Electronics and Communication Engineering Undergraduate | Machine Learning | Software Development

• Machine Learning and Applied AI
  Frontend Development
• Data Structures, Algorithms, and Competitive Programming

• C
• C++
• Python
• JavaScript
• TypeScript
• React

• Libraries: NumPy, Pandas, Scikit-learn, Matplotlib
• Concepts: Regression, Feature Engineering, Model Evaluation
• Tools: Jupyter Notebook, Google Colab

• Frontend: HTML5, CSS3, JavaScript
• Frameworks: React
• Focus: Clean UI, responsiveness, maintainable code

• Git, GitHub
• VS Code
• Jupyter Notebook

Machine learning–based system for predicting residential property prices using structured datasets. Highlights

• Data preprocessing and exploratory analysis
• Feature engineering and model selection
• Regression-based predictive modeling
• Performance evaluation and visualization

Frontend prototype illustrating a digital workflow for reporting and tracking civic issues.

Highlights

• Intuitive and responsive user interface
• Issue categorization and workflow visualization
• Frontend-focused system design

Contributor during the IEEE IGT-DUW Open Source Program, working on UI improvements and issue resolution.

Contributions

• UI styling and layout enhancements
• Bug fixes and issue handling
• Collaborative development using GitHub

• Strong focus on data structures and algorithms
• Regular practice of:
  • Arrays, Linked Lists, Stacks, Queues
  • Recursion, Dynamic Programming
  • Time and space complexity optimization
• Applying algorithmic thinking to real-world problems

• Advancing machine learning projects beyond basic regression models
• Building scalable frontend applications using React and TypeScript
• Strengthening DSA and competitive programming foundations
• Contributing to meaningful open-source projects

• LinkedIn:(https://www.linkedin.com/in/amrit-bhardwaj-50836528a/)
• Email: [email protected]

Focused on building strong fundamentals and impactful software systems.