A three stage unsupervised learning pipeline for data analysis.
<p>The first stage involves data preprocessing, where the dataset undergoes standardization, normalization, and cleaning procedures to address missing values and noise. The second stage consists of sequential dimensionality reduction by PCA to identify principal components to capture global da...
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2025
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| Summary: | <p>The first stage involves data preprocessing, where the dataset undergoes standardization, normalization, and cleaning procedures to address missing values and noise. The second stage consists of sequential dimensionality reduction by PCA to identify principal components to capture global data structure, followed by t-SNE which preserves local relationships in the reduced dimensional space. The final stage applies a clustering algorithm (DBSCAN) to identify distinct groups within the processed data. Countries in each cluster are mapped and the mean trajectories towards ideal scores are calculated. Arrows between the stages indicate the sequential flow of data through the pipeline, with the output of each stage serving as the input for the subsequent stage.</p> |
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