A Comparative Study of House Price Prediction Using Machine learning and Deep learning Techniques

It is always a dream for everyone to own a house or flat., House prices increase every year, so there is a need for a system to, predict house prices in the future. House price prediction system will, help the developer to determine the selling price of a house and also, help the customer to make correct decision to look for a suitable, property to purchase. Most of the buyers expect the house based on, budget, prime location, property type, size etc. This paper presents, a comparative study on house price prediction using three distinct, models that is Artificial Neural Network (ANN), Naive Bayes, and, Random Forest (RF). Through comparative analysis, we evaluate the, performance of these models on a real-world housing dataset. The, outcome of this research contributes to advancing the field of real, estate prediction models that utilize the advantages of models. These, methods improved accuracy and efficiency in predicting house, prices, aiding house owners for reselling, real estate professionals, and investors facilitating informed decision in the ever-changing, real estate market.