## 3 Dataset
Although it has well-defined objectives and assessment criteria for comparing various techniques and models, this dataset has some limitations, including:  
* The data was collected on a specific fixed date, emphasising the importance of frequent updates to maintain its reliability over time. * The presence of duplicated and irrelevant entries may compromise the quality and accuracy of the models. * An uneven distribution of labels may lead to biased outcomes. * The dataset typically includes outliers or extreme observations, which are common in the real estate market. If these observations are not handled properly, they may affect the results of the models. The shape of the label exhibits an upward skew (see Figure 1), showing a longer tail on the right side of the distribution. The significant decline in frequency as rent increases, with a small number of observations in the upper rent categories (e.g., rentals above 5000), implies the presence of outliers compared to average rental prices. This non-symmetry may negatively affect model estimation. Hence, when forecasting rental prices, it is crucial to consider the skewness of the rent values to normalise the data. Two feature selection algorithms, CatBoost and PSO, were used to analyse feature significance within the dataset. The findings led to the creation of two unique feature sets, Sets 1 and 2, which demonstrate the significance of various characteristics in determining rental rates. Below is a breakdown of the pipeline used by both approaches to extract features for Sets 1 and 2. After splitting the dataset into features (X) and the target variable (y) with an 80-20 training and testing split, feature set 1 was generated using the CatBoost algorithm. The CatBoost Regressor was configured with RMSE as the loss function, 100 iterations, a depth  
Figure 1: The shape of the target. of 6, and a learning rate of 0.1. Following training, the get_feature_importance() function was used to generate the feature importance scores, prioritising the top 10 features that contributed the most to prediction accuracy. Feature set 1 was then constructed using these top features. PSO was employed to extract features from feature set 2 after pre-processing the features using normalisation and categorical handling. Several subsets of attributes were assessed using an objective function based on MSE, incorporating feature weights in each iteration. These weighted variables were used to train a basic linear regression model, with MSE serving as the optimisation guide. PSO parameters were defined to explore the solution space and identify the most optimal weights, incorporating cognitive and social factors. After averaging the relevance of each characteristic across several rounds, the top ten features were chosen for set 2 based on their highest ratings. The differences between these two sets stem from the various techniques applied for feature selection. Set 1 is more model-specific, as it leverages the gradient boosting process of CatBoost to discover features that minimise loss. In contrast, feature set 2 employs a general-purpose PSO strategy that optimises feature weights to reduce prediction errors. Some variables, such as \"baseRent\" and \"serviceCharge\", are scored highly in both sets, while other attributes differ, indicating that various approaches capture distinct portions of the data for predictions. These differences imply that combining techniques might result in a more thorough and reliable feature selection process, with PSO providing flexibility across various model types and CatBoost being more suitable for tree-based models. This comparative research shows that different methods have distinct effects on feature selection, which can enhance model design and improve prediction accuracy. Figure 2 shows the importance of features selected by these tools. The vertical axis (y-axis) lists the names of the features in the dataset, while the horizontal axis (x-axis) represents the importance scores of the characteristics.