This project focused on applying various techniques in predictive data mining, specifically utilizing linear regression, decision tree, and random forest algorithms. Each experiment carried out served a distinct purpose, and the results shed light on the benefits of employing different techniques and conducting multiple comparisons.

The comparison highlighted the strengths and weaknesses of each model in capturing complex and non-linear relationships within the data. The Random Forest and Decision Tree models stood out for their ability to capture specific features and patterns present in the data, demonstrating a higher capability for predictive accuracy.

Takeaway: Employing multiple algorithms for predictive modeling provides valuable insights into the data, enabling a more comprehensive understanding of relationships and patterns. The Random Forest and Decision Tree models offer enhanced predictive power for identifying key features and trends, particularly in complex datasets.

Industry Importance: The application of advanced machine learning algorithms like Random Forest and Decision Trees has significant potential in the real estate and property analysis sectors. These models can improve property valuation accuracy and offer data-driven insights into market trends, making them indispensable tools for price prediction and investment decision-making.