House and Rent Price Prediction System using Regression

Abstract

Real estate market analysis plays a crucial role in predicting house prices and rental trends. In recent years, several techniques have been developed to address this challenge, including machine learning (ML) algorithms, data mining approaches, and statistical models. There were a few issues which needed to be solved, like data quality and availability pose significant obstacles. Housing datasets often exhibit missing values, outliers, and inconsistent formats, which can hinder the performance of prediction models. Addressing these challenges requires careful data preprocessing techniques and collaboration with data providers and government agencies to ensure data accuracy and coverage. Existing systems face significant challenges that hinder their accuracy and reliability. Some of these challenges include limited availability of comprehensive and up-to-date data, complex market dynamics, and the need for efficient feature selection and model optimization. The proposed objectives includes Data collection and preprocessing, feature selection and engineering, Model selection and development along with evaluation, usability, comparison with existing systems, etc. By addressing these challenges, proposed system aims to provide more accurate and reliable predictions for real estate market analysis, aiding homeowners, tenants, and real estate professionals in making informed decisions.