International Research Journal of Education and Technology

Home prices increase every year and it indicates the current economic situation so there is a need for a system to predict Home sales in the future for both buyer and the seller. Here we use dataset of India across different cities and having more then 68,613 entries of test data and 28000 train data of housing sales in whole, India. This analysis includes the effect of markdowns on the sales and the extent of effects on the sales by size, price, area etc. has been analysed using different machine learning algorithms. Home sales prediction can help the developer determine the selling price of a Home and can help the buyer to arrange the right time to purchase a Home. Algorithms predict the output values based on input features from the data fed in the system and analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. There are three factors that influence the sales price of a Home which include physical conditions, concept and location.

An exact forecast of forthcoming development market interest, particularly the private market, is central imperative to strategy producers, as it could assist with forming procedures to develop/balance out the economy and fulfill the social requirements (at full scale level). In spite of that, a sensible forecast of future private interest is never a simple assignment, as it is represented by various social and monetary elements. In this paper, four proactive factor models are created and thought about for straightforwardly anticipating India private area a wide range of interest

Key Words: Forecast, Machine Learning Algorithms, Home Price, economy