<![CDATA[This study explores optimal pricing strategies for the female fashion sector through the application of advanced data science methodologies. Utilizing a dataset of 4,272 entries, comprising various attributes such as original prices, promotional prices, and discount percentages, we employed regression models to predict promotional pricing. The research highlights Ridge Regression as the most effective model, balancing high accuracy with reduced overfitting. The model achieved an R-squared (R²) value of 0.9999999999999678, a Mean Absolute Error (MAE) of 4.31×10−6, and a Mean Squared Error (MSE) of 4.89×10 −11, demonstrating its robustness and reliability. The study's findings indicate that dynamic pricing and tailored discount strategies can significantly enhance revenue and profitability. High-value items are best priced with moderate discounts, maintaining higher promotional prices, while low-value items benefit from aggressive discounting to drive sales volume. Sensitivity analysis further supported these strategies by showing that a 10% increase in original prices proportionally increased promotional prices, while a 10% increase in discount percentages led to lower promotional prices, affecting sales performance differently across product categories. Practical implications for e-commerce businesses include implementing dynamic pricing, developing targeted discount strategies, and timing promotions strategically. Regular sensitivity analysis and continuous model validation are recommended to adapt to market changes effectively. Future research should consider broader datasets, advanced modeling techniques, external market factors, and customer segmentation to enhance the generalizability and applicability of pricing strategies across different sectors. This research underscores the importance of data-driven approaches in optimizing digital marketing strategies, offering actionable insights that can significantly boost revenue and profitability in the female fashion sector.]]>
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