Data Scientist and M.Tech student in Artificial Intelligence and Data Science, skilled in Python, SQL, and Machine Learning. I build data analysis, dashboarding, and machine learning projects, from data cleaning and exploratory analysis to model evaluation, turning raw datasets into clear, actionable insights.
I believe every dataset hides a decision. My job is to find it — cleanly, quickly, and beautifully.
I am an M.Tech student in Artificial Intelligence and Data Science at Parul University, with a B.Tech in Information Technology. I build data analysis, dashboarding, and machine learning projects using Python and SQL, with hands-on experience in data cleaning, exploratory analysis, and model evaluation. My research focuses on rare skin lesion classification under limited labeled data, using Few-Shot Learning, Explainable AI, and Uncertainty Quantification.
A curated stack — not just what I know, but what I've shipped with.
Real problems, measurable impact. Every project links to code and case study.
Web-based platform for classification and regression with 8 machine learning models, covering CSV upload, feature/target selection, training, evaluation, prediction, and visualization. Tested on 50+ datasets with approximately 90% average model performance and an R-squared score of 0.94 for Random Forest Regression on one test dataset.
Analyzed 6,699 cleaned orders across 2,400 customers, 28 cities, and 7 categories — INR 91.03 lakh in delivered revenue and 32.7% year-over-year growth. Applied quintile-based RFM scoring to segment 2,097 customers into 8 groups; 304 Champions contribute 28.5% of delivered revenue.
2-page Power BI dashboard with KPI cards tracking 5 US stocks and 3 major indices (S&P 500, NASDAQ, Dow Jones). Automated data collection at 60-second intervals, integrated with Power BI through its REST API, analyzing price changes, trading volume, 52-week highs/lows, and volatility.
A quiet timeline of trainings, tools, and outcomes.
Completed a 6-month Data Science Training Program covering Python programming, data analysis, and machine learning fundamentals. Built a practical foundation in working with data — cleaning and exploring datasets, drawing insights from them, and understanding how machine learning models are built and evaluated — which now underpins the analytics and ML projects in this portfolio.
Completed Deloitte Australia's virtual Data Analytics Job Simulation on Forage, applying data analysis skills to a business-style scenario — from understanding the brief and analyzing the data to presenting insights that a business stakeholder can act on.
Training and simulation credentials from Internshala and Deloitte Australia.
Long-form essays and short reads on analytics, ML, and career.
Discover how to transform messy datasets into clear, compelling stories that help business leaders make smarter, faster decisions.
Break down supervised vs. unsupervised learning, model evaluation metrics, and why understanding ML is now a must-have skill.
Elevate your Power BI dashboards from plain to professional with these design, performance, and storytelling techniques.
INNER, LEFT, RIGHT, FULL — a practical walkthrough of how each join actually behaves, with real query patterns you'll use daily.
What actually matters when breaking into analytics — the skills employers screen for, and the portfolio habits that get interviews.
Neither tool wins every time. A practical framework for deciding based on dataset size, repeatability, and who else needs to touch the file.
A beautiful chart with no context is just decoration. How to frame numbers so stakeholders remember them past the meeting.
You don't need a PhD in stats — you need these ten concepts, understood well enough to explain them to a non-technical stakeholder.
I reply within 24 hours. Whether it's a role, a project, or a coffee chat.
Prefer email or LinkedIn — I'm most active there. Always happy to talk about analytics, ML pipelines, and career moves.
Thanks for stopping by my portfolio! Feel free to reach out for collaborations, opportunities, or just to say hi.
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