MS in Physics (AI Specialization) | Applied Machine Learning & Data Science
I am an Applied Machine Learning Scientist with a strong foundation in Physics, AI, and Data Science.
My work focuses on building end-to-end ML pipelines — from data preprocessing, EDA, and feature engineering to model development, evaluation, and deployment-ready workflows.
I bring a unique mix of analytical depth from Physics and practical AI skills, enabling me to design solutions that are both rigorous and impactful.
- Built a complete regression pipeline to predict house prices.
- Includes data cleaning, exploratory analysis, feature engineering, and multiple regression models (Linear Regression, Random Forest, etc.).
- Evaluated models with RMSE, MAE, and R² to ensure robust performance.
- Analyzed Uber ride data to model demand forecasting and operational efficiency.
- Developed predictive models and generated insights for business-driven decision-making.
- End-to-end workflow: EDA → Feature Engineering → ML Models → Interpretability.
- Programming: Python
- Libraries & Frameworks: pandas · numpy · scikit-learn · matplotlib · seaborn
- Core Expertise: regression · classification · feature engineering · model evaluation · visualization
- Development: Jupyter Notebook · Git/GitHub
- Applied ML for transportation, pricing, and customer analytics.
- Physics-inspired modeling approaches in AI.
- Building interpretable and scalable ML systems.
- 🌐 GitHub: amitkumar0651
- 💼 LinkedIn: (add your LinkedIn profile link here)
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