Sana afrin

Final-year B.Tech graduate in Artificial Intelligence and Machine Learning

Hyderabad, Telangana 80 · Excellent

About

Final-year B.Tech graduate in Artificial Intelligence and Machine Learning with a strong foundation in Python programming, Object-Oriented Programming (OOP), Data Structures & Algorithms (DSA), SQL, and the Software Development Life Cycle (SDLC). Hands-on experience building and testing REST APIs, backend services, and data-driven applications using Git version control. Quick learner with strong analytical and problem-solving skills, eager to contribute to application development, automation, and emerging technologies including cloud and AI.

Skills & Expertise (36)

Python Advanced

8.0/10

Feature Engineering Intermediate

7.0/10

Node.js Intermediate

7.0/10

React.js Intermediate

7.0/10

Data Structures & Algorithms Intermediate

6.5/10

Model Evaluation Intermediate

6.5/10

Data Visualization Intermediate

6.5/10

Data Cleaning Intermediate

6.5/10

SQL Intermediate

6.0/10

Git Intermediate

6.0/10

Flask Intermediate

5.5/10

Excel OOP Classification Regression LightGBM XGBoost scikit-learn DBMS SDLC EDA Java NumPy Pandas Power BI Microsoft Azure Google Colab Jupyter Notebook Streamlit GitHub MongoDb MySql REST APIs JavaScript CSS3 HTML5

Work Experience

Full Stack Developer Intern

Smart Learn Edtech

Oct 2024 - Oct 2024

Developed a Food Ordering Web Application using React.js, Node.js, and MongoDB, following OOP design principles. Designed and integrated REST APIs for user and order management, and worked with SQL/MongoDB for backend data handling. Improved application response time by 30% through backend optimization and automation of repetitive tasks; used Git version control for collaborative development, testing, and debugging.

Machine Learning Intern

Skilltimate Technologies

May 2024 - May 2024

Developed a Heart Disease Prediction System using Python and Scikit-Learn, following OOP principles and structured SDLC practices for coding, testing, and documentation. Processed and analyzed 5,000+ patient records using Pandas and NumPy as part of a data processing and analytics pipeline; improved prediction accuracy by 15% through feature engineering and model optimization. Built a Random Forest model achieving 87%+ prediction accuracy; participated in code reviews and presented technical documentation and project findings to mentors and team members.