Summary
Work History
Education
Skills
Timeline
Projects
Co-curricular activities
Reference
Generic

Sadhana Aryal

Auburn,NSW

Summary


Developed technical and analytical skills in fast-paced engineering environment. Adaptable and quick to learn, with focus on problem-solving and project management. Seeking to leverage these transferrable skills in new field to drive innovation and efficiency.

Work History

Full Stack Web Developer Intern

Compsoft Technologies
04.2022 - 07.2022
  • Developed responsive web applications using HTML, CSS, and JavaScript to enhance user experience.
  • Assisted in database management and CRUD operations utilizing MySQL for data storage solutions.
  • Collaborated with team members to troubleshoot and debug code, improving application functionality.
  • Conducted user testing sessions to gather feedback and implement design improvements effectively.
  • Created interactive UI components utilizing ReactJS, enhancing overall usability and user engagement.
  • Leveraged modern frameworks like Node.js and Express.js for efficient server-side development.

Education

Bachelor of Science - Computer Science And Engineering

CMR INSTITUE OF TECHNOLOGY
Banglore India
01-2023

Skills

  • Strong work ethic
  • Active listening
  • AutoCAD
  • Team leadership
  • Engineering software
  • Creative thinking
  • Computer programming
  • Excellent problem-solving skills
  • System troubleshooting
  • Git version control

Timeline

Full Stack Web Developer Intern

Compsoft Technologies
04.2022 - 07.2022

Bachelor of Science - Computer Science And Engineering

CMR INSTITUE OF TECHNOLOGY

Projects

Disease prediction using Machine Learning 

This project focuses on building a disease prediction system using machine learning techniques. The dataset used was the Disease-Symptom Knowledge Database, which was preprocessed and cleaned using Pandas to ensure data quality and consistency. For prediction, a Naive Bayes Classifier was implemented, leveraging its efficiency in handling categorical data and probabilistic relationships between symptoms and diseases.

The trained model demonstrated strong performance, achieving an accuracy of 95% in predicting diseases based on symptom inputs. The system highlights the potential of machine learning in assisting early diagnosis and decision-making in healthcare, providing a reliable and scalable approach to medical prediction tasks.

Co-curricular activities

  • Technical seminar: Artificial Intelligence and COVID-19 : Present state and future vision.
  • Online courses: Java(Udemy), Artificial Intelligence(NPTEL).

Reference

Provide upon request.

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Sadhana Aryal