Accommodation Price Prediction Using Machine Learning


  • Rohit Suryawanshi
  • Nikki Thakur
  • Kailas Waghmare
  • Monika Kshirsagar
  • Rutika Gaurkar
  • Mukta Wagh


Housing Price Prediction; Linear Regression; Machine Learning; Artificial Intelligence.


Accommodation Price Prediction is used to estimate the variably changing house prices. Since housing price is strongly correlated with factors such as location, area, population and it requires other information apart from to predict individual housing price. The problem faced by the customers in finding houses has been an issue of all time and is increasing due to malpractices by the builders and construction companies which tends to problem for customer only. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they are less concerned about the performance of individual models and neglect the less popular yet complex models. This model takes in consideration of the varies datapoints and modulates it through the various machine learning algorithms like linear regression model and convolution neural networks which checks the image recognition and converts to data and recognition of image points. The dataset developed gets validated through the regression algorithm and gives a prediction to maximum accuracy and efficiency.


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How to Cite

Rohit Suryawanshi, Nikki Thakur, Kailas Waghmare, Monika Kshirsagar, Rutika Gaurkar, & Mukta Wagh. (2022). Accommodation Price Prediction Using Machine Learning. International Journal of Progressive Research in Science and Engineering, 3(04), 127–131. Retrieved from




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