House Price Index (HPI) is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from HPI to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.
Objective & Data
The competition goal is to predict sale prices for homes in India. You’re given a training and testing data set in csv format as well as a data dictionary.
Our training data consists of 13350 examples of houses with 79 features describing every aspect of the house. We are given sale prices (labels) for each house. The training data is what we will use to “teach” our models.
The test data set consists of 1,459 examples with the same number of features as the training data. Our test data set excludes the sale price because this is what we are trying to predict.
Front End – Anaconda IDE
Backend – SQL
Language – Python 3.8
•Hard Disk: Greater than 500 GB
•RAM: Greater than 4 GB
•Processor: I3 and Above
House Price Prediction using Machine Learning House Price Prediction using Machine Learning Predicting House Prices with Machine Learning Project PPT Report