Harsh Gaikwad — Portfolio

Projekte

Zertifiziert

Selbstgesteuertes Projekt

Business Analytics Sprint

Data Science · Oktober 2026

84/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end profit-paradox analysis that framed hypotheses, audited the data, traced the margin collapse to discounting, inflation, and logistics, and translated the findings into a prioritized recovery plan. The work shows strong business reasoning, clear executive communication, and a disciplined approach to turning analysis into action.

Bewertet nach

Problem Framing & Hypotheses10%
Data Analysis & SQL30%
Root-Cause & Forecasting25%
Dashboard & Visualization15%
Executive Communication & Strategy20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Identified the core paradox with specific figures: revenue up while profit and margin fell sharply
Correctly mapped all eight KPI drivers and flagged the data audit issues, including the missing revenue value and duplicate row
Built a coherent recovery narrative that prioritised discount governance, raw-material hedging, and logistics efficiency
Communicated the business case clearly in stakeholder-facing language, including a boardroom-style executive verdict
Structural Hypothesis Formation
KPI Driver Mapping

Meine eingereichte Arbeit19 Aufgaben

11 Aufgaben · 8.2k Zeichen · 8 Textantworten · 418 Wörter

Hypothesis Sheet862 Zeichen
KPI Driver Map174 Zeichen
Data Audit100 Zeichen
Week1 Update945 Zeichen
Sql Category Profit310 Zeichen
Sql Region Join359 Zeichen
Forecast272 Zeichen
Recommendations974 Zeichen
Stakeholder Qa1.0k Zeichen
Executive Summary1.6k Zeichen
Bosch Real Case Study1.6k Zeichen
Hypothesis Sheet104 Wörter
KPI Driver Map1 Wort
Data Audit4 Wörter
Week1 Update130 Wörter
Sql Category Profit32 Wörter
Sql Region Join41 Wörter
Forecast6 Wörter
Recommendations100 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

Fundamentals of Cybersecurity

Cybersicherheit · Oktober 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end incident response workflow for a phishing-led breach: triaged the email indicators, identified anomalous authentication activity, mapped the attack to kill-chain stages, and chose a containment sequence that cut access before remediation. Also drafted a GDPR Art. 33 notification and translated the method to the public Continental LockBit case with a concrete control recommendation grounded in the ~40 TB exfiltration impact.

Bewertet nach

Threat Triage & Detection20%
Log Analysis & Investigation30%
Incident Response & Containment20%
Compliance & Breach Notification15%
Executive Communication & Lessons15%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly identified all four phishing red flags: sender, attachment, urgency, and link
Flagged the anomalous sign-in rows and explicitly caught impossible travel
Sequenced containment to cut access first with disable-account before other actions
Transferred the lesson to the Continental case by choosing application control as the highest-impact control
Leakage-Free Incident Triage
Evidence-Based Log Investigation

Meine eingereichte Arbeit18 Aufgaben

10 Aufgaben · 4.8k Zeichen · 8 Textantworten · 270 Wörter

Phishing Triage186 Zeichen
Killchain Map244 Zeichen
Log Triage190 Zeichen
Ioc Extraction176 Zeichen
Containment Plan373 Zeichen
Breach Notification457 Zeichen
Incident Report1.5k Zeichen
Recommendations648 Zeichen
Real Case Analysis619 Zeichen
Teach Back480 Zeichen
Phishing Triage8 Wörter
Killchain Map12 Wörter
Log Triage9 Wörter
Ioc Extraction8 Wörter
Containment Plan14 Wörter
Breach Notification45 Wörter
Incident Report161 Wörter
Recommendations13 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Softwareentwicklung · Oktober 2026

96/ 100

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

Built and verified a complete legacy-code modernization for a fleet service tool: fixed the wear calculation, handled missing service readings safely, corrected the nightly report, repaired a hidden mileage-conversion bug, and added the missing regression test. Also delivered a data-driven breakdown-risk analysis that identified km_since_service, avg_daily_km, and load_factor as the real predictors while rejecting odometer and age as misleading signals.

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

Caught the quiet fleet_utils.km_to_miles conversion bug that no test directly exposed
Wrote a sensible breakdown-risk analysis that follows the data instead of the mileage assumption
Verified the fix with the provided acceptance script and documented the checks in NOTES.md
Leakage-Free Feature Engineering
Honest Model Evaluation
Directed AI Prompting

Das Ergebnis

Zum Code45harsh16 / proov-io-weather-fix

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 1.9k Zeichen · 8 Textantworten · 90 Wörter

Division Predict70 Zeichen
Diagnose Line130 Zeichen
Diagnose Why195 Zeichen
Scope Prompt66 Zeichen
Audit Findings158 Zeichen
Acceptance Checklist151 Zeichen
Audit Why175 Zeichen
Bob Trial Click74 Zeichen
Bob Ready60 Zeichen
Template Repo Click82 Zeichen
Verify Report71 Zeichen
Exec Pitch685 Zeichen
Division Predict1 Wort
Diagnose Line10 Wörter
Diagnose Why30 Wörter
Scope Prompt1 Wort
Audit Findings1 Wort
Acceptance Checklist21 Wörter
Audit Why25 Wörter
Bob Trial Click1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Energy and Renewable: Machine Learning for the Power Grid

Junior Machine Learning Engineer · KI / ML · Oktober 2026

88/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a leakage-aware turbine yield predictor end to end: framed TEY as the target, excluded downstream CO/NOx emissions, and validated on a genuine time-based holdout rather than a random split. Compared a LinearRegression baseline with a tuned RandomForest, reported held-out MAE/RMSE/R2, checked residual behavior, and documented the model’s intended use, limits, and monitoring plan.

Bewertet nach

Build and validate the model correctly35%
Optimize and evaluate honestly25%
Write the Model Card (clarity + honesty)25%
Explain it to a non-expert15%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly excluded CO and NOx as downstream leakage features and justified why they cannot be used at prediction time.
Compared a LinearRegression baseline against a tuned RandomForest and showed a clear held-out improvement in MAE and R2.
Read residuals and explicitly noted that strong performance on one plant does not imply generalization to other turbines.
Provided a manager-friendly explanation that states what the model predicts, how accurate it is, why the number is trusted, and when it can fail.
Leakage-Free Feature Selection
Time-Based Model Validation

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 13 Zeilen · Python, JavaScript · 8 Textantworten · 467 Wörter

Problem Frame220 Zeichen
Leakage Flags113 Zeichen
Train Validate BaselinePython · 1.4k Zeichen
Train Validate Forest958 Zeichen
Tune Evaluate SetupPython · 1.4k Zeichen
Tune Evaluate ResidualsPython · 2 Zeilen
Tune Evaluate132 Zeichen
Residual Read278 Zeichen
Teach BackJavaScript · 714 Zeichen
Train Validate124 Zeichen
Drift Monitor95 Zeichen
Model Card908 Zeichen
Problem Frame19 Wörter
Leakage Flags1 Wort
Train Validate Baseline143 Wörter
Train Validate Forest77 Wörter
Tune Evaluate Setup99 Wörter
Tune Evaluate Residuals92 Wörter
Tune Evaluate1 Wort
Residual Read35 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · KI / ML · Juli 2026

78/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a compact rolling-bearing fault detector end to end: engineered time-domain and envelope-spectrum features, trained a depth-capped decision tree on training bearings only, and validated it on held-out healthy and faulty bearings. The final memo defended a practical alarm threshold using the candidate’s own catch-rate and false-alarm numbers and translated the dominant defect-frequency result into a maintenance action.

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 supplied defect-frequency geometry and explicitly named BPFO as the dominant band in the memo.
Chose a shallow decision tree and stated depth, node count, and embedded-budget fit.
Reported held-out catch-rate and false-alarm rate and tied the threshold choice to maintenance cost tradeoffs.
Leakage-Free Feature Engineering
Honest Held-Out Evaluation
Physics-Grounded Fault Interpretation

Meine eingereichte Arbeit18 Aufgaben

10 Aufgaben · 7.1k Zeichen · Python · 8 Textantworten · 495 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 · 1.1k Zeichen
Train Classifier363 Zeichen
Evaluation Heldout288 Zeichen
Decision Memo1.6k 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 Features105 Wörter
Train Classifier31 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Frontend Engineering with Prediction Game

Junior Front-End Engineer · Softwareentwicklung · Juli 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a complete World Cup prediction game end to end: rendered the fixture board, scored picks with the exact 5/2/0 rule, computed team attack/defense strengths from real results, and used those strengths to drive a bot predictor. Also persisted and ranked the leaderboard in localStorage and shipped the finished game to a live HTTPS deployment.

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

Implemented the exact 5/2/0 scoring rule and explicitly handled draw-vs-draw cases correctly.
Computed team strengths from the embedded real-results sample instead of hard-coding a lookup.
Built a bot prediction that uses both teams’ strengths through a lambda-based heuristic.
Persisted leaderboard rows to one localStorage key and re-sorted them after reload.
Leakage-Free Feature Engineering
Honest Model Evaluation

Meine eingereichte Arbeit16 Aufgaben

8 Aufgaben · 11 Zeilen · JavaScript · 8 Textantworten · 578 Wörter

Lab Orientation667 Zeichen
Act1 Fixtures FormJavaScript · 2 Zeilen
Act2 Scoring FunctionJavaScript · 2.2k Zeichen
Act3 Compute Strength108 Zeichen
Act3 House Bot108 Zeichen
Act4 LeaderboardJavaScript · 2 Zeilen
Act5 Assemble2 Zeilen
Final Submission97 Zeichen
Lab Orientation65 Wörter
Act1 Fixtures Form143 Wörter
Act2 Scoring Function121 Wörter
Act3 Compute Strength1 Wort
Act3 House Bot1 Wort
Act4 Leaderboard138 Wörter
Act5 Assemble108 Wörter
Final Submission1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV