Vaishnavi Chougule

Create your portfolio with ProoV
ProoV
DE Verified work portfolio

ProoV Portfolio

Vaishnavi Chougule

Computer Science- AIML · KIT'S COLLEGE OF ENGINEERING KOLHAPUR

View the ProoV leaderboard

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 an end-to-end gas-turbine TEY predictor using leakage-free sensor features, with CO and NOx excluded because they are downstream emissions. Trained and compared a LinearRegression baseline and a RandomForest on a time-based held-out split, then evaluated the winner honestly with MAE, R2, and a residual read. The final model achieved about 1.53 MWh MAE and 0.983 R2 on unseen later-period data, with a clear model card describing intended use, limits, and drift monitoring.

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 dropped CO and NOx because they are downstream emissions and would leak future information into the model.
  • Used an earlier-period train / later-period test split instead of a random shuffle, which is the right validation pattern for ordered operational data.
  • Compared a LinearRegression baseline against a RandomForest and showed a clear held-out MAE improvement.
  • Noted heteroscedasticity and slight positive bias at the top end in the residual read, showing calibrated evaluation rather than overclaiming.
  • Leakage-Free Feature Selection
  • Time-Based Holdout Validation
The work I submitted18 tasks

10 tasks · 11 lines · Python, JavaScript · 8 written answers · 422 words

  • Problem Frame238 characters
  • Leakage Flags113 characters
  • Train Validate BaselinePython · 1.4k characters
  • Train Validate Forest943 characters
  • Train Validate124 characters
  • Residual Read168 characters
  • Drift Monitor91 characters
  • Model Card1.5k characters
  • Teach BackJavaScript · 848 characters
  • Jupyterlite Code2 lines
  • Problem Frame18 words
  • Leakage Flags1 word
  • Train Validate Baseline141 words
  • Train Validate Forest76 words
  • Train Validate1 word
  • Residual Read18 words
  • Drift Monitor1 word
  • Model Card166 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 · August 2026

84/ 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. Combined the datasets, explored price and mileage patterns, engineered predictive features such as car age and mileage per year, and compared Linear Regression with Random Forest using R², MAE, and RMSE. Delivered a clear pricing recommendation for a Certified Pre-Owned team based on the stronger model and the most influential drivers of price.

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 table with a brand indicator for cross-brand comparison
  • Compared Linear Regression against Random Forest using R², MAE, and RMSE and selected the stronger model based on performance
  • Translated model outputs into a CPO pricing recommendation and quantified the operational impact of pricing error
  • Leakage-Aware Data Preparation
  • Comparative Model Evaluation
  • Business-Oriented Interpretation
The work I submitted10 tasks

5 tasks · 6 lines · 5 written answers · 163 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 Insights159 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
88Average score
Create your portfolio with ProoV