AI Slop Code Risk Monitor

DataTalksClub/machine-learning-zoomcamp

Name: DataTalksClub/machine-learning-zoomcamp — Jupyter Notebook Code Quality & Security Review | AI Slop Code Risk Monitor
Creator: AI Slop Code Risk Monitor

Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

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1.1 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for DataTalksClub/machine-learning-zoomcamp, a Jupyter Notebook project with 14,893 GitHub stars. SynthScan examined 51,064 lines of code across 465 source files, recording 46 pattern matches distributed across 7 syntactic categories. The legacy adjusted pattern signal is 1.1 (Low pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

1.1

Adjusted Signal

1.1

Raw Pattern Score

100%

Time Factor

2026-10-26

Last Push

14.9K

Stars

Jupyter Notebook

Language

51.1K

Lines of Code

465

Files

Pattern Hits

2026-10-31

Scan Date

0.00

HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 3.6/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 30 finding(s).

Hallucinated Dependency & Supply Chain Risk 1.4/100 · LOW

Are imports declared and package/API names verified? 14 finding(s).

Agent Over-Engineering Ratio 2.1/100 · LOW

Are wrappers, guards, and repeated structures necessary? 21 finding(s).

PR Review Velocity Impact 4.6/100 · LOW

Which changes need focused human review? 46 finding(s).

What These Metrics Mean

Adjusted Signal: Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score: Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor: A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits: Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate: High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files: Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 0MEDIUM 1LOW 45

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 46 distinct pattern matches across 7 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Structural Annotation Overuse9 hits · 16 pts

Severity	File	Line	Snippet	Context
LOW	01-intro/02-ml-vs-rules.md	46	### Step 1: Get data	COMMENT
LOW	01-intro/02-ml-vs-rules.md	50	### Step 2: Define and calculate features	COMMENT
LOW	01-intro/02-ml-vs-rules.md	70	### Step 3: Train and use the model	COMMENT
LOW	01-intro/04-crisp-dm.md	29	## Step 1: Business understanding	COMMENT
LOW	01-intro/04-crisp-dm.md	41	## Step 2: Data understanding	COMMENT
LOW	01-intro/04-crisp-dm.md	53	## Step 3: Data preparation	COMMENT
LOW⚡	01-intro/04-crisp-dm.md	71	## Step 4: Modeling	COMMENT
LOW⚡	01-intro/04-crisp-dm.md	79	## Step 5: Evaluation	COMMENT
LOW⚡	01-intro/04-crisp-dm.md	85	## Step 6: Deployment	COMMENT

Unused Imports14 hits · 14 pts

Severity	File	Line	Context
LOW	06-trees/images/2026-09-08-semantic-plot-repair.py	14	CODE
LOW	10-kubernetes/code/gateway.py	7	CODE
LOW	11-kserve/code/churn/churn-train.py	4	CODE
LOW	04-evaluation/images/2026-09-09-roc-01-03-render.py	8	CODE
LOW	04-evaluation/images/2026-09-09-roc-plot-render.py	9	CODE
LOW	…orts/2026/homework/08-deep-learning/reference_train.py	1	CODE
LOW	cohorts/2026/homework/09-serverless/smoke_test.py	1	CODE
LOW	cohorts/2026/homework/09-serverless/lambda_function.py	1	CODE
LOW	cohorts/2026/homework/05-deployment/smoke_test.py	1	CODE
LOW	cohorts/2026/homework/05-deployment/predict.py	1	CODE
LOW	cohorts/2026/homework/05-deployment/model.py	1	CODE
LOW	cohorts/2026/homework/05-deployment/q6_test.py	1	CODE
LOW	cohorts/2026/homework/05-deployment/train.py	1	CODE
LOW	cohorts/2026/data/validate_homework.py	9	CODE

Hyper-Verbose Identifiers11 hits · 11 pts

Severity	File	Line	Snippet	Context
LOW	08-deep-learning/pytorch/README.md	546	def train_and_evaluate_with_checkpointing(model, optimizer, train_loader, val_loader, criterion, num_epochs, device):	CODE
LOW	01-intro/08-linear-algebra.md	76	def vector_vector_multiplication(u, v):	CODE
LOW	01-intro/08-linear-algebra.md	135	def matrix_vector_multiplication(U, v):	CODE
LOW	01-intro/08-linear-algebra.md	189	def matrix_matrix_multiplication(U, V):	CODE
LOW	01-intro/08-linear-algebra.md	335	def vector_vector_multiplication(u, v):	CODE
LOW	01-intro/08-linear-algebra.md	351	def matrix_vector_multiplication(U, v):	CODE
LOW	01-intro/08-linear-algebra.md	367	def matrix_matrix_multiplication(U, V):	CODE
LOW	…orts/2026/homework/08-deep-learning/reference_train.py	60	def augmented_train_transform():	CODE
LOW	cohorts/2026/data/validate_homework.py	68	def split_with_lecture_shuffle(df: pd.DataFrame, seed: int):	CODE
LOW	.tmp/ml-intro-rollout-08-10.md	33	- Invariants: `def vector_vector_multiplication(u, v)`; shape assertion; loop `for i in range(n)`; accumulation `result	CODE
LOW	02-regression/13-regularization.md	154	def train_linear_regression_reg(X, y, r=0.001):	CODE

AI Structural Patterns8 hits · 8 pts

Severity	File	Line	Context
LOW	10-kubernetes/code/gateway.py	61	CODE
LOW	cohorts/2022/09-serverless/homework/homework.py	70	CODE
LOW	cohorts/2025/05-deployment/homework/q6_predict.py	22	CODE
LOW	cohorts/2025/05-deployment/homework/q4_predict.py	22	CODE
LOW	cohorts/2021/09-serverless/homework/homework.py	70	CODE
LOW	09-serverless/workshop/lambda-onnx/lambda_function.py	65	CODE
LOW	09-serverless/workshop/lambda-keras/lambda_function.py	56	CODE
LOW	09-serverless/code/lambda_function.py	48	CODE

Excessive Try-Catch Wrapping2 hits · 3 pts

Severity	File	Line	Snippet	Context
LOW	10-kubernetes/workshop/load_test.py	17	except Exception as e:	CODE
MEDIUM	10-kubernetes/workshop/load_test.py	13	def send_request(_):	CODE

Example Usage Blocks1 hit · 2 pts

Severity	File	Line	Snippet	Context
LOW	…evaluation/images/2026-09-09-precision-recall-crops.sh	10	# Usage:	COMMENT

Over-Commented Block1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	…evaluation/images/2026-09-09-precision-recall-crops.sh	1	#!/usr/bin/env bash	COMMENT