HOUSE PRICE PREDICTION SYSTEM USING MACHINE LEARNING
Krish Kumar Roy, Krish Kumar Roy, Aditya Yadav, Krishna Kumar, Suraj Sharma, Shefali Bisht
Abstract
House price prediction has become an important problem in modern real estate analysis because prices are influenced by many interacting factors such as location, area, number of rooms, amenities, and market conditions. This paper presents a machine learning-based House Price Prediction System that processes structured property data, cleans and transforms the inputs, and trains regression models to estimate property prices with improved accuracy. The system follows a complete pipeline that includes data collection, preprocessing, feature selection, model training, evaluation, and prediction. Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn are used to support the workflow. Experimental analysis shows that the model can capture important pricing patterns and generate reliable estimates for new property records. The proposed approach reduces manual effort, improves consistency, and supports faster decision-making for buyers, sellers, and analysts.
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