MINAL V G

Mechatronics Engineering Student · AI/ML Intern

Tiruppur, Tamil Nadu, India

@minal_v_g

LinkedIn GitHub

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🎓 B.E. Mechatronics Engineering at Sri Krishna College of Engineering and Technology · Graduating 2028

About

Mechatronics Engineering student with hands-on experience in Python-based software development, machine learning, and AI-driven applications. Built and deployed projects in predictive maintenance, computer vision, and data-driven engineering using tools such as Scikit-learn, OpenCV, and Streamlit.

Experience

AI/ML Internship

Škoda Auto

May 2026 – May 2026

Developed and deployed machine learning models for automotive service cost estimation, customer complaint classification, and predictive maintenance. Applied Random Forest, XGBoost, and Linear Regression; evaluated model performance using accuracy, F1-score, and Mean Absolute Error (MAE).
Automotive Engineering Internship

Tata Motors

Jan 2026 – Jun 2026

Observed electric vehicle systems and gained familiarity with EV architecture and automotive engineering workflows. Completed hands-on training using Toughbook devices for vehicle updates and developed knowledge of power transmission and vehicle electrical systems.

Education

2028

B.E. Mechatronics Engineering

Sri Krishna College of Engineering and Technology

Awarded by Anna university

Mechatronics Engineering · 2024 – 2028

Mechatronics Engineering · 2024 – 2028

Skills

<3 months

C SQL RAG Yolo ONNX NumPy Faiss Python Pandas OpenCV XGBoost Streamlit Regression scikit-learn Data Analysis Random Forest Classification Computer Vision Machine Learning Model Evaluation Gradient Boosting Linear Regression Predictive maintenance Natural Language Processing Generative AI (ChatGPT, Copilot, Gemini)

Tools / apps / platforms

<3 months

Git GitHub Jupyter Notebook

Projects

Aircraft Remaining Useful Life Prediction

XGBoost

Developed an XGBoost Regression model using NASA CMAPSS FD001 data to estimate aircraft engine Remaining Useful Life (RUL), achieving MAE of 25.40 cycles, RMSE of 35.91 cycles, and R^2 of 0.718.
AeroAssist AI

FAISS, all-MiniLM-L6-v2

Built a retrieval-augmented generation workflow over 698 pages of FAA aircraft maintenance manuals, retrieving the top five passages. Qualitatively validated retrieval and response relevance with a compressor-stall query; numerical RAG metrics were not measured.
Pothole Detection

YOLO11n, OpenCV, ONNX

Developed a three-class object detector for potholes, cracks, and normal road surfaces; achieved 72.1% precision, 51.4% recall, 55.8% mAP@50, and 37.5% mAP@50–95. Exported the model to ONNX and integrated it with a local detection dashboard.
Automotive Predictive Health Maintenance

AI4I 2020 Dataset

Compared Logistic Regression, Decision Tree, MLP Neural Network, Gradient Boosting, and Random Forest on an 80/20 stratified train-test split; Gradient Boosting achieved 98.60% accuracy, 98.47% weighted F1-score, and 96.35% ROC-AUC on 2,000 test samples.

Courses & certifications

MongoDB Data Modelling & Atlas Fundamentals · MongoDB
NPTEL Design Thinking · NPTEL
Prompt Engineering for Generative AI · IBM/Coursera
IBM AI & ML · IBM
Tata GenAI Job Simulation · Tata
TCS iON Generative AI Certification · TCS iON

⚽ Extracurricular activities

Smart India Hackathon 2026 — Team Lead

Led a solar-powered onion storage project; Top 3 at department level and Top 7 at college level.
Tata Technologies Innovate Hackathon — Team Lead

Led the AeroPredict project.
NIT Trichy 36-hour National-level AI/ML Hackathon — Team Lead

Led AgriBot, an AI-powered banana disease detection robot.
MSME Hackathon 6.0 — Team Lead

Led a hackathon team.

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