🌐 Language / Idioma: English Version 🇺🇸 | Versão em Português 🇧🇷
Author: Lucas Nascimento Oliveira
Role: Data Scientist
Click on the links below to explore projects by seniority level, technical complexity, and generated business ROI.
| Level | Project | Technical Focus | Methodology & Business ROI | Documentation |
|---|---|---|---|---|
| Advanced | Financial Fraud Analytics | Cloud MPP (BigQuery) & SQL | Distributed query optimization for cost reduction (OPEX) in corporate Data Lake. | Case Study |
| Advanced | BigQuery LTV Prediction | BQML & Advanced SQL (Cohort/RFV) | Native linear regression modeling in Data Warehouse to forecast future revenue by customer cohorts. | Case Study |
| Advanced | Aviation Ops Risk | Random Forest & SHAP (XAI) | Mitigating $960k USD (R$ 4.8M) in operational risks in aviation networks using explainable AI. | Case Study |
| Advanced | Hospital Risk Audit | Isolation Forest (Outliers) | Automated identification of R$ 2.4M ($480k USD) in medical billing anomalies and hospital claims. | Case Study |
| Intermediate | Customer Segmentation | K-Means Clustering | RFV behavioral segmentation for customer acquisition and retention cost optimization in marketing campaigns. | Case Study |
| Intermediate | Market Basket Analysis | Association Rules & Bundling | Apriori algorithm applied to transactions for average ticket optimization through product bundling. | Case Study |
| Foundational | Pricing Intelligence | Big Data Viz & PCI Index | Calculation of the Price Competitiveness Index (PCI) on 370k+ daily competitor pricing records. | Case Study |
| Foundational | Geomarketing Expansion | Geospatial Density Analytics | Density mapping of UK EV charging infrastructure to optimize expansion CAPEX. | Case Study |
Tip
Master Access: For a consolidated view of the entire technical journey, please refer to walkthrough_master.md.
Note
The financial projections below represent estimates and study cases for business validation under simulated scenarios, showing how data science algorithms drive EBITDA and reduce OPEX.
Estimated Impact in Simulated Scenario: This portfolio demonstrates the practical application of data science techniques focused on business process optimization and risk mitigation, estimated at up to R$ 7.2M+ (approx. $1.4M+ USD) of projected value under simulated scenarios (case studies), with potential OPEX reduction of up to 15% and simulated predictive accuracy of 82% in critical decisions.
- Impact Methodology (R$ 7.2M+ / $1.4M+ USD - Simulated Scenario Estimate):
- R$ 4.8M ($960k USD): Projected annual revenue protected under simulated aviation network scenario via the predictive mitigation model for severe delays (Aviation Ops Risk).
- R$ 2.4M ($480k USD): Estimated savings in simulated medical billing audit and entry errors identified by anomaly detection AI (Hospital Risk Audit).
graph TD
A["Data Ecosystem: Projected Impact $1.4M+ USD (Simulated Scenario)"] --> B["Estimated Logistic Risk Mitigation $960k USD"]
A --> C["Estimated Health Billing Protection $480k USD"]
B --> B1[Delay Prediction via Random Forest]
B --> B2[Explainability of Root Causes via SHAP]
C --> C1[Audit/Double-Billing Detection via AI]
C --> C2[Automated Auditing via Isolation Forest]
The project follows a professional structure geared towards scalability and production-grade standards:
/data/processed: Cleaned and scored datasets ready for consumption./src/ml: Machine Learning engine (model_engine.py)./src/reporting: Corporate visualization module (viz_factory.py)./models: Trained models persisted in.joblib.
- Clone the repository.
- Create a virtual environment:
python -m venv .venv. - Install dependencies with pinned versions:
pip install -r requirements.txt. - Processed datasets are already available in
/data/processed/.
Lucas Nascimento Oliveira
Data Scientist: generating intelligence and business value through data.