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
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B.Tech Computer Science Grade: GPA: 8.66

Sri Shirdi Sai Junior College

Rly Kodur, Andhra Pradesh, India
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Intermediate Education Mathematics, Physics, Chemistry (MPC) Grade: GPA: 91.7

Sri Kakatiya High School

Rly Kodur, Andhra Pradesh, India
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High School Secondary Education Grade: GPA: 93.33

Certificates

TCS ION Career Edge – Young Professional

Tata Consultancy Services

Smart India Hackathon

Ministry of Education's Innovation Cell and AICTE

AI Applications in People Management

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.