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Sneaker Price Profitability Prediction
This is a data analysis project , focusing on over 99,956 transactions to develop a robust predictive model. Utilizing machine learning algorithms such as Linear Regression, CART, and XGBoost, impressive model accuracies was achieved, with the XGBoost model reaching an RMSE as low as 0.151.
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The predictive model played a pivotal role in creating Key Performance Indicators (KPIs) and metrics to track product success. This enabled stakeholders to make informed decisions, enhancing product management and strategic planning based on empirical data.
This project not only demonstrated the power of machine learning in deriving actionable insights but also underscored the importance of data-driven decision-making in product success.
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