# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 4 Results
### Dataset B--California Housing  
The California Housing Prices dataset is a popular dataset from Kaggle that contains information on the median house prices for various districts in California. The dataset contains a total of 20,640 records, with each record representing a different district in California. The dataset has a total of 10 columns, with each column representing a different attribute of the district. The attributes included in the dataset are as follows:  
* longitude: Represents the longitude coordinate of the district. * latitude: Represents the latitude coordinate of the district. * housing_median_age: Represents the median age of the houses in the district. The mean value is 28.64. * total_rooms: Represents the total number of rooms in the district. The mean is 2643.66. * total_bedrooms: Represents the total number of bedrooms in the district. The mean is 538.43
* population: Represents the total population of the district. The mean is 1425.48. * households: Represents the total number of households in the district. The mean is 499.54. * median_income: Represents the median income of the households in the district. The mean value is 3.87. * median_house_value: Represents the median house value in the district. The mean is 206,855. * ocean_proximity: Represents the proximity of the district to the ocean. It contains five different categories. \begin{table}
\begin{tabular}{c c c c} \hline
**Model Name** & **MAE** & **RMSE** & **SMAPE (\%)** \\ \hline
**KAN** & **35,861** & **52,158** & **16.53** \\ GRU–LSTM & 36,972 & 53,763 & 16.92 \\ CatBoost & 37,615 & 54,918 & 17.14 \\ XGBoost & 38,157 & 55,711 & 17.41 \\ LGB & 38,961 & 55,906 & 17.98 \\ MLP & 39,123 & 57,034 & 18.07 \\ Random forest & 46,124 & 67,341 & 21.16 \\ Linear regression & 46,451 & 68,176 & 21.65 \\ \hline \end{tabular}
\end{table} 
Table 2: Results in Dataset A.  
Figure 4: Comparison plot of predicted and actual values in Dataset A—KAN. Regarding the data cleaning process, rows that contained empty fields were removed. Additionally, 5% of the outliers with respect to the median house value were removed, including the minimum and maximum values. The categorical variable was encoded using one-hot encoding. Numerical variables were standardized using a standard scaler. Table 3 and Figure 5 below demonstrate the results sorted by performance (error), showing KAN outperforming significantly all of the existing state-of-the-art deep NN approaches. Traditional machine learning algorithms XGBoost and LGB have slightly better results because of the nature of the dataset. California housing has few input variables and also not so many rows, which makes it hard for NN-based models to get their full potential. Additionally, Figure 6 presents a comparison plot between the values predicted by KAN and the actual values. To address the concern regarding the limited input variables and dataset size of the California Housing dataset, which may constrain the potential of the model, an additional benchmarking experiment was conducted to evaluate the performance of KAN. The evaluation process included a comparison of KAN with XGBoost, the best-performing baseline, using an augmented version of the California Housing dataset. The augmentation process involved generating 3000 additional samples by applying Gaussian noise to the numerical features of randomly selected rows from the original dataset. Specifically, Gaussian noise with a standard deviation of 0.05 was added to these features, ensuring that the augmented samples retained the original data distribution while introducing diversity. These perturbed samples were then appended to the original dataset, resulting in an expanded dataset for  
Figure 5: Models’ performance in Dataset B.