Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India
- Title
- Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India
- Creator
- Jayadharshini P.; Ragunath R.; Prabhakaran D.; Krishnasamy L.; Abinaya N.; Priyanka S.
- Description
- As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics. 2024 IEEE.
- Source
- TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Decision Tree Algorithm; Gradient Boosting Technique; Linear Regression Model; Random Forest Algorithm; Rental Rate
- Coverage
- Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, India; Ragunath R., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Prabhakaran D., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Krishnasamy L., Christ University, Department of Cse, Bangalore, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, Erode, India; Priyanka S., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038427-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jayadharshini P.; Ragunath R.; Prabhakaran D.; Krishnasamy L.; Abinaya N.; Priyanka S., “Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19159.