Multi-layered Deep Learning Perceptron Based Model for Predicting Drug Price Changes

Product pricing is a critical task that has a profound impact on demand and the target audience. Setting the price of an existing product is even more challenging, as it can significantly affect business growth, customer purchasing patterns, and brand perception. In this paper, we propose an approach that leverages a deep learning Multi-layer Perceptron (MLP) Neural Network to predict changes in drug prices. Our model differs from existing approaches as it focuses on utilizing the stated reasons for price changes rather than product or market attributes for price change prediction. The MLP-based model is designed to learn complex patterns and dependencies within the drug price data, providing a data-driven solution for accurate forecasting. The text field containing the reason for the price change is pre-processed and then fed into the MLP neural network. We employ a Continuous Bag of Words approach to convert the text into a numerical format for model development. The model is trained on a diverse dataset, and its performance is evaluated using various metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), which indicate the model's predictive accuracy. The results demonstrate that the MLP-based model excels in predicting fluctuations in drug prices, showcasing its ability to adapt to the dynamic nature of the pharmaceutical market.