House Price Prediction using ML

Author Name : Abhishek Kumar Sinha, Kiran Raj Suresh Malali, Ayush Dhar, Reddi Shiva Kumar, Krishna Sai Reddy Peddinti, Bhanu Prakash Reddy B, Mekala Mukhesh Chandra

ABSTRACT

The relationship between house prices and the economy is an important motivating factor for predicting house prices. Housing price trends are not only the concern of buyers and sellers, but it also indicates the current economic situation. Therefore, it is important to predict housing prices without bias to help both the buyers and sellers make their decisions. This project uses an open source dataset, which include 56 explanatory features and 2,985,217entries of housing sales in Los Angeles, CA. There are different machine learning algorithms to predict the house prices the project compares different feature selection methods and feature extraction algorithm with Support Vector Regression (SVR) to predict the house prices in three different counties in Los Angeles, CA. The feature selection methods used in the experiments include Recursive Feature Elimination (RFE), Lasso, Ridge, and Random Forest Selector. The feature extraction method in this work is Principal Component Analysis (PCA). After applying different feature reduction methods, a regression model using SVR was built. With log transformation, feature reduction, and parameter tuning, the price prediction accuracy will be achieved. The benefit of applying feature reductions is that it helps us to pick the more

The motivation for choosing SVR algorithm is it can accurately predict the trends when the underlying processes are non-linear and non-stationary.

This project is guided by these questions: Which features are important for predicting price of houses? How to select those features in the data to achieve a better performance? Which parameters in SVR have better performance in predicting house price?