# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Preprocessing Data  
This subsection discusses the preprocessing steps performed on the data prior to analysis. Data preprocessing is a crucial step in any data-driven project, helping to clean, normalize, and transform the raw data into a format suitable for further analysis or model training. These steps are essential to ensure the quality and reliability of the results derived from the data. We outline the specific preprocessing techniques used in our study, detailing why each step was necessary and how it contributed to the overall analysis.  
2.1 Variable Modification and Reduction (in This Paper, the Terms 'Variable' and 'Feature' Are Used Interchangeably)  
During preprocessing, we conducted both variable elimination and modification to optimize our dataset for further analysis. We identified a set of variables, as presented in Table 1, that either contained duplicate information or were not applicable to our study. The variables 'SIGUNGU_CD' (Si-Gun-Gu Code), 'EMDL_CD' (Eub-Myeon-Li Code), 'CLL' (Classification of Land Lot), 'MNO' (Main Number), and 'SNO' (Sub Number) encapsulate address-related information, which is already sufficiently represented by the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Feature Description** \\ \hline SIGUNGU\_CD & Municipality Code 1  \\ EMDL\_CD & Submuniciipality Code 2  \\ CLL & Land Lot Classification (1: Regular, 2: Mountain) \\ MNO & Land Lot Number (Main) 3  \\ SNO & Land Lot Number (Sub) 3  \\ ADRES & Address Name (Legal District) \\ HUS\_TP & Type of Multi-unit Housing (Apartment, Multi-family, Studio) \\ COMP\_NM & Complex (Building) Name \\ BLDG\_YEAR & Year of Construction \\ FLR & Floor Information \\ XUAR & Exclusive Area (m\({}^{2}\)) \\ CTRT\_YRMTH & Contract Year and Month \\ CTRT\_DAY & Contract Day \\ TRANJECT\_TYPE & Transaction Type (Sale, Jeonse 4, Monthly Rent) \\ DLNG\_AMOUNT & Sale Price (in 10,000 KRW) \\ GRNITE & AMOUNT & Security Deposit (in 10,000 KRW) \\ MTHUNT\_AMOUNT & Monthly Rent (in 10,000 KRW) \\ NEAR\_SUBW\_NM & Nearest Subway Station Name \\ NEAR\_SUBW\_DIST & Straight-line Distance to the Nearest Subway Station \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset specification of metro-adjacent residential transactions provided by the Korea Real Estate Board.  
'ADRES' (Address) variable. To avoid redundancy, these overlapping variables were eliminated. Additionally, we removed variables such as 'GRNTE_AMOUNT' (Guarantee Amount) and 'MTHRNT_AMOUNT' (Monthly Rent Amount) which were irrelevant to sale transactions from our analysis. This streamlined the dataset and ensured that our model would only be trained on features pertinent to our research objectives.  
After variable elimination, we modified the 'HUS_TP' (House Type) variable, which represents the property type of each transaction. 'HUS_TP' consists of three unique categories: 'Apartment', 'Studio', and 'Multi-family residential'. To allow the machine learning algorithms to process these categorical data more effectively, we applied the one-hot encoding technique. This process converts each category into a separate column assigned a binary value of 1 (presence of the feature) or 0 (absence of the feature). This transformation allowed our model to utilize the property type data effectively without any inherent order or priority.