About Me
I build machine learning systems that transform raw data into actionable predictions
View ResumeThe Beginning
My journey into machine learning started with a simple question: "Can data actually predict what happens next?"That curiosity led me from writing my first Python script to building end-to-end ML pipelines.
Currently pursuing B.Tech in Computer Science (Data Science) at National Institute of Science and Technology, I've complemented my formal education with hands-on projects and professional certifications from the University of Michigan and Coursera.
What started as curiosity has become a passion for turning messy real-world data into actionable insights that solve real problems.
How I Think Now
After building several ML systems, my engineering philosophy has crystallized around a few core beliefs:
A simple model on clean data outperforms a complex model on messy data.
The best predictions come from understanding why something happens.
If you can't reproduce your results, you don't understand them.
Education
B.Tech Computer Science & Engineering
Data Science
National Institute of Science and Technology
August 2024 – August 2028
Higher Secondary (XII Science)
CBSE
St. Xavier's High School
April 2020 – May 2022
Professional Courses
Applied Machine Learning in Python
University of Michigan • Coursera
April 2024 – June 2024
- •Supervised & unsupervised learning algorithms
- •Model training, testing, and evaluation
- •Feature engineering & data preprocessing
Machine Learning with Python
Coursera • Coursera
January 2024 – March 2024
- •Supervised & unsupervised learning algorithms
- •Model training, testing, and evaluation
- •Feature engineering & data preprocessing
Values
Intellectual Honesty
I say "I don't know" when I don't. I share failures alongside successes.
Continuous Learning
ML evolves fast. I dedicate time weekly to learning new techniques.
Clear Communication
Complex ideas should be explained simply. Jargon hides weak thinking.
Ship & Iterate
Perfect is the enemy of good. Working solutions beat theoretical perfection.
Languages
Working Style
- ✓I prefer async communication for deep work, with sync catchups for alignment
- ✓I document as I build — code comments, READMEs, and decision logs
- ✓I ask questions early rather than making assumptions
- ✓I value honest feedback and give it respectfully in return
Environments I Avoid
Being upfront about what doesn't work for me:
- ✕Teams where metrics don't matter (decisions by opinion, not evidence)
- ✕Environments that punish honest mistake acknowledgment
- ✕Pressure to deploy models without proper validation