pricing-optimization

The official Python client library for the ParityVend API. Add location-based pricing to your Python applications and take your business to a global level.

E-commerce pricing optimization & dynamic user profiling

A machine learning project that uses an XGBoost model to predict optimal pricing strategies for Walmart products, incorporating confidence scores.

ML-powered pricing optimization for UK coffee shops. R²=0.997 accuracy with weather integration, SHAP explainability, and ethics guardrails. Historical backtest proves 16% margin lift (£17,959 annual opportunity).

Dynamic Pricing Optimization using Reinforcement Learning and Q-Learning to learn revenue-maximizing pricing strategies in a custom OpenAI Gym environment, featuring demand simulation and an interactive Streamlit dashboard.

An end-to-end MySQL analytics pipeline optimized for Zepto's quick-commerce catalog data. Features structural schema alterations, data cleaning (paise to INR conversion), and 20 business intelligence queries tracking revenue opportunities, inventory footprints, pricing anomalies, dead stock, and supply chain health.

Mathematical-modeling solution for residential elevator pricing, cost allocation, and optimization analysis.

Comprehensive market analysis of Athens Airbnb listings delivering actionable Tableau insights for strategic decision-making.

Revenue intelligence and pricing optimization analytics system for forecasting, pricing strategy, elasticity analysis, and business decision support.

End-to-end retail analytics solution unlocking $10.9M in revenue through churn prediction, customer segmentation, and pricing optimization algorithms. Built with Python, Scikit-learn, and Streamlit.

B2B distribution profitability forecasting + customer segmentation + margin-based pricing recommendations. Three connected models (LightGBM forecasting + KMeans segmentation + iso-elastic pricing engine), counterfactual simulation against fixed-discount baseline, full mypy + ruff + 34 pytest cases.

AI pricing model which works for E-commerce business model which has suppliers and clients

contains our team submission for Hackathon Bank Indonesia. Problem that we want to solve is the unfairness of fish price. In summary, we propose to (1) using geospatial ML when to decide the next location of cold storage (2) build app to help fish auction decide the fair price and monitor the recommended storage location based on stock management

Autonomous AI agent that analyzes revenue and return signals across product categories, reasons about pricing opportunities, and self-determines when to act — auto-applying HIGH confidence price adjustments while queuing uncertain decisions for human review.

End-to-end Price Optimization Engine using Random Forest. Simulates price elasticity and competitor data to prescribe optimal pricing strategies for revenue maximization. Built with Python & Streamlit.

Profit-aware pricing optimization with demand modeling, price elasticity analysis, constrained price search, and A/B test rollout planning.

Analyzed multi-city Airbnb data to solve a key real estate investment problem: identifying which property types, pricing strategies, locations, and amenities generate the most consistent bookings and predictable returns for investors.

Price optimisation for retail paint SKUs using OLS elasticity, XGBoost demand modelling, quasi-experiment, and A/B test design. Identifies +$1.83M–$5.02M annual gross margin opportunity across a 30-store chain.

Buynomics is a Cologne, Germany-based software company providing an AI-powered Revenue Growth Management (RGM) platform for consumer goods, retail, telecom, and consumer software companies.

Ergo (ergo Offers, Inc.) is a New York-based e-commerce pricing company that lets online shoppers name their own price. Its flagship product is a Shopify app that adds a "Make an Offer with ergo" button to product pages: a customer submits the price they are willing to pay and how long they are willing to wait, and the merchant accepts or rejects…