I'm a Computer Science and Engineering student at Lovely Professional University, specialising in Artificial Intelligence & Data Engineering, with hands-on experience in AI/ML projects, data analytics, preprocessing, and application development.
I enjoy exploring how machine learning, computer vision, NLP, and data engineering can come together to solve practical problems.
- 🤖 Exploring machine learning, model evaluation, and applied AI.
- 🧠 Building projects involving computer vision, NLP, and retrieval-augmented generation.
- 📊 Working with data processing, exploratory analysis, and predictive modelling.
- ☁️ Exploring cloud-based AI workflows and API-driven applications.
- 🎯 Focused on strengthening my Python, SQL, and AI/ML engineering skills.
| Area | Focus |
|---|---|
| Machine Learning | Classification, predictive analytics, feature analysis, model evaluation |
| Computer Vision | Image classification, object detection, traffic-scene understanding |
| NLP | Text processing, named-entity recognition, language-model applications |
| Generative AI | Embeddings, semantic retrieval, document-based question-answering |
| Explainable AI | Understanding model predictions and feature importance |
| Data Analytics | Data cleaning, EDA, statistical analysis, dashboard reporting |
| Languages |
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| Data Science |
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| ML Frameworks |
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| Computer Vision & NLP |
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| Generative AI |
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| Backend & Databases |
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| Cloud & Developer Tools |
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2026
- Performed data preprocessing and exploratory analysis on business datasets using Python, Pandas, and NumPy.
- Contributed to KPI and exception-detection logic for workforce analytics.
- Worked with FastAPI, React, SQLAlchemy, and SQLite.
- Developed analytics dashboards for workforce utilisation and operational monitoring.
- Maintained documented code and version-controlled work using Git and GitHub.
2026
- Prepared a strategic roadmap and feasibility study for FusionDrive AI.
- Documented a proposed multimodal sensor-fusion architecture.
- Developed a conceptual risk-assessment and evaluation framework.
- Prepared a simulation and testing strategy.
- Evaluated AI applications across automotive safety, maintenance, and autonomous-driving research.
2025
- Preprocessed and validated structured datasets using Python and Pandas.
- Conducted exploratory data analysis and designed functional test cases.
- Developed Power BI dashboards for reporting and data visualisation.
2024
- Collaborated on building an OLA Clone ride-hailing application as part of a hands-on team project.
- Explored core ride-hailing features, including ride matching, GPS tracking, and user-friendly application design.
- Gained practical experience in application development, team collaboration, and translating requirements into functional features.
| Project | Description | Technologies |
|---|---|---|
| FusionDrive AI | Conceptual automotive AI project exploring sensor fusion, road-hazard detection, and collision-risk assessment. | Python · AI/ML · Computer Vision |
| Traffic Scene Understanding with NLP Alert System | Combines traffic-scene image analysis and NLP to support data-driven alerts. | PyTorch · TensorFlow · BERT · spaCy · FastAPI |
| TechWave — Layoff Contagion Radar | Predictive analytics project exploring layoff-risk patterns and model interpretability. | Pandas · XGBoost · SHAP · Streamlit |
| RAG Application | Document-based question-answering system using embeddings and semantic retrieval. | Python · OpenAI API · NLP |
| PromptShield AI | Project exploring prompt safety and safer AI input handling. | AI Safety · Prompt Engineering |
Only FusionDrive AI is linked directly here because its repository URL is confirmed. Add the other project links once their repository URLs have been verified.
- Oracle Cloud Infrastructure 2025 Certified AI Foundations Associate
- Master Generative AI & Generative AI Tools — Udemy
- AI Fluency Framework & Foundations — Anthropic
- AI Foundations — OpenAI
- Python problem-solving and coding interview patterns
- NumPy and Pandas for AI/ML workflows
- SQL joins, CTEs, and window functions
- Model evaluation and practical machine-learning workflows
- Git, Docker, and reproducible development practices
Building practical AI projects, learning continuously, and growing toward AI/ML engineering. 💙