Dynamic pricing of e-shop products through machine learning algorithms

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Creating a Bitcoin Lightning Network Enabled Energy Grid

This is the repository of our accepted CIKM 2022 paper "Prediction-based One-shot Dynamic Parking Pricing".

Optimize energy consumption and battery storage by identifying cheapest charging windows based on dynamic electricity prices

Creating a decision support system for a newly launched product.

STAD-GCN: Spatial-Temporal Attention-based Dynamic Graph Convolutional Network for Retail Market Price Prediction, pytorch version (ESWA 2024)

This project applies Q-Learing for dynamic pricing decision-making optimization

Agentes y Big Data aplicado a la optimización de recursos de movilidad urbana en la ciudad de Santander, España

Dynamically adjust cost of the rides in response to changing factors

🚖 A hybrid Machine Learning + Reinforcement Learning project that predicts dynamic ride pricing. Combines historical ride data (ML) with a Q-learning agent (RL) to recommend fair, adaptive prices based on demand, supply, and customer loyalty.

Research simulation comparing Thompson Sampling vs. Epsilon-Greedy algorithms in non-stationary hyper-inflationary markets. Reveals "Bayesian Inertia" phenomenon.

Dynamic pricing algorithm for Uber rides. Focus on dataclass usage and pipeline execution efficiency.

Comprehensive workflow for analyzing price trends in e-commerce platforms using SQL, Python, and Tableau. Includes data engineering, warehousing, dynamic pricing, and BI visualization.

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.

Adaptive pricing system for profit maximisation featuring frontend adapters, user modelling, APIs, and model evaluation.

Source code for "Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model"

A Django-based electronics store using price anchoring with fake competitors to boost conversions. Built with Bootstrap for a sleek UI. Features dynamic pricing comparisons to make discounts feel irresistible.

Fun university project @ IU (2020) for implementation of a smart service business model through dynamic tire rental pricing based on road quality data.

AI-powered dynamic pricing copilot with a Next.js 15 frontend, FastAPI backend, LangChain agent, and explainable ML via SHAP.