Software Engineer and AI/ML enthusiast passionate about building intelligent systems and scalable applications. I enjoy solving challenging problems, developing full-stack applications, and leveraging machine learning to create impactful products.
- Building AI-powered solutions and modern web applications.
- Strong interest in Machine Learning and Software Engineering.
- Enthusiastic about Data Structures & Algorithms.
- Passionate about Product Engineering and Problem Solving.
- Exploring Generative AI and MLOps.
- Open to internships and collaboration opportunities.
| Domain | Proficiency | Details |
|---|---|---|
| Machine Learning | Advanced | Model development, feature engineering, evaluation |
| Deep Learning | Intermediate | Neural Networks, CNNs |
| NLP | Intermediate | Text classification, BERT, embeddings |
| Explainable AI | Intermediate | SHAP, feature importance |
| Data Analysis | Advanced | Pandas, NumPy, Matplotlib |
| Model Deployment | Intermediate | Flask, Streamlit |
| Computer Vision | Intermediate | MNIST digit recognition |
| Generative AI | Beginner | LLMs, embeddings, vector databases |
- Software Engineering Internships
- AI / ML Roles
- Open Source Contributions
- Full Stack Development Opportunities
- Collaborative Projects
Learning:
- Advanced DSA
- Machine Learning
- Deep Learning
- System Design
Building:
- AI Projects
- Full Stack Applications
- Scalable Backend Systems
Exploring:
- Generative AI
- MLOps
- LLM Applications
Open_To:
- Internship Opportunities
- Collaboration
- Open Source🏏 IPL Win Predictor
Machine Learning project designed to predict IPL match outcomes using feature engineering and explainable AI techniques.
| Attribute | Details |
|---|---|
| Stack | Python, Scikit-Learn, Pandas, NumPy, Streamlit |
| Scale | Historical IPL Dataset |
| Performance | High prediction accuracy |
| Explainability | SHAP Feature Importance |
| Impact | Improved interpretability of match predictions |
| Repository | Coming Soon |
- Extensive feature engineering.
- Created derived features like CRR vs RRR.
- Used Explainable AI techniques with SHAP.
- Identified venue bias and wicket pressure effects.
- Interactive prediction interface using Streamlit.
🎨 DrawChain
A blockchain-inspired collaborative drawing platform focused on creativity and transparency.
| Attribute | Details |
|---|---|
| Stack | React, JavaScript, HTML, CSS |
| Scale | Multi-user drawing platform |
| Performance | Responsive UI |
| Security | Client-side validation |
| Impact | Interactive creative collaboration |
| Repository | https://github.com/PankajKumar-11/DrawChain- |
- Real-time drawing experience.
- Modern and responsive interface.
- Canvas-based implementation.
- Interactive user experience.
- Modular frontend architecture.
🤖 AI Resume Screener
AI-powered system for evaluating resumes and matching them with job descriptions.
| Attribute | Details |
|---|---|
| Stack | Python, NLP, BERT, Streamlit |
| Scale | Automated candidate evaluation |
| Performance | Semantic similarity matching |
| Security | Input validation |
| Impact | Reduced manual screening effort |
| Repository | Coming Soon |
- Resume and job description analysis.
- Semantic similarity scoring.
- Candidate ranking system.
- NLP-based evaluation pipeline.
- Streamlit interface for ease of use.
🏨 Hotel Booking Web Application
Full Stack hotel reservation platform providing seamless booking and management functionalities.
| Attribute | Details |
|---|---|
| Stack | Node.js, Express.js, MongoDB, JavaScript |
| Scale | Dynamic booking platform |
| Performance | Efficient backend operations |
| Security | Authentication and validation |
| Impact | Streamlined reservation process |
| Repository | Coming Soon |
- User authentication.
- Hotel listings and booking management.
- REST API architecture.
- MongoDB database integration.
- Responsive design.
🔢 Handwritten Digit Recognition (MNIST)
Deep Learning model for handwritten digit classification using neural networks.
| Attribute | Details |
|---|---|
| Stack | Python, TensorFlow, Keras |
| Scale | MNIST Dataset |
| Performance | High classification accuracy |
| Security | Data preprocessing pipeline |
| Impact | Computer Vision application |
| Repository | Coming Soon |
- Image preprocessing.
- Neural network implementation.
- Model training and evaluation.
- Accurate digit classification.
- Visualization of predictions.
Software Developer | Independent Projects
2024 - Present
Building AI/ML systems and full stack applications while continuously improving software engineering and problem-solving skills.
- Developed machine learning applications.
- Built scalable backend systems.
- Designed responsive frontend interfaces.
- Applied explainable AI techniques.
- Practiced DSA and competitive programming.
- Contributed to personal and open-source projects.
| Recognition | Details |
|---|---|
| Problem Solving | Actively practicing DSA and Competitive Programming |
| Machine Learning | Built multiple end-to-end ML projects |
| Full Stack Development | Developed scalable web applications |
| Explainable AI | Applied SHAP-based model interpretability |
| Continuous Learning | Exploring Deep Learning and Generative AI |
