**Overall Verdict:** Accept — The task is complete, objectively verifiable, and reachable, with only non-blocking reference-quality improvements remaining.

**Completeness Check:**
- Instruction: Complete — The workflow, preprocessing rules, model parameters, outputs, and validation requirements are explicit and internally consistent.
- Inputs: Complete — The numeric 294-row, 14-column input is readable, structurally valid, and matches the stated checksum, schema, sentinel counts, target classes, and conditional records.
- Toolchain: Complete — The declared open-source Python stack is cross-platform, offline-capable, and suitable for the supplied code.
- Reference output: Complete — The notebook and cleaned CSV reproduce the specified numerical results, tables, and plots. The notebook provides an interpretation guide rather than the requested finished narrative, but the verification contract states every required conclusion explicitly.
- Rubric: Complete — Exact expected results, tolerances, fold scores, assertions, and qualitative interpretation criteria support objective evaluation.

**File and Task Reachability:**
Attachment classification: `reachable`. Task classification: `reachable`. All three manifest attachments were inspected: `heart_risk.csv`, `Heart_Risk_Classification_Pipeline.ipynb`, and `Heart_Risk_Cleaned.csv`. Both CSVs are parseable 294 × 14 numeric tables; their checksums match the verification contract, the cleaned target matches the source exactly, and the cleaned predictors contain no sentinel, missing, or nonfinite values. The notebook is valid notebook JSON with 15 code cells, 11 markdown cells, sequential recorded execution, no error outputs, and five readable embedded plots. Its recorded tables match the gold cleaning statistics, ranking, fold scores, summary, and correlations. No attachment content remained unreviewed. The notebook was statically inspected rather than re-executed because uploaded code cannot be executed during review; its relative paths, offline dependencies, and recorded run provide a credible end-to-end path on an ordinary single machine. License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.

**Special Screening:**
`public_benchmark` — The distinctive 294-record schema and representative row sequence identify the processed Hungarian Heart Disease benchmark with recoded missing values. The custom preprocessing contract, protected reference outputs, and offline workflow mitigate live answer lookup, although canonical input data may be represented in model training corpora.

**Recommended Changes:**
The numerical implementation and evaluation evidence are strong. No material changes are required. As non-blocking reference-quality improvements, replace the notebook’s interpretation guide with the actual required conclusions and record a validation run under the declared Python 3.11-era stack rather than the newer runtime shown in notebook metadata.