Tejas Maske

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Tejas Maske

Computer Science · University of Mumbai

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Projects

Certified

Self-directed project

Energy and Renewable: Machine Learning for the Power Grid

Junior Machine Learning Engineer · AI / ML · August 2026

92/ 100

A ProoV case study · educational project, not employment

Built a leakage-aware turbine yield predictor end to end: framed TEY as the target, used only the eight legitimate ambient and turbine sensor inputs, and excluded CO/NOx because they are downstream emissions not available at prediction time. Compared a LinearRegression baseline with a RandomForest on a chronological hold-out set, reported held-out MAE/RMSE/R², and documented limitations, drift monitoring, and deployment use in a concise model card.

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 emissions that would not be available at prediction time
  • Used a time-based split with earlier years for training and later years for testing
  • Reported held-out MAE, RMSE, and R² and interpreted residual behavior instead of only quoting a headline score
  • Wrote a model card that clearly states intended use, limitations, and a concrete monitoring plan
  • Leakage-Free Feature Selection
  • Chronological Hold-Out Evaluation
The work I submitted20 tasks

12 tasks · 13 lines · Python, JavaScript · 8 written answers · 501 words

  • Problem Frame195 characters
  • Leakage Flags113 characters
  • Train Validate BaselinePython · 1.4k characters
  • Train Validate Forest944 characters
  • Tune Evaluate ResidualsPython · 2 lines
  • Residual Read455 characters
  • Train Validate124 characters
  • Tune Evaluate SetupPython · 1.5k characters
  • Tune Evaluate132 characters
  • Drift Monitor94 characters
  • Model Card2.5k characters
  • Teach BackJavaScript · 816 characters
  • Problem Frame14 words
  • Leakage Flags1 word
  • Train Validate Baseline141 words
  • Train Validate Forest76 words
  • Tune Evaluate Residuals92 words
  • Residual Read66 words
  • Train Validate1 word
  • Tune Evaluate Setup110 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
1Project completed
1Verified certificate
92Average score
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