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Leonardo - Software Developer

Location: Kuala Lumpur, Malaysia
Contact: [email protected]
LinkedIn: Leonardo001

About Me

I am a passionate software developer with a background in Artificial Intelligence and a focus on creating impactful solutions using cutting-edge technologies. With a Bachelor's degree in Computer Science (AI) and hands-on experience in both frontend and backend development, I enjoy solving complex problems and collaborating with cross-functional teams to bring innovative ideas to life. My interests lie in machine learning, computer vision, and natural language processing.

What I’m Good At

• Frontend Development: Proficient in Vue.js, HTML, CSS, and JavaScript. I love building responsive, user-friendly web apps.
• Backend Development: Experience with Express.js and Prisma with PostgreSQL. Focused on building scalable and efficient systems.
• AI & Machine Learning: Skilled in developing predictive models, text classification systems, and computer vision applications using tools like YOLO, Random Forest, and Naive Bayes.
• Data Analysis & Visualization: Strong knowledge in cleaning and processing large datasets, identifying trends, and building visualizations to uncover insights.

Interesting Projects

1. AI Cooking App

• Tech Stack: Vue.js, Quasar, Express.js, Prisma (PostgreSQL), YOLO
• Description: Developed an AI-powered culinary assistant that recognizes ingredients in real-time and suggests recipe substitutions. The app uses computer vision and natural language processing to create a seamless cooking experience.
• Impact: Achieved over 90% accuracy in real-time ingredient recognition, improving accessibility and cooking convenience for users.

2. Predictive Modeling for High-Risk Restaurant Violations

• Tech Stack: Python, PostgreSQL, Pandas, Seaborn
• Description: Built a predictive model to identify high-risk locations and violations in restaurant inspections. Cleaned and processed over 50,000 records, developed data visualizations, and improved model accuracy by 20%.
• Impact: Helped regulators and restaurants focus on potential risks and improve food safety.

3. E-commerce Text Classification & Sentiment Analysis

• Tech Stack: Python, NLTK, Random Forest, Naive Bayes
• Description: Worked with a team to build a text classification system for customer reviews, categorizing sentiment with up to 98.2% accuracy. Implemented advanced data preprocessing to improve classification results.
• Impact: Provided actionable insights for e-commerce platforms to improve customer service and product offerings.

Skills

• Programming Languages: Python, JavaScript, HTML, CSS
• Frameworks: Vue.js, Express.js, Quasar
• Databases: Prisma (PostgreSQL), MySQL (learned but not used in projects)
• Machine Learning: YOLO, Random Forest, Naive Bayes, NLP
• Tools: Git, Visual Studio Code, Unix, Microsoft Office

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