HOUSE PRICE PREDICTION

Abstract

Because house prices rise every year, a mechanism to forecast future house values is required. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. Physical conditions, concept, and location are only a few of the aspects that determine the price of a home. Usually, House price index represents the summarized price changes of residential housing. While for a singlefamily house price prediction, it needs a more accurate method based on location, house type, size, build year, local amenities, and some other factors which could affect house demand and supply. A practical and composite data pre-processing, creative feature engineering method is investigated with limited dataset and data features. This model is used to predict the house prices so as to cut down the complications faced by the customers. The present method where customers reach real-estate agents and search for houses in their budget, and should analyze whether a particular price is accurate or not. To overcome this our proposal is used. This system makes optimal use of the Machine Learning Algorithms. By extracting data from datasets of different houses, preprocessing the data and model is built using that data using Regression. The algorithm used for the model building is KNN (K Nearest Neighbor) Algorithm. This system design is modularized into various categories.