My Profiles
About Me
I am a passionate and driven Computer Science student at SRKR Engineering College with a strong foundation in software development and problem-solving. My journey in technology is fueled by a curiosity to learn and a desire to build meaningful applications that can make a difference.
With a current CGPA of 9.19, I am dedicated to academic excellence and continuously seek opportunities to apply my knowledge in real-world scenarios. I am proficient in multiple programming languages and frameworks, and I enjoy tackling complex challenges, whether it's developing a web application or solving algorithmic puzzles.
Education
- B.Tech, Computer Science & Engineering
SRKR Engineering College, Bhimavaram (2022-2026)
CGPA: 9.19 - Intermediate (MPC)
Shridi Sai Junior College (2020-2022)
Percentage: 95.8% - Secondary School
Gowtham Concept School, Gudivada (2020)
Percentage: 81.2%
Projects
A responsive job portal built with Django where companies can post job openings and job seekers can apply for them. This project demonstrates full-stack development capabilities, including database management, user authentication, and front-end design.
Technologies: HTML, CSS, Bootstrap, Python, Django, SQLite
An online examination system with separate, feature-rich panels for Admins, Teachers, and Students. This application showcases skills in building multi-user systems with different permission levels.
Technologies: PHP, JavaScript, Bootstrap, HTML, CSS, MySQL
Technical Skills
Languages
PythonJavaC++JavaScriptC
Frameworks & Technologies
DjangoReact JSNode JSPHPAWSGitMachine Learning
Databases
SQLMongoDBMySQLSQLite
Internship Experience
Machine Learning Intern
NIELIT VIRTUAL ACADEMY | June 2024
During my internship, I developed a stroke prediction model using machine learning techniques. This experience provided hands-on practice in data preprocessing, model implementation, and performance evaluation.
- Implemented a Logistic Regression model to predict stroke risk.
- Performed data cleaning, handled missing values, and scaled features.
- Addressed class imbalance using the SMOTE technique.
- Evaluated model performance using accuracy, confusion matrix, and classification reports.
- Compared Logistic Regression with a Random Forest model to identify the more effective approach.
Achievements & Certifications
- Google Cloud Computing Foundations: 4 Skill Badges.
- 4 badges in LeetCode for daily problem solving.