PavanKumar Reddy Naramreddy

Results-oriented Software Engineer with 1.5 years of experience at Capgemini, including an internship and a full-time role, where I specialized in Python, SQL, and backend API development. Contributed to performance optimization initiatives that improved customer response times by 30% through efficient API enhancements, and streamlined data handling. Skilled in designing scalable backend solutions, and collaborating in Agile development environments. Recently completed a Master’s degree in Business Analytics from the University of North Texas, gaining deep expertise in data modeling, statistical analysis, and business intelligence tools. Passionate about leveraging technical skills and analytical thinking to solve complex business problems, and deliver impactful, data-driven software solutions.

Capstone Project – Crime Pattern Analysis and Prediction in Urban Areas ~ UNT, Denton

• Used publicly available crime datasets to identify patterns and provide insights for law enforcement strategies.
• Analyzed relationships between victim demographics, crime locations, and crime types to guide police resource allocation.
• Applied data visualization to reveal crime trends by hour, month, and region.
• Handled data challenges like missing values, high-cardinality features, and inconsistent fields.
• Built a predictive model to forecast crime types and high-risk zones using historical, spatial, and demographic data.

Finite Element Analysis of Anti-Roll Bars ~ New Horizon College of Engineering - india

• Modeled anti-roll bars using SolidWorks and conducted finite element analysis (FEA) using ANSYS Workbench to assess structural behavior under various loads.
• Analyzed multiple cross-sections (25mm, 30mm, and 35mm) to evaluate strain and deformation; confirmed that larger diameters lead to reduced stress and improved stability.
• Performed both linear and non-linear static structural analysis, incorporating bushings and clamps to simulate real-world suspension scenarios.
• Compared materials like SAE 9262 steel and polyurethane to study the impact on stiffness, strain, and weight.
• Concluded that optimized design and material selection can significantly reduce body roll, improve vehicle handling, and aid in fuel efficiency by reducing component weight.

Speed Control System in Electric Vehicle using Raspberry Pi New Horizon College of Engineering - india

• Contributed to the design and implementation of an intelligent speed control system for electric vehicles by working jointly with the Electrical & Electronics department.
• Focused on the vehicle integration and mechanical aspects of the system, ensuring that the speed control module aligned with real-world EV architecture and safety requirements.
• Supported the integration of the BLDC motor drive system into the EV chassis layout and verified compatibility with Arduino Nano and L298N motor driver components.
• Assisted in configuring the mechanical response of the motor to match real-time speed inputs processed by Raspberry Pi and PiCamera, used for speed limit recognition.
• Helped in assembling and testing the prototype vehicle setup, ensuring proper placement of sensors, wiring, and control units for efficient and safe performance.
• Gained hands-on experience in embedded automotive systems, sensor-based control, and cross-domain collaboration, contributing to a working model that regulated vehicle speed automatically based on road signs.
• Documented system performance from a vehicular dynamics perspective and evaluated the impact of speed regulation on vehicle control and passenger safety.