# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 5 Discussion, Implications, and Limitations  
The results of this study demonstrate the strong performance of the proposed dynamic pricing model. Among the tested machine learning algorithms, the linear support vector machine (SVM) achieved the highest average accuracy (86.92%), indicating its effectiveness in classifying pricing decisions. This high accuracy underscores the suitability of the SVM for the dataset used and highlights the potential of the model to support real-time pricing decisions in e-commerce. The success of the linear SVM can be attributed to its robustness in handling datasets with high-dimensional feature spaces and complex decision boundaries. The algorithm's ability to maximize the margin between classes makes it particularly effective for smaller datasets, like the one used in this study, where overlapping data points pose a challenge. The kernel trick used in nonlinear variants further enhances its capacity to model non-linear relationships, giving it an edge over simpler algorithms. The dataset used in this study, consisting of 500 transactions from an e-commerce store, presented specific challenges for the machine learning models, including a relatively small sample size, a mix of numerical and categorical variables, and potentially overlapping or non-linear decision boundaries. These characteristics impacted the performance of the algorithms differently:  
1. The Naive Bayes classifier operates on the assumption that all features are conditionally independent, given the target variable. While this simplification enables fast computations and scalability, it is rarely valid in real-world datasets. In this study, dependencies between features such as storage costs, demand dynamics, and price relative to competitors violated this assumption, leading to suboptimal classification accuracy. Furthermore, the model struggled with the small sample size, where the underlying feature distributions were not well represented. 2. Decision trees, while highly interpretable, are prone to overfitting, particularly with smaller datasets. Overfitting occurs when the model becomes too tailored to the training data, capturing noise instead of general patterns. Additionally, decision trees are sensitive to small variations in data, leading to unstable predictions. Despite their ability to handle mixed data types, their performance in this study was limited by the dataset's size and variability. 3. The k-nearest neighbor (k-NN) algorithm classifies instances based on their proximity to other data points, which requires meaningful distance metrics. In this dataset with mixed numerical, categorical, and ordinal features, defining appropriate distances proved challenging. Additionally, the relatively small dataset limited the algorithm's ability to form reliable neighborhoods, reducing its classification accuracy. The results indicate that the proposed dynamic pricing model can effectively support managerial decision-making in e-commerce. The linear SVM algorithm's high accuracy demonstrates its utility in automating pricing decisions in real time, which is crucial in dynamically changing market conditions. By integrating criteria related to customers, enterprises, and market conditions, the model provides managers with a comprehensive tool for price management tailored to specific business needs. The model's limitations primarily concern the quality and availability of data. Its effectiveness relies on large, well-balanced datasets that enable accurate algorithm training. A lack of sufficient data or uneven distribution (e.g., the class imbalance problem) may negatively affect prediction accuracy. The model is scalable to larger datasets, making it applicable to larger e-commerce enterprises or more complex environments. However, such scalability comes with challenges:  
1. Larger datasets require greater computational power and appropriate technological infrastructure to ensure efficient processing. 2.