Dynamic Pricing is an application of data science that involves adjusting the prices of a product or service based on various factors in real time. It is used by companies to optimize revenue by setting flexible prices that respond to market demand, demographics, customer behaviour and competitor prices.

Analyze product data for an online sports retail company to optimize revenue.

Applying product segmentation, demand forecasting, and revenue optimization to increase online retailer revenue

Understanding customer buying patterns and retention behavior.

Analysis of KMS' Order data from 2009-2012. Includes pivot tables, charts, and insights on sales, regions, customer profitability, and shipping costs. Provides recommendations to optimize operations and enhance revenue for Kultra Mega Stores.

Predictive Revenue Engine & Shark Detector | Recovering Unattributed Luxury Segment Income via Algorithmic Lead Scoring (Random Forest) and LTV Regression. Built with BigQuery, Parquet, and Streamlit.

Comprehensive customer acquisition systems covering modern lead generation, conversion optimization, and customer onboarding methodologies. Build sustainable customer acquisition processes.

A Reinforcement Learning agent built with Scikit-Learn and NumPy that optimizes product pricing strategies in competitive markets, achieving 28% revenue lift in simulations.

Fintech Optimization Engine: Achieving 32% Conversion Lift & 5.6% Revenue Growth via Rigorous A/B Testing. Features Power Analysis (80% Standard), Live Market API Ingestion, and Automated Executive Decision Logic.

From data engineering to behavioral clustering, predictive modeling, and a live BI dashboard.

An enterprise-grade system that automatically discovers open tenders, analyzes RFP requirements, generates competitive proposals, and submits responses to maximize revenue through automated bid management.

🔔 Receive Apple App Store revenue notifications in real-time on your device with RevenueBell, a lightweight Cloudflare Worker script.

Optimización de ingresos e-commerce mediante priorización ICE/RICE y análisis de Tests A/B. Implementación robusta con Mann-Whitney U, corrección de Bonferroni y filtrado técnico de outliers (P96/P97) para identificar el impacto real en conversión. Desarrollado con Python y Pandas 3.0 (PyArrow backend) para procesamiento de alto rendimiento.

A falta de traducción a inglés y piloto para Negocio

SQL and Python-powered analysis of airline data to uncover insights on occupancy rates, revenue performance, and pricing strategies for improved profitability.

End-to-end customer churn prediction system using machine learning, feature engineering, and probability calibration. The project goes beyond prediction by optimizing decision thresholds based on business profit, implementing customer segmentation, lift analysis, and explainable AI (SHAP) to support data-driven retention strategies.

Analyzed hotel booking cancellations, implemented dynamic pricing for a 15% reduction, initiated targeted marketing for 12% rise in peak month bookings, and optimized booking sources. Enhanced revenue and strategy through data-driven insights.

An alternative to Nomis solutions on e-Cars case through Logistic Regression with Lasso regularization

End-to-end retail sales analysis using Excel | Revenue optimization insights | Interactive dashboards with actionable business intelligence

🏨 Dynamic Pricing Dashboard for Hotel Revenue Optimization - AI-driven pricing strategies with machine learning models and interactive visualizations