Kaduru Mahendra

Final Year B.E Computer Science Student, Saveetha School of Engineering, Chennai

A self-motivated and quick learner with a strong analytical mindset and a passion for technology. Adept at problem-solving and delivering innovative solutions through technical expertise.

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About Me

Driven by a deep curiosity for technology, I am a final-year Computer Science student at Saveetha School of Engineering in Chennai. My academic journey has equipped me with a solid foundation in programming languages like Java and C, database management with SQL and MySQL, and web development technologies including HTML, PHP, CSS, and JavaScript. I'm also keen on mobile application development using Kotlin and Flutter.

I thrive on challenges and enjoy applying my technical skills to create impactful solutions. My projects, such as the Speech Sound Therapy App for Children and the Email Classification prediction model, showcase my ability to leverage technology for real-world applications. As a team player with strong problem-solving abilities, I am eager to contribute to innovative projects and continue learning and growing in the tech industry.

Education

Bachelor of Engineering in Computer Science

Saveetha Institute of Medical And Technical Sciences

July 2021 - June 2025

CGPA: 8.4/10

Higher Secondary Education (Class XII)

Narayana Junior College

June 2019 - May 2021

Percentage: 91%

Secondary Education (Class X)

Narayana High School

June 2018 - May 2019

CGPA: 9.0/10

Technical Skills

Programming Languages

Java

Database

MySQL

SQL

Web Development

HTML5

CSS3

JavaScript

PHP

Mobile Application Development

Android Studio

Flutter

Design & Other Tools

Figma

Projects

Speech Sound Therapy App

Developed a mobile application for both Android and iOS to assist in the assessment and intervention of speech sound disorders in children.

Utilized Android Studio for native Android app development and Xcode for iOS app development.
Implemented interactive modules to help children practice and improve speech sounds.
Designed a user-friendly interface tailored for speech therapists, parents, and educators to track progress and customize therapy plans.

Email Classification Prediction Model

Developed a machine learning-based system to classify emails as spam or ham (non-spam), enhancing automated email filtering.

Implemented classification algorithms such as Naive Bayes, Logistic Regression, and Support Vector Machines.
Processed and cleaned the dataset to remove noise and ensure quality input for the model.
Evaluated model performance using accuracy, precision, recall, and F1-score to ensure robust classification.