Arroju Harshitha

Aspiring Software Engineer
Hyderabad, IN.

About

Aspiring Software Engineer with a robust foundation in computer science and hands-on experience in software development through academic projects and internships. Eager to apply strong programming skills and problem-solving abilities to contribute to innovative software solutions. Motivated to learn, grow, and drive impact within a dynamic tech environment, leveraging expertise in AI/ML, web development, and data analysis.

Education

ACE ENGINEERING COLLEGE
Ghatkesar, Telangana, India

B.Tech

Computer Science and Engineering

Courses

Data Structures (DSA)

Operating Systems

Machine Learning

DBMS

OOPS

Certificates

Cisco IT Essentials Certification

Issued By

Cisco

Cisco C Certification

Issued By

Cisco

Cisco Python Certification

Issued By

Cisco

UI Path Automation Explorer

Issued By

UI Path

Skills

Languages

C/C++, Java, Python, HTML, CSS, Javascript, Kotlin.

Web Development

HTML, CSS, Javascript, Java Backend, Spring MVC.

Database Management

MySQL, Oracle, MongoDB.

Data Science & ML

NLTK, NLP, Regression, Classification, Clustering, NumPy, Pandas, Matplotlib.

Developer Tools

VS Code, Git, Eclipse, Docker, Maven.

Interests

Creative Arts

Photography & editing, Video editing, Painting.

Projects

JARVIS - The Future at Your Fingertips

Summary

Developed an AI-powered human-computer interaction system leveraging computer vision and natural language processing to enable intuitive, hands-free control of digital environments.

Music Player Application

Summary

Designed and developed a feature-rich music player application using Python, Tkinter, and Pygame, providing comprehensive audio playback and management functionalities.

Smart Attendance System

Summary

Developed an automated attendance tracking system leveraging biometric recognition, RFID, and mobile applications to enhance accuracy and streamline administrative processes in educational and professional environments.

Face Recognition System

Summary

Developed a robust face recognition system utilizing the K-Nearest Neighbor (K-NN) algorithm and OpenCV for precise and efficient facial detection.