An Ensemble of ML Algorithms for Predicting Accurate House Price

In our ecosystem, real estate is clearly a distinct industry. Predicting house prices, significant housing characteristics, and many other things is made a lot easier by the capacity to extract data from raw data and extract essential information. Daily fluctuations in housing costs are still present, and they occasionally rise without regard to calculations. According to research, changes in property prices frequently have an impact on both homeowners and the real estate market. This study aims to propose a system for “House price prediction” by using Machine Learning. This house price prediction is an approach that can precisely estimate the price of a new house based on its attributes using previous data on house features (such as square footage, number of bedrooms and bathrooms, location, etc.) and their corresponding prices.

To analyze the key elements and the best predictive models for home prices, literature research was conducted. Five algorithms namely linear regression, support vector machine, Lasso regression, Random Forest and XGBoost have been applied in this study to predict house prices using a dataset of real estate properties. Exploratory data analysis (EDA) was conducted for the house price prediction project. The analyses' findings supported the usage of artificial neural networks, support vector regression, and linear regression as the most effective modeling techniques. Results also showed that Random Forest and XGBoost can handle high-dimensional datasets, capture complex relationships, and effectively manage feature interactions by their superior performance. This study's results also imply that real estate agents and geography play important roles in determining property prices. Finding the most crucial factors affecting housing prices and identifying the best machine learning model to utilize for this research would both be greatly aided by this study, especially for housing developers and researchers.