AI/ML engineer based in Roorkee, India. I build end-to-end systems across healthcare, finance, and NLP — from data cleaning to deployed Streamlit apps.
Trained a convolutional network to 99.34% test accuracy on MNIST — benchmarked against a Perceptron (90.73%) and a dense ANN (97.47%). Full confusion matrix and training curves in the repo.
Classifier on the UCI medical dataset at 85%+ accuracy. Compared Logistic Regression, Random Forest, and SVM. Deployed as a Streamlit app with pickled scaler.
Multi-class text classifier across 6 emotions. Tokenization, stopword removal, TF-IDF vectorization. Live Streamlit demo with probability breakdown.
Trained a convolutional network to 99.34% test accuracy on MNIST — benchmarked against a Perceptron (90.73%) and a dense ANN (97.47%). Full confusion matrix and training curves in the repo.
Classifier on the UCI medical dataset at 85%+ accuracy. Compared Logistic Regression, Random Forest, and SVM. Deployed as a Streamlit app with pickled scaler.
Multi-class text classifier across 6 emotions. Tokenization, stopword removal, TF-IDF vectorization. Live Streamlit demo with probability breakdown.
XGBoost regression reaching 92% R² with GridSearchCV tuning. Feature importance flagged mileage and year as top drivers.
7 end-to-end projects: heart disease, insurance, Ford pricing, and a KNN classifier with cross-validation.
CNN at 99.34% on MNIST, plus an Iris classifier, confusion matrices, and accuracy curves.
Emotion classifier across 6 classes with NLTK, TF-IDF, and a live Streamlit prediction app.
Established and scaling a 300+ member technical collective focused on AI/ML education, peer-led research reviews, and structured career mentorship.
Completed forensic technology and business intelligence simulation, delivering insights across complex, client-grade datasets.
Architected executive dashboards emphasizing narrative clarity, transforming raw metrics into strategic decision frameworks.
Certified across Python, Statistics, Pandas, NumPy, and Core ML — establishing rigorous fundamentals for production-grade systems.
Pursuing undergraduate degree with CGPA 7.1/10. Focus areas: Machine Learning Systems, Deep Learning, and Applied Data Science. Seeking internships.
No pitch decks or long threads needed. Just tell me the role, location, and start date — I'll reply with my availability and current projects.
I'm looking for a role where I can ship ML systems to real users — not just notebooks. If that's what you're building, we should talk.