AI/ML Engineer Β· Machine Learning Β· Data Science Β· Python
π New Delhi, India Β |Β π§ rajbhadani9897@gmail.com Β |Β π LinkedIn
- π B.Sc. Computer Science @ Deshbandhu College, University of Delhi (2022β2026)
- πΌ AI/ML Intern @ Edunet Foundation (IBM SkillsBuild & Shell-AICTE)
- π Data Science Intern @ Tamizhan Skills (RISE Program)
- π Former President, CyberNaut CS Society β led 30-member team, organized SYNTAX'24 tech fest
- π± Currently deepening expertise in Generative AI and MLOps
- π¬ Ask me about: Machine Learning, EDA, Python, Power BI, NLP
Languages & Tools
ML & Data Science
| # | Project | Description | Tools |
|---|---|---|---|
| π | Customer Segmentation Using K-Means | Identified customer groups from transaction data to enable personalized marketing | Python, Scikit-learn, Pandas, Matplotlib |
| π | E-Commerce Data Insights | EDA & visual dashboards revealing revenue trends and product performance | Python, Power BI, Excel, Pandas |
| π | Sales Forecasting with Linear Regression | Predicted future sales from historical data to support demand planning | Python, Scikit-learn, Pandas, Matplotlib |
| π | Student Performance Analytics Dashboard | Interactive dashboard identifying factors affecting academic outcomes | Python, Power BI, Pandas |
| π | Greenhouse Gas Emission Prediction | Predictive models on environmental datasets for sustainability analysis | Python, ML, Data Visualization |
| π¦ | COVID-19 Data Analysis & Prediction | Time-series analysis and forecasting of global pandemic case trends | Python, Pandas, Matplotlib, ML |
| π | Social Network Analysis in Public Health | Applied SNA centrality metrics to reveal hidden health & security relationships | Python, Network Analysis |
| π₯ | Early Stage Diabetes Risk Prediction | Classification model on patient indicators for early diabetes detection | Python, Scikit-learn, Pandas |
- β Introduction to Data Science
- β Fundamental AI Concepts
- β Python with Advanced AI
- β Introduction to Generative AI
- β Introduction to Responsible AI
"Building intelligent systems, one model at a time."
