HOUSE PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

Abstract

The real estate market is dynamic and influenced by factors such as location, property size, amenities, and economic conditions. Accurate housing price prediction is crucial for enabling homebuyers, sellers, and investors to make informed decisions. This study aims to develop a reliable predictive model using machine learning (ML) algorithms. The data, sourced from Kaggle, undergoes thorough preprocessing, including missing value imputation, outlier detection, and feature selection. The research applies various ML techniques, including Linear Regression, Random Forest, Gradient Boosting Machine (GBM), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. The dataset is divided into 80% for training and 20% for testing to ensure comprehensive model evaluation and accurate performance assessment.