Technical Portfolio
Featured Machine Learning Projects
End-to-end data science solutions: NLP, inference latency optimization, and containerized cloud deployment.
Lead Data Scientist & Deployment Lead
August 2026 FinanceAI – Intelligent Financial Diagnostic & NLP Platform
End-to-end Machine Learning platform engineered during the Oracle Next Education (ONE) Hackathon. Features an NLP transaction classification pipeline, a multivariate financial health diagnostic model, and multi-container cloud deployment.
Evaluation Metric
Weighted F1
Dataset Scope
240k+ Trans.
Inference Latency
< 0.8 ms (ONNX)
Architecture
4 Containers
Key Technical Highlights:
- • Categorized 240,000+ financial records across 10 classes using Scikit-learn (TF-IDF + Linear Classifier) evaluated via Macro & Weighted F1-Scores.
- • Serialized Scikit-learn pipelines to ONNX (skl2onnx) for native in-memory execution inside a Java 17 / Spring Boot backend (< 0.8ms latency).
- • Enforced strict data leakage prevention via temporal splits and optimized hyperparameters using GridSearchCV across 36 candidates (Stratified CV).
- • Orchestrated 4 microservices (Vue 3, Spring Boot, MySQL, Jupyter) using Docker Compose and deployed to Oracle Cloud Infrastructure (OCI Compute).
Python Scikit-Learn NLP (TF-IDF) ONNX Java Spring Boot Docker Compose MySQL Oracle Cloud (OCI)
Independent Data Practitioner
2026 Predictive Modeling & Tabular Data Analytics Engine
Modular experimentation environment for large tabular datasets. Implements IQR outlier filtering, continuous feature transformation, and rigorous trade-off analysis across classification and regression algorithms.
Cross Validation
5-Fold CV
Outlier Detection
IQR Filter
Evaluation Metrics
ROC-AUC / F1
Key Technical Highlights:
- • Multidimensional exploratory data analysis and statistical visualization using Pandas, Matplotlib, and Seaborn.
- • Automated preprocessing pipelines: missing value imputation, StandardScaler normalization, and One-Hot Encoding.
- • Evaluated baseline models (Logistic Regression, Decision Trees) using ROC-AUC curves, Confusion Matrices, and weighted F1-Scores.
- • Clean, reproducible technical documentation adhering to PEP 8 code standards.
Python Pandas NumPy Scikit-Learn Matplotlib Seaborn MySQL Jupyter Lab