Shivam Navadia
Sydney,Australia
Summary
Graduate student in Information and Communication Technology with a major in Artificial Intelligence and a strong foundation in machine learning, deep learning, and software development. Experienced in building AI/ML models for health and e-commerce applications, including life expectancy prediction and sentiment analysis. Proficient in Python, TensorFlow, PyTorch, and NLP techniques. Seeking AI/ML Engineer roles to apply analytical and problem-solving skills in real-world scenarios.
Overview
2
2
years of professional experience
Work History
Retail Assistant
David Jones
07.2024 - 04.2025
- Assisted customers, maintained displays, and processed sales as retail assistant. Led a team to manage inventory and fulfill orders as logistics supervisor. Strengthened teamwork, leadership, and problem-solving skills across customer-facing and operational tasks.
Hotel Front Desk
Avenue hotel
03.2023 - 06.2024
- Managed front desk operations, welcoming guests, overseeing bookings, and addressing inquiries. Enhanced communication and customer service skills within dynamic hospitality setting. Strengthened multitasking capabilities and resolved guest issues with professional care.
Education
Master of Information and Communication Technology - Artificial Intelligence
Western Sydney University
Sydney, NSW12-2026
Bachelor of Computing -
Australian National University
Canberra, ACT06.2024
Skills
- Programming Languages: Python, Java, Haskell, HTML, CSS
- ML Frameworks: PyTorch, Scikit-learn, TensorFlow
- NLP & AI: NLTK, SpaCy, Transformers
- Database: MySQL
- DevOps/MLOps: CI/CD Pipelines, Docker
- Tools: Pandas, Matplotlib, Seaborn, Git
Affiliations
Life expectancy prediction
- Developed a machine learning model to predict life expectancy based on global health indicators such as immunization rates, BMI, GDP, and healthcare expenditure
- Utilized Python, Scikit-learn, and pandas to analyze data from WHO datasets
- Trained and optimized a random forest and linear regression model
Twitter sentiment analysis
- Developed an NLP model to analyze customer reviews from an e-commerce platform
- Used Python, NLTK, SpaCy, and Scikit-learn for text preprocessing and sentiment classification
- Achieved 89% accuracy using a logistic regression classifier
- Visualized sentiment trends to help businesses understand customer feedback
Timeline
Retail Assistant
David Jones
07.2024 - 04.2025
Hotel Front Desk
Avenue hotel
03.2023 - 06.2024
Master of Information and Communication Technology - Artificial Intelligence
Western Sydney University
Bachelor of Computing -
Australian National University
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