Dimensionality Reduction (Linear Technique Using California Housing Data)

Abstract

Most automatic pricing systems for existing homes are based solely on specific text data, such as neighborhood and number of rooms. The final price will be determined by a human agent who visits the house and visually evaluates. In this article, we suggest removing the visual features of the images from the house and merging them with the text information of the house. The data set consists of 535 model houses from the state of California in the United States. Our experiments showed that adding visual features increased and reduced the average error by an order of magnitude compared to plain text properties. Linear techniques for reducing dimensionality, especially the analysis of key components, are often used in data analysis to interpret high-dimensional datasets. These linear methods may be appropriate for analyzing nonlinear process data in the housing system. Recently, a lot of techniques have been developed to reduce nonlinear dimensionality, which can be a potentially useful tool for identifying small varieties in climatic data sets due to nonlinear dynamics. In this paper, I used linear techniques to do data analysis for prediction.

Keywords: Dimensionality Reduction, Statistics, Housing Data, Predictions, Linear and Nonlinear