Enhancing Predictive Modeling in Kuala Lumpur Real Estate: A Comprehensive Data Preprocessing and Feature Engineering Approach

Abstract

The real estate market in Kuala Lumpur exhibits complex dynamics influenced by various factors. This paper presents a comprehensive approach to enhance the prediction of real estate prices in Kuala Lumpur utilizing a dataset of 49,416 records with advanced data preprocessing techniques, exploratory data analysis (EDA), and predictive modeling. Initially, the dataset undergoes rigorous preprocessing including handling missing values through property type-specific mean imputation, label encoding categorical variables, and standardization to ensure uniformity and compatibility for analysis. Subsequently, EDA techniques are employed to gain insights into the dataset's characteristics and relationships among variables. To improve model performance and interpretability, feature selection is performed based on Mutual Information (MI) score and correlation metrics. This aids in identifying the most relevant features for predicting real estate prices in Kuala Lumpur. Additionally, feature engineering techniques are applied to create two new features that capture nuanced aspects of the real estate market. The predictive modeling phase employs a Linear Regression algorithm to forecast real estate prices. Leveraging the preprocessed data and optimized feature set, the model aims to accurately predict property prices based on available features. The linear regression model offers interpretability, enabling stakeholders to understand the driving factors behind price variations. Through a rigorous methodology encompassing data preprocessing, exploratory analysis, feature selection, engineering, and predictive modeling, this study contributes to enhancing the accuracy and interpretability of real estate price prediction in Kuala Lumpurthat applies advanced machine learning methods. The findings offer valuable insights for real estate investors, policymakers, and stakeholders to make informed decisions in this dynamic market landscape.