DARSHAN PREMRAJ

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DARSHAN PREMRAJ

Data Science and ML · Christ University

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Projects

Certified

Self-directed project

Machine Learning for Automotive Safety

Perception Validation Engineer (ADAS Safety Sign-off) · AI / ML · June 2026

92/ 100

A ProoV case study · educational project, not employment

A verified, self-directed Perception Validation Engineer (ADAS Safety Sign-off) project — completed and passed a real industry rubric at 92/100 on ProoV.

Graded against

  • Detection metrics correctness (precision, recall, VRU false-negative rate)30%
  • Failure-mode analysis & per-condition rigour25%
  • SOTIF safety case quality (ISO 21448 reasoning)25%
  • Ship verdict — evidence-based & internally consistent20%

Passed · pass mark 60/100

What stood out6
  • Correctly locked the IoU >= 0.5 detection definition and classified the worked pair as a miss when IoU was 0.111.
  • Computed and interpreted the key metrics correctly, including aggregate precision 0.95, recall 0.87, and VRU false-negative rate 0.24.
  • Identified the dangerous slices clearly: night recall 0.39, occluded recall 0.45, and night+occluded recall 0.27.
  • Wrote a consistent GO-WITH-CONDITIONS verdict that cited the dossier's own evidence and avoided the common mistake of treating aggregate accuracy as the safety metric.
  • Data-Driven Decision Making
  • Statistical Analysis
The work I submitted20 tasks

12 tasks · 17.3k characters · Python · 8 written answers · 845 words

  • Sensor Match553 characters
  • Example Iou Demo1.5k characters
  • Task Definition Code1.1k characters
  • Task Definition377 characters
  • Example Metrics DemoPython · 2.2k characters
  • Scored Metrics RunPython · 2.3k characters
  • Scored Metrics351 characters
  • Ttcs Example SlicePython · 1.3k characters
  • Ttcs Triage SlicePython · 1.6k characters
  • Failure Triage1.8k characters
  • Sotif Case2.7k characters
  • Ship Verdict1.6k characters
  • Sensor Match27 words
  • Example Iou Demo169 words
  • Task Definition Code149 words
  • Task Definition42 words
  • Example Metrics Demo154 words
  • Scored Metrics Run143 words
  • Scored Metrics18 words
  • Ttcs Example Slice143 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

Machine Learning for Automotive

AI / ML · June 2026

91/ 100

A ProoV case study · educational project, not employment

A verified, self-directed industry project — completed and passed a real industry rubric at 91/100 on ProoV.

Graded against

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

Passed · pass mark 60/100

What stood out6
  • Correctly identified engine size and vehicle age as major price drivers through correlation and scatter analysis.
  • Compared Linear Regression against a tuned Random Forest and quantified the performance lift with R² and MAE.
  • Translated model performance into a business-facing pricing recommendation for the CPO division.
  • Provided a clear interpretation of brand premium, showing Audi's higher resale value relative to VW.
  • Data-Driven Decision Making
  • Statistical Analysis
The work I submitted10 tasks

5 tasks · 6 lines · 5 written answers · 174 words

  • VW Sprint1 Complete56 characters
  • VW Sprint2 Complete61 characters
  • VW Sprint3 Complete59 characters
  • VW Business Insights1.7k characters
  • Jupyterlite Code2 lines
  • VW Sprint1 Complete1 word
  • VW Sprint2 Complete1 word
  • VW Sprint3 Complete1 word
  • VW Business Insights170 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
92Average score
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