Hi, I'm Amit, a data analytics enthusiast who loves SQL, dashboards & real-world business problems.

Designed a multi-page Power BI healthcare analytics dashboard to analyze patient diagnostics, clinical symptoms, blood test insights, and demographic trends. The dashboard enables healthcare professionals to identify high-risk patients, monitor symptom patterns, and evaluate diagnostic indicators for better clinical decision-making.

Built an interactive Power BI dashboard to analyze telecom customer churn, revenue loss, and contract risk. The dashboard identifies high-risk customer segments, contract performance, and revenue impact to support retention strategies and reduce churn-driven revenue leakage.

Designed a multi-page Power BI dashboard to analyze sales performance and profitability across a pharmacy network. The analysis focuses on geographic performance, pharmacy-level benchmarking, product mix, and promotion impact to answer real business questions and support strategic decision-making.

Built a complete analytics solution using Microsoft Fabric—starting from raw Excel files to a fully automated Power BI report. The project includes a Lakehouse, Dataflows, Data Pipelines, Warehouse SQL transformations, and a semantic model powering insights on project budgets, capital allocation, and department-wise financial performance.

Built an interactive Power BI dashboard to track mobile sales performance across brands, models, and cities. Analysed sales volume, transactions, customer ratings, and payment methods to highlight what drives revenue.

Created a Power BI report powered by a live Weather API, showing a 7-day forecast with temperature trends, humidity, AQI, wind speed and rain probability. The data refreshes automatically through API integration to keep the dashboard up to date.

Built an end-to-end Power BI analytics solution to analyze Blinkit grocery sales and outlet performance. The dashboard uncovers how product categories, outlet characteristics, visibility, and ratings influence overall sales outcomes and operational efficiency.

Developed a Power BI dashboard to analyse overall sales performance: revenue by category and region, discount vs revenue, profit analysis, and key KPIs. The dashboard helps stakeholders quickly identify high-performing segments and areas needing attention.

Built an interactive Looker Studio dashboard connected to a live PostgreSQL (Supabase) database to analyze applicant trends, program demand, geographic distribution, and recruitment channel performance. Delivered a real-time, filterable analytics solution to support admissions and recruitment decision-making.

Designed a relational database structure for CRM sales data and wrote SQL queries to analyse performance by agent, product, and region. Explored win/loss patterns, sales trends, and basic revenue forecasting to surface opportunities for improvement.

Performed advanced SQL analysis on the Brazilian Olist e-commerce dataset to uncover business insights related to sales performance, customer behavior, delivery efficiency, and product demand. The project includes cohort analysis, RFM customer segmentation, revenue trends, retention analysis, and seller performance evaluation to support data-driven decision-making for e-commerce businesses.

Analysed Zepto sales data using SQL to understand customer orders, product performance, and city-wise demand. Designed queries to track total revenue, average order value, repeat customers, and top-performing product categories to support data-driven decision-making for business growth.

Performed data cleaning, aggregation, and business-driven analytics using SQL. Extracted KPIs such as total revenue, customer retention, product popularity, and time-based trends. Delivered actionable insights for stakeholders based on raw transactional data.

Performed exploratory and business-driven analysis on a toy store e-commerce dataset using Python. Evaluated funnel conversion, revenue leakage from refunds, customer behavior, and product performance to uncover actionable growth opportunities.

Performed exploratory data analysis on telecom customer data to identify key factors driving customer churn. Analyzed customer demographics, subscription patterns, contract types, and service usage to uncover churn trends and actionable retention insights.

Performed exploratory data analysis on multi-month sales data using Python. Cleaned and transformed raw transactional data, analyzed sales trends by month, city, product, and time of purchase, and identified patterns to support business and marketing decisions.

Conducted exploratory data analysis on Diwali sales data to understand customer demographics, purchasing behavior, and product category performance. The analysis highlights key trends to support targeted marketing and festive-season sales optimization.