Eurostat-Based Data Pipeline for Comparative Analysis of Income Inequality and Poverty Risk in Europe

The first file, raw_data.zip, contains the original data (from Eurostat) before any modifications or preprocessing. This dataset is uncleaned and unprocessed, likely containing raw information from various sources. This could involve demographic, economic, or other types of data. At this stage, the data may have inconsistencies such as missing values, outliers, and potential errors, which can prevent any meaningful analysis or predictions. This raw dataset serves as the foundational data for further processing and is essentially the starting point of the entire workflow. Next, we move to the pre_processed_df.zip file. After the raw data is cleaned and organized, it is merged and processed to create a more structured version of the data. This pre-processed data typically involves the merging of various datasets based on local identification techniques, ensuring that different data sources are properly aligned and correlated. In this step, categorical variables are handled using Shannon entropy, a method that helps manage the uncertainty or randomness in these variables, making them more suitable for modeling. At this stage, the data is still in a form that is not yet ready for predictive modeling, but it is now cleaned and structured in a way that makes it more manageable for analysis. Finally, the read_for_prediction.zip file contains the dataset that has been fully prepared for machine learning or other predictive methods. This dataset has undergone further cleaning, scaling, and any necessary transformations, such as dealing with missing values, normalizing numerical data, and handling outliers. Scaling is particularly important to ensure that different features are comparable in terms of magnitude, especially when using algorithms that rely on distance calculations or gradient descent. After this extensive preparation, the data is ready to be input into predictive models. At this point, the dataset is optimal for use in any modeling techniques you plan to apply. This pipeline, from raw data to prediction-ready data, ensures that every step of the process—cleaning, structuring, and preparing—is thoroughly addressed, making the dataset ready for accurate and effective modeling. Disclaimer:This dataset includes data sourced from Eurostat (the statistical office of the European Union). The original data is publicly available and licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The data has been further processed and transformed solely for academic and research purposes. The content, views, interpretations, and any possible errors in this dataset are solely those of the author and do not reflect the official opinion of Eurostat or the European Union. Eurostat is not liable for any use that may be made of the information contained herein.