## 2 Materials and Methods
Serving as the starting point, this phase ensured the acquisition of high-quality data. Leveraging the Python'requests' library, they seamlessly interfaced with the API, extracting meaningful statistics regarding confirmed urban issues within a specified timeframe and geographic location. Step 2: Check Data (.py). The researchers embarked on a comprehensive examination, integrating the extract, transform, load (ETL)/extract, load, transform (ELT) methodology. Employing the Pandas library, they systematically scrutinized the dataset's structure and intrinsic attributes. This ETL/ELT-driven exploratory analysis yielded valuable insights into data types, forming a basis for subsequent processing and enhancement within the sophisticated data lake infrastructure. ETL involves extracting data from the source system, transforming it into a format that can be used by the digital twin, and then loading it into the digital twin. The ELT approach involves extracting data from the source system, loading it into the digital twin, and then transforming it as needed. Step 3: Convert the 'Reported' Column to Datetime Format (.py). Recognizing the importance of temporal precision in urban analytics, the researchers transformed the'reported' column into a datetime format. This not only established a standardized temporal reference but enriched the features of the data set. Step 4: Correct a Row of Coordinates (.py). Addressing the imperative of spatial accuracy in urban studies, the methodical approach to coordinate correction played a pivotal role in ensuring data integrity. This step validated each entry in the 'loc' column for adherence to the expected format, contributing to the spatial reliability of our dataset. Step 5: Coordinates to Area and New Column (.py). Delving into the geospatial context, this work executed a precise conversion of coordinates to human-readable area names, further enhancing the spatial granularity of the dataset. OpenLayers, the chosen open-source mapping library, facilitated reverse geocoding, attributing each citizen report to its corresponding urban area, a crucial step for robust spatial analyses within the data lake environment. Step 6: Weird Characters to Greek (.py). Addressing encoding intricacies is vital for uniform linguistic representation. The application of 'utf-8-sig' encoding has harmonized character encoding complexities, resulting in a linguistically coherent dataset. This linguistic clarity is integral for diverse analyses within the data lake. Step 7: Columns Need Intervention for Predictions (.py). A meticulous assessment of data types revealed nuanced characteristics, necessitating thoughtful consideration. Identification of non-numeric columns, requiring specialized intervention for predictive modeling, set the stage for subsequent machine learning endeavors within our data framework. Step 8: Count the Different Categories Issue_Area (.py). An enumeration of urban issues and associated areas unfolded, providing a comprehensive understanding of the dataset's categorical composition. Step 9: Check the Data (.py). An examination of the dataset unfolded, encompassing essential checks for integrity, completeness, and overall structure. This step, executed within the data lake environment, ensured that subsequent analyses were founded upon a robust and reliable dataset. Step 10: Converting Non-Numeric Columns (.py). In preparation for machine learning endeavors, the researchers encoded non-numeric columns. This step involved the precise conversion of categorical variables into a format suitable for predictive modeling, fostering an optimal representation of features within our data lake infrastructure. Step 11: Train RandomForest and Save the Model (.py). In the pursuit of predictive modeling, we employed AI techniques, specifically machine learning, the RandomForest Classifier, as the vehicle for understanding complex patterns within the data. Trained with precision, this model serves as an analytical instrument, capable of discerning intricate relationships among various features.