the project as completed with clear evidence of originality and sustained semester work.
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-## 9. Final Review & Submission Plan
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-- **Authenticity cross-check** – Revisit notebooks and experiment logs to ensure they reflect the adaptive rehearsal workflow (detector alarms → rehearsal bursts → evaluation) described in Sections 4 and 5. Capture screenshots or metadata hashes where appropriate for the appendix.
-- **Evidence packaging** – Bundle the energy/compute summaries, alarm statistics, and comparison plots referenced in Section 6 so evaluators can validate the claimed efficiency gains without rerunning experiments.
-- **Narrative alignment** – In the written report, mirror the README structure (innovation → roadmap → evaluation) so reviewers immediately see the semester-long progression and novel contribution.
-- **Repository hygiene** – Finalise README links, clean temporary notebooks, and update the submission checklist table once the ⚠️ items are addressed to avoid confusion during marking.
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