Prediction of House Price Using Machine Learning Algorithms

People are very careful when they want to buy a new house with market strategies and their budgets. The objective of this paper is to predict the house prices for non-house holders based on their aspirations and financial provisions. By analyzing different parameters like area of the house, square feet of the house, no of floors in the house etc. This research work has utilized the dataset from Kaggle.. An analysis is performed by applying advanced machine learning regression techniques such as Linear regression, KNN Regression, Random Forest Regression, Decision Tree Regression, Extra Trees Regression etc. to attain the most efficient and least error driven regression technique. From the analysis performed, an observation has been made that Catboost Regression Algorithm has outperformed other algorithms. The model predicts the final output with respect to correlated attributes in the dataset.