Shubham Ahuja — Portfolio

Projekte

Zertifiziert

Selbstgesteuertes Projekt

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · KI / ML · Juli 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end rolling-bearing fault detector from vibration data: visualized the signal, engineered RMS/kurtosis/crest-factor and envelope-spectrum features at the supplied defect frequencies, localized the fault to the outer race via BPFO, and trained a depth-capped decision tree on training bearings only. Evaluated on a held-out set with catch-rate and false-alarm rate, then justified a 0.50 alarm threshold using explicit miss-vs-inspection costs in a maintenance memo.

Bewertet nach

Feature engineering (signal to honest numbers)25%
Held-out evaluation (honest measurement)25%
Cost-based threshold decision + memo25%
Compact model + embedded-budget justification15%
Plain-language explanation10%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly found BPFO as the dominant envelope-spectrum band and mapped it to the outer race.
Reported a compact tree with stated depth 1 and 3 nodes, aligned with the embedded-budget constraint.
Used held-out metrics with both catch-rate and false-alarm rate, then tied the threshold to explicit miss and false-alarm costs.
Wrote a maintenance memo that names the fault location and explains the tradeoff in plain language using the student's own results.
Leakage-Free Feature Engineering
Honest Held-Out Evaluation

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 7.1k Zeichen · Python · 8 Textantworten · 470 Wörter

Initial Prediction62 Zeichen
Example Load Plot HealthyPython · 952 Zeichen
Load Plot Signal CodePython · 1.8k Zeichen
Load Plot Signal205 Zeichen
Example Rms WorkedPython · 697 Zeichen
Time Features143 Zeichen
Freq FeaturesPython · 1.0k Zeichen
Fault Location239 Zeichen
Train Classifier363 Zeichen
Evaluation Heldout288 Zeichen
Threshold Decision334 Zeichen
Decision Memo1.1k Zeichen
Initial Prediction9 Wörter
Example Load Plot Healthy90 Wörter
Load Plot Signal Code163 Wörter
Load Plot Signal15 Wörter
Example Rms Worked72 Wörter
Time Features10 Wörter
Freq Features97 Wörter
Fault Location14 Wörter

Bewusst zusammengefasst — der eingereichte Code und die Texte bleiben privat, damit diese Seite nicht als Lösungsvorlage dient. Die vollständige Einreichung steht hinter dem verifizierten Zertifikat.

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Machine Learning for Automotive Safety

Perception Validation Engineer (ADAS Safety Sign-off) · KI / ML · Juli 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a perception validation dossier end to end for a fictional Aurora-7 detector: defined the IoU-based detection rule, computed precision/recall and VRU false-negative rate from a synthetic scored table, and sliced recall by lighting, visibility, and class to surface the highest-risk night and occlusion failures. Closed the loop with ISO 21448 SOTIF chains, a condition-limited ship recommendation, and a real-world comparison to the Uber Tempe crash.

Bewertet nach

Detection metrics correctness (precision, recall, VRU false-negative rate)30%
Failure-mode analysis & per-condition rigour25%
SOTIF safety case quality (ISO 21448 reasoning)25%
Ship verdict — evidence-based & internally consistent20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly locked the detection rule at IoU >= 0.5 and classified the worked pair as a miss when IoU was 0.111
Used VRU recall and VRU false-negative rate as the safety-critical metric instead of relying on aggregate accuracy
Exposed the collapse in night and occluded slices, with the worst combined slice at 0.27 recall
Wrote mitigations that are concrete ODD restrictions and fallback behaviors rather than vague monitoring language
Leakage-Free Detection Definition
Honest Safety Metric Evaluation

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 16.3k Zeichen · Python · 8 Textantworten · 687 Wörter

Sensor Match554 Zeichen
Task Definition377 Zeichen
Example Metrics DemoPython · 2.2k Zeichen
Scored Metrics RunPython · 2.3k Zeichen
Scored Metrics351 Zeichen
Ttcs Example SlicePython · 1.3k Zeichen
Ttcs Triage SlicePython · 1.6k Zeichen
Failure Triage1.7k Zeichen
Sotif Case2.7k Zeichen
Realcase Analysis937 Zeichen
Teachback891 Zeichen
Example Iou Demo1.5k Zeichen
Sensor Match27 Wörter
Task Definition42 Wörter
Example Metrics Demo154 Wörter
Scored Metrics Run143 Wörter
Scored Metrics18 Wörter
Ttcs Example Slice143 Wörter
Ttcs Triage Slice112 Wörter
Failure Triage48 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Machine Learning for Automotive

KI / ML · Juni 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

A verified, self-directed industry project — completed and passed a real industry rubric at 92/100 on ProoV.

Bewertet nach

Exploratory Data Analysis20%
Feature Engineering25%
Model Building & Evaluation35%
Business Communication20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Compared Linear Regression and Random Forest using multiple metrics and clearly identified the stronger model.
Interpreted the most important pricing drivers correctly: car age, mileage, and engine size.
Connected model error to business impact with a concrete portfolio-risk estimate.
Provided a practical recommendation for the CPO team rather than only reporting metrics.
Data-Driven Decision Making
Predictive Modeling

Meine eingereichte Arbeit10 Aufgaben

5 Aufgaben · 6 Zeilen · 5 Textantworten · 189 Wörter

VW Sprint1 Complete56 Zeichen
VW Sprint2 Complete61 Zeichen
VW Sprint3 Complete59 Zeichen
VW Business Insights1.6k Zeichen
Jupyterlite Code2 Zeilen
VW Sprint1 Complete1 Wort
VW Sprint2 Complete1 Wort
VW Sprint3 Complete1 Wort
VW Business Insights185 Wörter
Jupyterlite Code1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV