Available for internships · Remote · In-office (Mumbai)

Data Analyst
turning numbers
into decisions.

B.Tech CS & AI student at Newton School of Technology. I build end-to-end analytics pipelines — from raw data to interactive dashboards — across real estate, e-commerce, and retail domains.

View Projects GitHub ↗ LinkedIn ↗

30K+

Records analysed

End-to-end projects

8.9

CGPA

Tools in stack

// stack

Technical Skills

Languages & Query

Python SQL CTEs Window Functions Subqueries MongoDB

Analysis

Pandas NumPy Scikit-learn EDA Hypothesis Testing Aggregation Pipelines

Visualisation & Sheets

Tableau Looker Studio Matplotlib Seaborn Google Sheets Microsoft Excel

Concepts

ETL Pipelines Feature Engineering Clustering Price Elasticity Regression NoSQL Modelling

// education

Newton School of Technology, Rishihood University

B.Tech — Computer Science & Artificial Intelligence · Expected 2028

8.9

CGPA / 10

// work

Projects

Blinkit Pricing Intelligence Dashboard

Solo

Python Pandas Scikit-learn KMeans Looker Studio Google Sheets

Analysed 5,000 orders across 268 products in 11 categories; flagged 26 at-risk products where discounts above 30% were eroding profit margins.
Applied KMeans clustering to segment products into 3 pricing tiers — Premium generates 3.3× revenue per item vs. Budget.
Simulated revenue uplift by category: Fruits & Vegetables identified as highest-opportunity segment (+17.6% potential uplift) via MRP vs. selling price gap analysis.
Detected seasonal patterns (August peak, December dip) enabling proactive pricing strategy for procurement and marketing teams.

GitHub Live Dashboard

US House Price Analysis — 10 States

Group · DVA Capstone Role: ETL pipeline, EDA, statistical analysis

Python Pandas Tableau Statistical Analysis Jupyter

Full EDA on 11,410 residential listings; showed California's $1.16M average inflates the national mean by 45% when included.
Built a 5-notebook Python ETL pipeline — extraction → cleaning → EDA → statistical analysis → load.
Conducted Welch's t-test confirming a statistically significant $150K weekday vs. weekend price gap driven by broker composition.
Identified North Carolina as highest price/sqft efficiency market ($684/sqft), projecting 15–20% higher yield on a $10M portfolio vs. California.

GitHub

Retail Store Sales Performance Analysis

Group Role: data cleaning, revenue analysis, dashboard

Python Pandas Tableau Looker Studio

Cleaned and transformed 12,575 transactional records: handled missing values, imputed zero-value discounts, engineered Month and Day_of_Week columns.
Revenue concentration analysis identifying top-performing categories; found applied discounts do not significantly increase transaction frequency.
Interactive dashboard tracking KPIs across revenue, discount impact, day-of-week patterns, and store location performance.

GitHub

Data Analyst turning numbers into decisions.

Technical Skills

Projects

Open to remote internships& freelance projects.

Data Analyst
turning numbers
into decisions.

Open to remote internships
& freelance projects.