ML Price Optimizer

Optimize Prices with Machine Learning

Train Random Forest or XGBoost models on your historical data to predict demand at any price point. Find revenue-maximizing prices backed by ML-driven insights.

Demand Predictions Predict sales at any price

Feature Importance Identify key demand drivers

Model Metrics R-squared, RMSE, MAE

Optimal Pricing Revenue maximization

🌲 Random Forest

⚡ XGBoost

Two powerful ML algorithms for demand modeling

⚡ How It Works

📤

Upload

Historical data

→

🤖

Train

ML model

→

💰

Optimize

Find best price

R Shiny Application

Run locally with full data privacy

✨ Key Features

✓ Random Forest ensemble modeling
✓ XGBoost gradient boosting
✓ K-fold cross-validation
✓ Feature importance visualization
✓ Price simulation with predictions
✓ Revenue optimization grid search
✓ Export predictions and results

🏢 Use Cases

E-commerce Pricing Retail Optimization Subscription Tiers Dynamic Pricing Promotion Planning Demand Forecasting

📦 What You Get

R Shiny App User Manual Sample Datasets Data Templates

Ready for ML-Driven Pricing?

Dr. Koray Cosguner

Founder & Principal Consultant
Associate Professor of Marketing

miaow.consulting@gmail.com

Kelley School of Business

#1 Online MBA · #4 UG Marketing (U.S. News)

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