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.
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.
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).
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.
Revenue intelligence and pricing optimization analytics system for forecasting, pricing strategy, elasticity analysis, and business decision support.
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.
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
SNHP (snhp.dev, operated by gametheory.dev / github.com/ryuxik and listed in the a2aregistry.org harvest as "Negotiation Copilot for Agents") is free, LLM-free game-theory negotiation math for AI agents: a hosted FastAPI "Game Theory Layer" with 74 operations served anonymously as OpenAPI 3.1 on snhp.dev that returns the math-optimal next move in…
AI pricing model which works for E-commerce business model which has suppliers and clients
Motor de revenue management hotelero: control de inventario EMSR-b, modelo de cancelacion (XGBoost, AUC 0.84) e ingreso esperado ajustado por riesgo. Dashboard en Streamlit.
PPO-based reinforcement learning dynamic pricing engine with SHAP explainability, trained on real e-commerce transaction data
Profit-aware pricing optimization with demand modeling, price elasticity analysis, constrained price search, and A/B test rollout planning.
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.
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.
Data-driven SaaS pricing optimization using ML to maximize LTV/CAC ratios. Employs Random Forest, clustering, and elasticity analysis on 4,222 customers to recommend tiered pricing strategies that balance revenue growth with sustainable churn rates.
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