House Interiors Suggestion

• Author(s): Deep Bansal
• Paper ID: 1704667
• Page: 441-444
• Published Date: 16-06-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 12 June-2023

Abstract

Real Estate is a clear industry in our ecosystem. The ability to extract data to extract relevant information from raw data makes it very useful to suggest house interiors, important housing features, and much more. Housing prices continue to change from day to day and decorating it with our interiors makes it much more unaffordable. Research has shown that fluctuations in housing prices often affect homeowners and the housing market. Literature research is done to analyze the relevant factors and the most effective models for suggesting house interiors. In this study we have proposed a machine learning algorithm that combines the best features of existing algorithms by using resnet50. The ResNet-50 model, a state-of-the-art convolutional neural network, is employed to extract high-level features from a vast collection of interior design images. These images are curated from various sources, including professional design portfolios, online platforms, and magazines. The model's pre-trained weights allow it to capture intricate patterns and characteristics that define different interior design styles. This study will be of great benefit, especially to housing interiors developers and researchers, to find the most important criteria for determining interiors prices and identify the best machine learning model used to conduct research in this field.

Keywords

CNN, House interior suggestion, KNN, ResNet50

Citations

IRE Journals:
Deep Bansal "House Interiors Suggestion" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 441-444

IEEE:
Deep Bansal "House Interiors Suggestion" Iconic Research And Engineering Journals, 6(12)