Retail Price Optimization using Machine Learning

In today’s competitive retail landscape, pricing plays a critical role in influencing consumer behavior and driving business profitability. Traditional pricing strategies often rely on manual adjustments or basic heuristics, which may not reflect real-time market conditions or competitor trends. This project aims to address these challenges by developing a data-driven Retail Price Optimization system that leverages machine learning to suggest optimal pricing decisions based on empirical insights. The primary objective of this study is to build an intelligent application that analyzes retail pricing data in comparison with competitor pricing, historical trends, and product-specific attributes across multiple categories. At the core of the solution is a Decision Tree Regression Model, selected for its interpretability and suitability in handling non-linear relationships. This model is trained on a dataset comprising features such as product category, cost, competitor price differences, and historical pricing information, enabling it to predict ideal price points that maximize revenue while maintaining competitive positioning.