About
Computer Engineering graduate with hands-on internship experience in AI/Machine Learning, including building an image classification model. Skilled in Python, SQL, data analysis libraries (NumPy, Pandas, Scikit-learn) and core ML techniques such as clustering and regression, with academic projects spanning customer segmentation, predictive modeling and responsive web development.
Experience2
AI/ML Industrial Internship
Jan 2026 — Mar 2026ADS Foundation
Built and trained a VGG16-based transfer learning model for image classification, achieving 95% test accuracy on a brain tumor image dataset. Applied foundational machine-learning concepts under mentorship during a structured 3-month internship.
Data Science & ML Intern
Apr 2026 — Jul 2026Infotact Solutions
Completed a structured Data Science & Machine Learning training program covering Python, statistics and core ML concepts through online lectures and guided exercises.
Projects4
Brain Tumor Detection using Deep Learning
Built an image classification model using VGG16 transfer learning to detect brain tumors from MRI scan images. Fine-tuned the pretrained VGG16 architecture on a labeled medical imaging dataset, applying data preprocessing and augmentation to improve generalization. Achieved 95% test accuracy in classifying tumor vs. non-tumor images.
- Python
- TensorFlow
- Keras
- VGG16
- NumPy
- Matplotlib
Customer Segmentation for Supermarket
Built a K-Means clustering model to segment supermarket customers into distinct behavioral groups based on spending patterns and demographics. Performed data preprocessing, feature scaling and visualization to identify and interpret cluster characteristics. Used Python, NumPy, Pandas, Matplotlib and XGBoost for analysis and modeling of customer purchase data.
- Python
- NumPy
- Pandas
- Matplotlib
- XGBoost
Maths Marks Prediction
Built a regression model to predict students' mathematics scores from demographic and academic features. Performed data preprocessing, feature engineering and hyperparameter tuning to improve model performance. Evaluated model performance using standard ML metrics such as RMSE and R².
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
Amazon Clone — Frontend
Designed and built a responsive Amazon-style e-commerce frontend using HTML, CSS and JavaScript, with a mobile- and desktop-friendly layout.
- HTML
- CSS
- JavaScript
Education3
Bachelor of Engineering
2022 — 2026Government Engineering College, Modasa
Computer Engineering
HSC (12th)
2021 — 2022Shree R A Patel Boys High School, Surendranagar
SSC (10th)
2019 — 2020Shree Govindsinhji Rana High School, Surendranagar