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• Create Date 1 August 2025
• Last Updated 1 August 2025

House Price Prediction App

1R. BHANU SANKAR ,2PEELA ABHISHEK

1Assistant Professor, Department Of MCA, 2MCA Final Semester,

1Master of Computer Applications,

1Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India.

Abstract:

Predicting housing prices is a vital task in real estate and urban planning, influencing decisions by buyers, sellers, developers, and policymakers. This project presents a machine learning-based prediction system for estimating residential property prices using classification algorithms. Historical housing data across multiple cities in India was utilized, containing attributes such as area, number of bedrooms and bathrooms, year built, location, garage, and property condition. The system employs supervised machine learning models Random Forest, Gradient Boosting, and Linear Regression to forecast house prices. Among these, the Gradient Boosting model achieved the highest accuracy. The selected model is integrated into an interactive web application built using Streamlit, providing users with instant price predictions and model comparisons. Through intuitive visualizations and real-time predictions, this system aids individuals and professionals in making informed property decisions.

Index Terms: House Price Prediction, Machine Learning, Regression Models, Gradient Boosting, Random Forest, Linear Regression, Real Estate Analytics, Streamlit Application, Housing Data, Supervised Learning, Property Valuation, Data-Driven Decision Making, Feature Engineering, Predictive Modeling, Urban Planning.