## 3 Methodology  
One of the first tasks in this study was to gather smart city datasets, ensuring their diversity and reliability. The collected data encompassed various indicators, such as economic development, infrastructure, technology adoption, environmental sustainability, and quality of life metrics. The collected data were subjected to meticulous preprocessing, including eliminating missing values common in large datasets.  
The dataset was then split into two groups to explore the relationships between variables and identify possible patterns effectively. The first group consisted of the top 20 smart cities globally, determined by established ranking criteria. This subset allowed for a detailed examination of highly successful cities and the identification of best practices. The separation enables us to compare the characteristics and patterns observed in top-performing cities against the broader landscape of smart cities represented by the second group. The remaining cities comprised a second group, which provided an understanding of how the factors identified in top-performing cities manifest across a wider range of urban environments. This approach offers a balanced and comprehensive examination of smart city development, combining a focused analysis of the leading examples with a broader perspective of the overall smart city landscape.  
The primary dataset was randomly divided into training and testing sets to create predictive models and to evaluate their performance. The training set comprised 70% of the data and was utilized to develop and train the study's models, while the remaining 30% was reserved for assessing the model's accuracy and generalizability.  
A RF algorithm was used to determine the most influential factors contributing to the success of smart cities. This ensemble learning method is prized for its effectiveness in handling complex datasets and providing insights into the significance of features. By constructing multiple decision trees, the random forest algorithm calculates each feature's mean decrease in impurity (Gini index), indicating its impact on the model's predictive power. Features were ranked based on their mean decrease in Gini values, facilitating the identification of the most crucial variables. A subset of the top-ranked features was selected for further analysis. This feature selection process reduced the data dimensions, improved the model's interpretability, and enhanced its predictive performance.  
Multiple linear regression was utilized to investigate the relationships between essential features and other relevant variables. This statistical method estimated the impact of each independent variable on the dependent variable while controlling for the effects of other variables. By examining the regression coefficients and their statistical significance, insights into the complex interplay among different factors were gained. A stepwise selection procedure was utilized to optimize the regression model and ensure its validity. This iterative process of adding or removing variables based on statistical criteria resulted in an economic model with the best predictive performance.  
The regression model's assumptions, which included linearity, normality of residuals, homoscedasticity, and absence of multicollinearity, were carefully examined before conclusions were drawn. Diagnostic tests were conducted to assess the validity of the assumptions and, if necessary, address any violations.  
Based on the analysis, conclusions were drawn regarding the key factors most influential in smart city success. The identified features and their interrelationships provided valuable insights into the complex dynamics of smart city development. Based on these findings, recommendations were formulated, offering guidance to policymakers, urban planners, and practitioners in smart cities. Figure 1 presents a complete flowchart for the study's framework.