Enhancing Multiple Linear Regression for Price Prediction: A PCA-Integrated Approach

The conventional multiple linear regression model is limited by its inability to process high-dimensional datasets, susceptibility to multicollinearity, and challenges in modeling non-linear relationships. To address these issues, alternative methodologies are required. Principal Component Analysis (PCA) is a widely employed technique for dimensionality reduction that transforms high-dimensional datasets into lower-dimensional spaces, thereby mitigating the issues associated with high-dimensionality and extracting principal features for dimension reduction. Additionally, PCA can alleviate multicollinearity concerns, enhancing the reliability of regression models. In this study, we employ PCA in conjunction with multiple linear regression to predict prices, achieving notably precise outcomes. This hybrid method not only elevates the accuracy of predictions but also provides substantial support in applications such as stock price forecasting, market pricing strategies, product positioning, and profit maximization. The integration of PCA with multiple linear regression holds significant practical relevance, offering a methodological advancement that can improve the decision-making accuracy and efficiency for investors.