HOUSE-PRICE-EATIMATIOM

Create a model to estimate house prices based on the number of bedrooms, square footage, etc.

Abstract The real estate market is a complex and dynamic ecosystem with fluctuating property values influenced by a myriad of factors. Accurate house price prediction is crucial for buyers, sellers, and real estate professionals to make informed decisions. This project aims to develop a robust and reliable house price prediction model using machine learning techniques. The dataset used for this study comprises various features, such as property size, location, number of bedrooms, bathrooms, and other relevant attributes. Through data preprocessing, feature engineering, and the application of machine learning algorithms, we seek to create a predictive model that can estimate house prices with a high degree of accuracy.