UIDAI Aadhaar ID Dashboard
Developed a national-scale analytics and decision-support dashboard using UIDAI Aadhaar enrolment, biometric, and demographic datasets to uncover societal trends, geographic anomalies, and operational insights through interactive Power BI visualizations.
Project Overview
Designed an end-to-end analytics and decision-support dashboard for an e-commerce platform using website sessions, transaction, marketing, and refund data to support business growth and conversion optimization. Built a robust data cleaning and preprocessing pipeline using Python (Pandas) to handle missing user identifiers, validate duplicate transactions, and standardize date, numeric, and categorical fields. Engineered key business metrics including conversion rate, gross profit, net revenue, gross margin, refund rate, revenue per session, and average order value to enable meaningful performance analysis. Modeled cleaned datasets into a scalable analytical structure and developed a multi-page Power BI dashboard tailored for executive and operational stakeholders. Created an executive overview layer presenting high-level KPIs such as sessions, orders, revenue, profit, refunds, and conversion rate for rapid performance monitoring. Conducted website engagement and funnel analysis to evaluate user behavior, bounce rates, session duration, and conversion efficiency across desktop vs mobile and new vs returning users. Performed marketing channel analysis to assess traffic quality, conversion performance, revenue contribution, refund impact, and campaign effectiveness. Delivered product-level revenue and profitability analysis to identify high-performing products, margin risks, and refund-driven revenue leakage. Implemented advanced Power BI features including drill-through analysis, Q&A visuals, and AI-based decomposition trees to identify root causes of revenue variation. Translated analytical insights into actionable business recommendations focused on marketing optimization, mobile UX improvement, product prioritization, and refund reduction.
Project Gallery
Key Features & Implementation
- Processed and cleaned large-scale UIDAI Aadhaar datasets (Enrollment, Biometric, Demographic) using Python, applying standardization, pincode-based geographic correction, and duplicate handling to ensure data integrity.
- Designed a star-schema data model with fact and dimension tables (Date, Geography) to enable scalable analysis across states, districts, zones, and time periods.
- Built multi-page Power BI dashboards with executive KPIs, geographic heatmaps, trend analysis, anomaly detection, and life-stage (age-group) insights using DAX measures.
- Identified high-risk 'Red Zone' districts and states using metrics such as Update Intensity Ratio, Child Share %, MoM Growth %, and Total Activity Load to support operational planning.
- Implemented predictive analytics using Python time-series forecasting to estimate enrollment volume, update demand, and revenue trends for Jan–Mar 2026.
- Developed a recommendation framework translating analytical insights into actionable interventions such as mobile Aadhaar units, staffing optimization, and school-based biometric drives.