Machine Learning-Based House Price Forecasting
Santushti Betgeri, Monisha Mohan, Dimple Thakar, DOI : 10.37591
[This article belongs to Current Trends in Information Technology(ctit)]
Keywords
Statistical Models, XGBoost Model, Statistical Models, Data Analytics, Risk Assessment
Abstract
This research endeavors to craft a predictive model leveraging machine learning to estimate the market value of houses in Delhi. By integrating Python and its powerful libraries—pandas for data processing, Plot for interactive visualizations, scikit-learn for implementing machine learning algorithms, XGBoost for boosting the model’s prediction accuracy, to evaluate the model’s performance cross- validation techniques are used. An interactive user interface is created using a Flask web application to enter characteristics of a house and according to that application will forecast the price of house. This project sets out to equip users with a dynamic tool for determining house prices based on essential property attributes. The initiative underscores the potential of machine learning technologies in transforming the real estate sector by enabling more precise property valuation, enhancing market analysis, and bolstering investment and risk assessment strategies. Through the application of sophisticated data analysis and predictive modeling techniques, the project aims to provide valuable insights for real estate professionals, investors, and analysts, facilitating informed decision-making and fostering profitable investment opportunities.
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