House Price Prediction - Sivasankar, Bindu; Ashok, Arun P; Madhu, Gouri; S, Fousiya (International Journal of Computer Sciences and Engineering, 2020)
Machine learning plays a major role from past years in image detection, Spam recognition, normal speech command, product recommendation and medical diagnosis along it provides better customer service and safer automobile systems. This shows that ML is trend in almost all fields so we try to coined up ML in our project for betterment. Nowadays, people looking to buy a new home tend to be more conservative with their budgets and market strategies. The current systems main disadvantage is that the calculation of house prices are done without the necessary prediction about future market trends and price increase. The goal of the project is to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. In the present paper we discuss about the prediction of future housing prices that is generated by machine learning algorithm. In-order to select the prediction methods we compare and explore various prediction methods. To predict the future price, the previous market trends, price ranges and also upcoming development will be analyzed. Every year House prices increase , so there is a need for a system to predict house prices in the future. We create a housing cost prediction model in view of Machine Learning algorithm models such as Lasso Regression, Ridge Regression, Ada-Boost Regression, XGBoost Regression, Decision Tree Regression, Random Forest 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 developer and customer will be benefited by this model on determining the selling price of a house and helps the latter to arrange the right time to purchase a house.