Exploring House Prices and Features with Tableau

Author(s)	Koppula Purna Venkateswara Rao, Palle Sravani, Porumalla Gowthami, Chandragiri Veeraswamy, Mutyala Satish
Country	India
Abstract	The real estate sector requires accurate housing price estimation for effective decision making. Traditional property valuation methods are often manual and inefficient. With the availability of large real estate datasets, data analytics and machine learning techniques can improve housing market analysis and price prediction. This study proposes a housing price prediction and visualization system that integrates Tableau dashboards with machine learning models. Tableau is used to visualize housing market trends, while algorithms such as Linear Regression, KNN, and Random Forest are applied to predict housing prices. The dataset is preprocessed, and models are evaluated using MAE, RMSE, and R² Score. The trained model is deployed using a Flask web application, where users can input property details and obtain predicted prices. The system also provides future price estimation and profit analysis based on growth rate. The proposed system enables effective housing market analysis and supports data-driven decision making.
Keywords	Housing Price Prediction, Tableau Visualization, Data Analytics, Machine Learning, Real Estate Analytics, Ensemble Learning.
Field	Computer > Data / Information
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-31
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.73152

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