International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
House Price Prediction Based On Machine Learning Chandan Barik, Deepak Kumar Sahu, Rajat Dewangan, Priyanka Devi B.Tech Student, Dept. Of Information Technology, Govt. Engineering College, Bilaspur, CG., India B.Tech Student, Dept. Of Information Technology, Govt. Engineering College, Bilaspur, CG., India B.Tech Student, Dept. Of Information Technology, Govt. Engineering College, Bilaspur, CG., India Assistant Professor, Dept. Of Information Technology, Govt. Engineering College, Bilaspur, CG., India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This study explores the utility of house price
budgetary constraints, facilitating a smoother and more informed homebuying process.
prediction in facilitating informed decision-making for both developers and potential buyers. While the House Price Index (HPI) is a widely employed tool for estimating housing price fluctuations, the intricate relationship between housing prices and various factors such as location, size, and demographics necessitates additional data for accurate individual price predictions. Despite the abundance of research utilizing traditional machine learning methods to forecast housing prices, there is a notable lack of focus on evaluating the performance of individual models and a tendency to overlook more sophisticated, albeit less mainstream, approaches.
1.1 Navigating the Complexity of Market Dynamics Real estate markets are subject to intricate webs of economic, social, and environmental forces. Crafting a model capable of comprehensively capturing and analyzing these dynamics poses a formidable challenge.
1.2 Data Variability and Quality Global house price data may originate from a myriad of sources, each varying in reliability and completeness. Tackling the task of cleaning, preprocessing, and harmonizing data across diverse regions and formats presents a substantial hurdle.
The Global House Price Prediction System project aims to enhance efficiency, accuracy, consistency, and risk mitigation in decision-making processes related to house price predictions. Key objectives include streamlining processes, leveraging historical data and machine learning for accuracy, ensuring consistency in decision-making, minimizing lending risks, creating a user-friendly interface, implementing robust security measures, and conducting thorough testing and validation.
1.3 Temporal and Spatial Dynamics Housing markets exhibit temporal and spatial fluctuations, with distinct regions experiencing unique trends and cycles. A robust model must adeptly accommodate these variations to furnish precise predictions across heterogeneous geographical landscapes
Key Words: Correlation Analysis, Mitigation, Regression, Scalability, Supervised Learning, Outliers, Price Index.
1.4 Feature Selection and Engineering
1. INTRODUCTION
The discernment of pertinent features and their seamless integration into the model is pivotal for ensuring prediction accuracy. Feature engineering plays a pivotal role in transforming raw data into meaningful predictors capable of capturing underlying patterns.
Typically, the House Price Index captures the aggregated fluctuations in residential property values. To enhance the ease of house hunting for families, we have refined the process by soliciting specific criteria such as desired square footage, number of bedrooms, and bathrooms. Employing preloaded datasets and innovative data features, this paper explores practical data preprocessing and inventive feature engineering techniques. Additionally, it introduces a regression technique within machine learning to forecast house prices. The primary advantages of this project lie in addressing the conservative budgeting and market strategies of prospective homebuyers. It aims to streamline operations and enhance efficiency, offering customers a swift and reliable method for determining house prices. By ensuring transparency and fairness, the project seeks to prevent users from being misled or exploited. Ultimately, its goal is to empower users to search for homes within their
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1.5 Model Generalization Crafting a model that not only excels on historical data but also extrapolates effectively to unseen data is paramount. Striking a balance to mitigate overfitting or underfitting, which could lead to erroneous predictions, represents a formidable challenge
1.6 Interpretability and Explainability Fostering transparency in the predictive process holds paramount importance, particularly in the realm of real estate where decisions carry significant ramifications. The
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