SVM
Classifying Success: Predicting Housing Market Trends with SVM and Random Forest
Boston Housing Classification with SVM and Random Forest
Uncovering Property Value Insights: This project delves into the Boston housing dataset to classify and predict whether a property’s value exceeds the median for owner-occupied homes. Using both Support Vector Machines (SVM) and Random Forest algorithms, we analyzed various property characteristics to make accurate predictions.
The project highlights the strength of combining powerful classification techniques to offer valuable insights into housing trends and market predictions.