MOUNIKA KAMBALLA

B.Tech Computer Science Student | AI/ML Enthusiast
Rly Kodur, IN.

About

Highly motivated B.Tech Computer Science student with a robust foundation in Java, Python, SQL, and Machine Learning, demonstrated through impactful AI and data-driven projects. Seeking an entry-level Software Engineer, Data Scientist, or ML Engineer role to leverage strong analytical and development skills, contributing to innovative solutions within a dynamic tech environment.

Education

Kalasalingam Academy of Research and Education
Krishnankoil, Tamil Nadu, India

→

B.Tech

Computer Science

Grade: GPA: 8.66

Sri Shirdi Sai Junior College
Rly Kodur, Andhra Pradesh, India

→

Intermediate Education

Mathematics, Physics, Chemistry (MPC)

Grade: GPA: 91.7

Sri Kakatiya High School
Rly Kodur, Andhra Pradesh, India

→

High School

Secondary Education

Grade: GPA: 93.33

Certificates

TCS ION Career Edge – Young Professional

Issued By

Tata Consultancy Services

Smart India Hackathon

Issued By

Ministry of Education's Innovation Cell and AICTE

AI Applications in People Management

Issued By

University of Pennsylvania

Skills

Programming Languages

Java, Python, SQL.

Libraries/Frameworks

scikit-learn, TensorFlow, Pandas, NumPy, Streamlit, Matplotlib, Seaborn, HTML, CSS, JavaScript, Flutter.

Tools/Platforms

Jupyter Notebook, VS Code, Power BI, Git, GitHub.

Core CS Concepts

Data Structures and Algorithms (DSA), Machine Learning, Artificial Intelligence, Data Science, UI/UX Design.

Projects

AI Applications in People Management

Summary

Developed an AI-driven solution to predict employee performance ratings and provide actionable insights for HR decision-making.

Interactive Learning Toy Platform (Smart India Hackathon Finalist)

Summary

Designed and developed an innovative educational toy platform with gamified elements, recognized in a national hackathon.

Water Contamination Prediction Using Sensors

Summary

Built a machine learning model to predict water contamination using real-time sensor data, focusing on model explainability.

PCOD Prediction and Dashboard

Summary

Created ML models and an interactive dashboard for predicting PCOD from health data, emphasizing interpretability and model evaluation.