Feature selection

Regression analysis

Elastic net regularization

Mathematics

Operations research

Regression

Statistics

World Wide Web

Machine learning

Random forest

Econometrics

Artificial intelligence

Economics

Set (abstract data type)

Programming language

Lasso (programming language)

Computer science

Linear regression

House price

Boosting (machine learning)

Gradient boosting

People are careful when they are trying to buy a new house with their budgets and market strategies. The objective of the paper is to forecast the coherent house prices for non-house holders based on their financial provisions and their aspirations. By analyzing the foregoing merchandise, fare ranges and also forewarns developments, speculated prices will be estimated. The paper involves predictions using different Regression techniques like Multiple linear, Ridge, LASSO, Elastic Net, Gradient boosting and Ada Boost Regression. House price prediction on a data set has been done by using all the above mentioned techniques to find out the best among them. The motive of this paper is to help the seller to estimate the selling cost of a house perfectly and to help people to predict the exact time slap to accumulate a house. Some of the related factors that impact the cost were also taken into considerations such as physical conditions, concept and location etc.

House Price Prediction Using Regression Techniques: A Comparative Study