Abstract:
Data mining investigates hidden patterns of information from various perspectives to categorize into useful data, which is collected and assembled in data warehouses, efficient analysis, data mining algorithms, decision-making assistance, and other data requirements to reduce costs and generate revenue.
The most powerful and popular categorization and prediction tool is the decision tree. A decision tree is a flowchart-like tree structure with internal nodes representing attribute tests, branches reflecting test outcomes, and leaf nodes (terminal nodes) containing class labels.
Data Mining can accurately estimate or predict this. Decision trees handle non-linear data well. Decision trees are used in business, law, engineering, etc. Real estate value is predicted by this system. It helps determine property beginning prices depending on geography. Breaking down past market patterns, value ranges, and anticipated improvements will estimate future costs.
The Housing values Prediction System uses Data Mining to anticipate house values and chooses the most accurate models. This technology allows admins to log in with a username and password. Administrators can add, update, remove, and view training data. The admin can see registered users and their info.
Users must register and log in with a username and password to access information. The user can edit their profile. Search for a city or state and pick other characteristics to anticipate house prices. Selecting a city or state displays house price reports for the past five years and projections for the following year.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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