SAI RANADHEER GOUD MOSANPALLY

• Extracted data from SQL server by joining multiple tables and performed data mining on online customer transactions data to gain insights, and analyze patterns, and trends
• Performed data analysis from the BI side including profitability, sales trends, inventory turnover, and understanding variance between standard and actual product cost
• Experienced in developing reports by writing complex SQL queries related to customer transaction data, purchase data, and shipping-related information using Views
• Wrote several SQL queries and extracted data from various sources and designed and deployed rich graphic visualization with drill-down options and parameterized using Tableau
• Created side-by-side charts, line charts, bar charts, and stacked bar charts according to the requirements gathered and specifications from stakeholders
• Debugged data issues between the source system and Enterprise Data Warehouse
• Involved in identifying reporting needs helping the team in gathering requirements and specifications and collaborating with other team members to achieve reporting goals
• Imported data from SQL Server and developed sales reports using Microsoft Excel and Power-BI.

• Writing SQL queries for huge sales transaction data pulls from the server and building ad-hoc reports for the stakeholders
• Interacted with business analysts to understand reporting requirements and provided quality data to the customers
• Created visually compelling and actionable interactive reports and dashboards using Tableau

Online Fraud Detection - Data Mining, University of South Florida

• Led a team of 3 people to build and implement a regression model to predict fraud detection in the banking domain
• Developed and implemented Logistic Regression and Support Vector Machine ML algorithms to detect online credit card fraud detection with an Accuracy of 0.95
• Performed and implemented feature engineering techniques to clean and transform raw transactional data
• Extracted new features from the data to understand trends and transactional patterns
• Implemented anomaly detection techniques to identify unusual behavior with transactional data deviating from normal behavior

Database Design of a Hospital -Advanced Database Management, University of South Florida

• Developed a comprehensive hospital database system using SQL to manage over 1,00,000 patient records with medical procedures and claims
• The database consists of 10 tables related to Patients, doctors, pharmacies, inventory, Invoices, Appointments, and staff-related data
• Implemented data integrity measures and optimized database performance, reducing query processing time by 40% through indexing and primary key constraints.