Sam Dcruze

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Sam Dcruze

Business Administration · University of the People

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Projects

Certified

Self-directed project

Energy and Renewable: Machine Learning for the Power Grid

Junior Machine Learning Engineer · AI / ML · September 2026

92/ 100

A ProoV case study · educational project, not employment

Built a leakage-aware gas-turbine TEY predictor using the eight pre-combustion sensors, with CO and NOx excluded because they are downstream emissions. Validated on a chronological holdout, compared Linear Regression against a Random Forest, and reported honest held-out performance (about 1.5 MWh MAE, ~0.98 R2) alongside residual analysis and a practical 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 documented why
  • Used a time-based split instead of a random shuffle, preserving the integrity of the evaluation
  • Reported held-out MAE, RMSE, and R2, then read the residuals for bias and heteroscedasticity
  • Wrote a model card that names the plant-specific limits, silent-drift risk, and a concrete retraining trigger
  • Leakage-Free Feature Selection
  • Chronological Holdout Validation
The work I submitted20 tasks

12 tasks · 13 lines · Python, JavaScript · 8 written answers · 431 words

  • Problem Frame238 characters
  • Leakage Flags113 characters
  • Train Validate BaselinePython · 1.4k characters
  • Train Validate124 characters
  • Train Validate Forest944 characters
  • Tune Evaluate SetupPython · 1.5k characters
  • Tune Evaluate ResidualsPython · 2 lines
  • Tune Evaluate130 characters
  • Residual Read256 characters
  • Drift Monitor92 characters
  • Model Card1.1k characters
  • Teach BackJavaScript · 736 characters
  • Problem Frame20 words
  • Leakage Flags1 word
  • Train Validate Baseline141 words
  • Train Validate1 word
  • Train Validate Forest77 words
  • Tune Evaluate Setup110 words
  • Tune Evaluate Residuals80 words
  • Tune Evaluate1 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

Driving Supply Chain Sustainability via Data Governance

Product Compliance Specialist · General · September 2026

84/ 100

A ProoV case study · educational project, not employment

Built a complete battery-passport compliance package for a single cell: classified regulated fields as known/compute/gap, designed public/legitimate-interest/restricted passport views, and traced every remaining gap to the supplier tier that owns it. Also computed the cell’s carbon footprint from code, identified raw materials as the dominant emissions source, and drafted actionable supplier requests to close the highest-priority data gaps.

Graded against

  • 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%

Passed · pass mark 60/100

What stood out6
  • Computed the carbon footprint in code and reported the stage breakdown and total (74.05 kg CO2e/kWh) rather than asserting a number.
  • Correctly identified raw materials as the dominant emissions stage and drew the right operational conclusion about upstream sourcing leverage.
  • Built a coherent access-tier split that distinguishes public passport fields from recycler-useful legitimate-interest data and restricted due-diligence records.
  • Drafted supplier requests with named fields, regulatory basis, deadlines, and requested formats, making them actionable for real contacts.
  • Leakage-Free Regulatory Classification
  • Honest Model Evaluation
The work I submitted16 tasks

8 tasks · 8.0k characters · Python · 8 written answers · 582 words

  • Passport Field Defences2.3k characters
  • Passport Field Classification659 characters
  • Carbon Footprint CalculationPython · 1.3k characters
  • Access Tier Design918 characters
  • Supply Chain Gap Audit316 characters
  • Supplier Data Request1.3k characters
  • Passport Verdict Reveal490 characters
  • Final Submission809 characters
  • Passport Field Defences160 words
  • Passport Field Classification1 word
  • Carbon Footprint Calculation118 words
  • Access Tier Design1 word
  • Supply Chain Gap Audit1 word
  • Supplier Data Request142 words
  • Passport Verdict Reveal56 words
  • Final Submission103 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

Business Analytics Sprint

Data Science · September 2026

92/ 100

A ProoV case study · educational project, not employment

Built an end-to-end profit-paradox analysis: framed hypotheses and KPI drivers, audited the data, wrote correct SQL for category and regional margin analysis, isolated the main profit drivers, and translated the findings into a board-ready recovery plan. The work is especially strong in connecting technical analysis to executive decisions, with a clear forecast and prioritized actions focused on margin repair.

Graded against

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

Passed · pass mark 60/100

What stood out6
  • Wrote both SQL queries with the correct margin formula and appropriate GROUP BY/JOIN logic
  • Correctly identified the main profit drivers as raw-material inflation, logistics, and discounting
  • Produced a coherent forecast and tied it to prioritized recovery actions
  • Communicated the story clearly across memo, deck, stakeholder Q&A, and executive summary
  • Structured Hypothesis Formation
  • Leakage-Free SQL and Margin Analysis
The work I submitted20 tasks

12 tasks · 5.9k characters · 8 written answers · 301 words

  • Hypothesis Sheet1.1k characters
  • KPI Driver Map174 characters
  • Data Audit100 characters
  • Week1 Update693 characters
  • Sql Category Profit354 characters
  • Sql Region Join491 characters
  • Root Causes158 characters
  • Forecast276 characters
  • AI Reflection617 characters
  • Recommendations693 characters
  • Boardroom Deck676 characters
  • Stakeholder Qa550 characters
  • Hypothesis Sheet129 words
  • KPI Driver Map1 word
  • Data Audit4 words
  • Week1 Update91 words
  • Sql Category Profit27 words
  • Sql Region Join38 words
  • Root Causes5 words
  • Forecast6 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
3Projects completed
3Verified certificates
89Average score
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