K Nithin Kumar Goud — Portfolio

Projekte

Zertifiziert

Selbstgesteuertes Projekt

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · KI / ML · August 2026

96/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end rolling-bearing fault detector from vibration data: visualized a faulty snapshot, engineered RMS/kurtosis/crest-factor and defect-frequency envelope features, localized the fault to the outer race via BPFO, and trained a tiny depth-capped decision tree on training bearings only. Evaluated on a held-out set with catch-rate and false-alarm metrics, then justified a 0.30 alarm threshold using the candidate’s own cost numbers 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

Used the provided envelope_spectrum() helper and correctly found BPFO as the dominant defect band, mapping it to the outer race.
Reported a compact decision tree with stated depth 1 and 3 nodes, explicitly noting it was trained on training bearings only and within the embedded budget.
Made the threshold decision with concrete operational costs (€20,000 missed failure vs €500 false alarm) and tied the memo to the observed 100% catch-rate and 42% false-alarm rate.
Leakage-Free Feature Engineering
Honest Held-Out Evaluation
Physics-Based Fault Localization

Meine eingereichte Arbeit19 Aufgaben

11 Aufgaben · 12 Zeilen · Python · 8 Textantworten · 401 Wörter

Initial Prediction62 Zeichen
Load Plot Signal CodePython · 1.7k Zeichen
Example Rms WorkedPython · 697 Zeichen
Time Features143 Zeichen
Freq FeaturesPython · 935 Zeichen
Fault Location239 Zeichen
Train Classifier363 Zeichen
Evaluation Heldout288 Zeichen
Threshold Decision334 Zeichen
Decision Memo1.2k Zeichen
Jupyterlite Code2 Zeilen
Initial Prediction9 Wörter
Load Plot Signal Code163 Wörter
Example Rms Worked72 Wörter
Time Features10 Wörter
Freq Features78 Wörter
Fault Location14 Wörter
Train Classifier31 Wörter
Evaluation Heldout24 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

Driving Supply Chain Sustainability via Data Governance

Product Compliance Specialist · Allgemein · August 2026

84/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a three-part battery-passport compliance package for a single cell using the PowerCo Salzgitter case as reference. Classified regulated fields into known, computable, and gap buckets; designed public, legitimate-interest, and restricted passport views; and traced unresolved gaps to the supplier tier that owns them. Also computed the cell’s carbon footprint from code and used the result to identify raw materials as the dominant contributor.

Bewertet nach

Supply-chain gap attribution and supplier requests30%
Judgment under regulatory ambiguity20%
Access-tier design judgment15%
Carbon-footprint methodology and calculation15%
Passport data-model completeness and traceability10%
Internal consistency across the package10%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Defended unsettled passport fields with explicit criteria that would change the classification
Computed the carbon footprint from code and correctly identified raw materials as the dominant stage
Mapped remaining gaps to the supplier tier that actually owns the data
Drafted supplier requests with field names, regulatory basis, deadlines, and batch-level specificity
Leakage-Free Regulatory Classification
Traceable Judgment Under Ambiguity

Meine eingereichte Arbeit2 Aufgaben

1 Aufgabe · 2 Zeilen · 1 Textantwort · 1 Wort

Jupyterlite Code2 Zeilen
Jupyterlite Code1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Softwareentwicklung · August 2026

94/ 100

Basierend auf einer öffentlichen Fallstudie von IBM · nicht mit IBM verbunden

Built and verified a full legacy-service repair for Vossberg Mobility: fixed the wear calculation so nearly-worn cars are flagged, handled missing service readings safely, corrected the fleet report’s average and crash path, and preserved the 15,000 km / 80% business rules. Also added the missing regression test, modernized helper code, and produced a data-driven breakdown-risk analysis that follows the fleet history rather than the naive mileage assumption.

Bewertet nach

Fix and extend the repo with the agent35%
Diagnose the bug20%
Direct and audit the AI agent25%
Explain it like an engineer20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly identified the floor-division bug and explained why 14,900/15,000 became 0% wear
Caught and reverted the agent’s unauthorized threshold change from 80% to 85%
Added the missing regression test for the nearly-worn car and documented the acceptance checks
Wrote NOTES.md with concrete, self-audited findings, including the helper-module mileage conversion bug
Leakage-Free Bug Diagnosis
Verified AI Output Auditing

Das Ergebnis

Zum Codenithingoud78 / km-waechter-fix

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 2.1k Zeichen · 8 Textantworten · 76 Wörter

Bob Trial Click198 Zeichen
Bob Ready60 Zeichen
Diagnose Line130 Zeichen
Diagnose Why163 Zeichen
Scope Prompt66 Zeichen
Audit Findings158 Zeichen
Audit Why119 Zeichen
Acceptance Checklist131 Zeichen
Template Repo Click82 Zeichen
Exec Pitch709 Zeichen
Bob Bonus229 Zeichen
Repo URL51 Zeichen
Bob Trial Click1 Wort
Bob Ready1 Wort
Diagnose Line10 Wörter
Diagnose Why24 Wörter
Scope Prompt1 Wort
Audit Findings1 Wort
Audit Why19 Wörter
Acceptance Checklist19 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Frontend Engineering with Prediction Game

Junior Front-End Engineer · Softwareentwicklung · Juli 2026

84/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a complete World Cup prediction game end to end: rendered the tournament fixtures as interactive pick cards, scored picks with the exact 5/2/0 rubric, derived team attack/defense strengths from real historical results, and used those strengths to generate bot predictions. Also implemented a persistent ranked leaderboard and deployed the finished experience to a live Netlify URL.

Bewertet nach

Scoring engine correctness30%
Prediction model genuinely from real data25%
Game completeness and leaderboard soundness25%
Shipped and shareable: live deployed URL20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Rendered the fixture board with one card per match and two numeric score inputs per card.
Implemented scorePick with correct handling for exact scores, same-result different-score cases, and draws.
Computed team strengths from a real-results sample and used those strengths inside botPredict rather than hard-coding outcomes.
Shipped a live https deployment on Netlify.
Leakage-Free Feature Engineering
Honest Model Evaluation

Meine eingereichte Arbeit18 Aufgaben

10 Aufgaben · 12 Zeilen · JavaScript · 8 Textantworten · 664 Wörter

Lab Orientation677 Zeichen
Example Render FixturesJavaScript · 2.0k Zeichen
Act1 Fixtures FormJavaScript · 3.6k Zeichen
Act2 Scoring FunctionJavaScript · 2.3k Zeichen
Act3 Compute Strength108 Zeichen
Act3 House Bot108 Zeichen
Act4 LeaderboardJavaScript · 3.2k Zeichen
Act5 Assemble2 Zeilen
Final Submission95 Zeichen
Jupyterlite Code2 Zeilen
Lab Orientation66 Wörter
Example Render Fixtures151 Wörter
Act1 Fixtures Form116 Wörter
Act2 Scoring Function113 Wörter
Act3 Compute Strength1 Wort
Act3 House Bot1 Wort
Act4 Leaderboard108 Wörter
Act5 Assemble108 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Amazon Robotics - Black-Friday Fleet Coordination

Softwareentwicklung · Juli 2026

98/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end Amazon Robotics fleet-coordination solution for Black Friday surge conditions: framed the bottleneck as aisle congestion and dock contention, implemented single-agent A* with an admissible Manhattan heuristic, extended it to centralized cooperative planning with vertex and edge reservations, and validated a realistic operating policy in simulation. The final pitch tied zero-collision execution and sustained throughput to a concrete pilot ask for one FC zone during a named surge event.

Bewertet nach

Problem framing & operational stakes20%
Algorithmic depth - A* and cooperative coordination30%
Coordination policy realism - deadlock, charging, idle25%
Throughput trade-off analysis15%
Executive pitch to leadership10%

Bestanden · Bestehensgrenze 55/100

Was herausstach6

Implemented A* with an admissible Manhattan heuristic on a 4-connected grid and showed it outperforming Dijkstra in node expansions.
Built a cooperative reservation table that books both vertices and directed edges, preventing same-cell and head-on swap collisions.
Chose a realistic fleet policy: centralized coordination, priority-watchdog deadlock handling, staggered charging, and dispersed perimeter idle parking.
Used simulator outputs to identify a concave throughput curve and locked a specific sweet spot fleet size of 36 robots.
Leakage-Free Multi-Agent Coordination
Honest Simulation-Based Evaluation

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 7.9k Zeichen · Python · 8 Textantworten · 376 Wörter

Amazon Robotics Context459 Zeichen
Grid World Theory119 Zeichen
State Representation Task ValidatorPython · 1.7k Zeichen
State Representation Task341 Zeichen
Phase1 Recap348 Zeichen
Astar Theory124 Zeichen
Astar Implementation Cell3.7k Zeichen
Astar Solution Correct4 Zeichen
Single Unit Challenge465 Zeichen
Phase2 Recap374 Zeichen
Multi Agent Problem111 Zeichen
Reservation Table Theory180 Zeichen
Amazon Robotics Context37 Wörter
Grid World Theory1 Wort
State Representation Task Validator129 Wörter
State Representation Task1 Wort
Phase1 Recap23 Wörter
Astar Theory1 Wort
Astar Implementation Cell171 Wörter
Single Unit Challenge13 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Machine Learning for Automotive

KI / ML · Juni 2026

84/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

A verified, self-directed industry project — completed and passed a real industry rubric at 84/100 on ProoV.

Bewertet nach

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

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly compared Linear Regression and Random Forest using R², MAE, and RMSE
Identified vehicle age, mileage, and engine size as the top pricing drivers
Translated model performance into a clear CPO business recommendation
Included a quantified financial impact statement to support decision-making
Exploratory Data Analysis
Feature Engineering

Meine eingereichte Arbeit10 Aufgaben

5 Aufgaben · 6 Zeilen · 5 Textantworten · 186 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 Insights182 Wörter
Jupyterlite Code1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV