Harsh Gaikwad — Portfolio

Projects

Certified

Self-directed project

Business Analytics Sprint

Data Science · October 2026

84/ 100

A ProoV case study · educational project, not employment

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.

Graded against

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

Passed · pass mark 60/100

What stood out6

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

The work I submitted19 tasks

11 tasks · 8.2k characters · 8 written answers · 418 words

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

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

Fundamentals of Cybersecurity

Cybersecurity · October 2026

92/ 100

A ProoV case study · educational project, not employment

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.

Graded against

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

Passed · pass mark 60/100

What stood out6

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

The work I submitted18 tasks

10 tasks · 4.8k characters · 8 written answers · 270 words

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

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Software Engineering · October 2026

96/ 100

Based on a public IBM case study · not affiliated with IBM

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.

Graded against

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

Passed · pass mark 60/100

What stood out6

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

The deliverable

View the code45harsh16 / proov-io-weather-fix

The work I submitted20 tasks

12 tasks · 1.9k characters · 8 written answers · 90 words

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

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

Energy and Renewable: Machine Learning for the Power Grid

Junior Machine Learning Engineer · AI / ML · October 2026

88/ 100

A ProoV case study · educational project, not employment

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.

Graded against

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

Passed · pass mark 60/100

What stood out6

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

The work I submitted20 tasks

12 tasks · 13 lines · Python, JavaScript · 8 written answers · 467 words

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

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · AI / ML · July 2026

78/ 100

A ProoV case study · educational project, not employment

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.

Graded against

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%

Passed · pass mark 60/100

What stood out6

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

The work I submitted18 tasks

10 tasks · 7.1k characters · Python · 8 written answers · 495 words

Initial Prediction62 characters
Example Load Plot HealthyPython · 952 characters
Load Plot Signal CodePython · 1.8k characters
Load Plot Signal205 characters
Example Rms WorkedPython · 697 characters
Time Features143 characters
Freq FeaturesPython · 1.1k characters
Train Classifier363 characters
Evaluation Heldout288 characters
Decision Memo1.6k characters
Initial Prediction9 words
Example Load Plot Healthy90 words
Load Plot Signal Code163 words
Load Plot Signal15 words
Example Rms Worked72 words
Time Features10 words
Freq Features105 words
Train Classifier31 words

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV

Certified

Self-directed project

Frontend Engineering with Prediction Game

Junior Front-End Engineer · Software Engineering · July 2026

92/ 100

A ProoV case study · educational project, not employment

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.

Graded against

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

Passed · pass mark 60/100

What stood out6

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

The work I submitted16 tasks

8 tasks · 11 lines · JavaScript · 8 written answers · 578 words

Lab Orientation667 characters
Act1 Fixtures FormJavaScript · 2 lines
Act2 Scoring FunctionJavaScript · 2.2k characters
Act3 Compute Strength108 characters
Act3 House Bot108 characters
Act4 LeaderboardJavaScript · 2 lines
Act5 Assemble2 lines
Final Submission97 characters
Lab Orientation65 words
Act1 Fixtures Form143 words
Act2 Scoring Function121 words
Act3 Compute Strength1 word
Act3 House Bot1 word
Act4 Leaderboard138 words
Act5 Assemble108 words
Final Submission1 word

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV