Thenujan Arulnesan

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Thenujan Arulnesan

Mechanical Engineering · University of Moratuwa

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Projects

Certified

Self-directed project

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · AI / ML · August 2026

92/ 100

A ProoV case study · educational project, not employment

Built a working rolling-bearing fault detector end to end: engineered time-domain and envelope-spectrum features, trained a compact depth-2 decision tree on training bearings only, and evaluated it on a held-out set with an 83% catch-rate and 25% false-alarm rate. The memo defended a 0.50 alarm threshold using the candidate’s own numbers and correctly localized the fault to the outer race via dominant BPFO energy.

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 provided envelope_spectrum helper and correctly found BPFO as the dominant defect band
  • Trained a depth-capped decision tree on training bearings only and stated depth 2 with 3 nodes against the embedded budget
  • Reported a held-out confusion summary with both catch-rate (0.83) and false-alarm rate (0.25)
  • Wrote a maintenance memo that ties the threshold choice to concrete operational costs and identifies the outer-race fault
  • Leakage-Free Feature Engineering
  • Honest Held-Out Evaluation
The work I submitted14 tasks

7 tasks · 8 lines · Python · 7 written answers · 344 words

  • Initial Prediction62 characters
  • Time Features143 characters
  • Freq FeaturesPython · 1.2k characters
  • Train Classifier363 characters
  • Evaluation Heldout288 characters
  • Decision Memo1.6k characters
  • Jupyterlite Code2 lines
  • Initial Prediction9 words
  • Time Features10 words
  • Freq Features101 words
  • Train Classifier31 words
  • Evaluation Heldout24 words
  • Decision Memo168 words
  • Jupyterlite Code1 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

Machine Learning for Automotive

AI / ML · August 2026

68/ 100

A ProoV case study · educational project, not employment

Built an end-to-end used-car pricing analysis for VW and Audi listings in the UK market. The work combined exploratory analysis, feature engineering, and model comparison, then used the tuned Random Forest’s strong R², MAE, and RMSE to support a pricing recommendation for a Certified Pre-Owned team. The submission also translated model outputs into business terms, including brand premium and portfolio risk.

Graded against

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

Passed · pass mark 60/100

What stood out6
  • Combined VW and Audi listings into a single analysis frame with a brand indicator and used it consistently in EDA.
  • Compared price distributions, correlations, and scatter relationships to identify the main pricing drivers.
  • Reported concrete model metrics for the tuned Random Forest and contrasted it against a Linear Regression baseline.
  • Connected model accuracy to a realistic CPO pricing decision and quantified portfolio-level mispricing risk.
  • Exploratory Data Understanding
  • Feature Engineering for Price Prediction
The work I submitted10 tasks

5 tasks · 6 lines · 5 written answers · 176 words

  • VW Sprint1 Complete56 characters
  • VW Sprint2 Complete61 characters
  • VW Sprint3 Complete59 characters
  • VW Business Insights1.4k characters
  • Jupyterlite Code2 lines
  • VW Sprint1 Complete1 word
  • VW Sprint2 Complete1 word
  • VW Sprint3 Complete1 word
  • VW Business Insights172 words
  • Jupyterlite Code1 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
2Projects completed
2Verified certificates
80Average score
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