<ul><li>City leaders can identify city districts that require revitalization using a Current Vitality Index and a Long-Term Vitality Index.</li><li>The indexes are based on a carefully curated set of indicators and employ machine learning methods such as K-Nearest Neighbors imputation, Random Forest, and k-means clustering.</li><li>Current vitality is visualized through an interactive map, while Long-Term Vitality is tracked over 15 years with predictions made using Multilayer Perceptron or Linear Regression.</li><li>The results show promise in optimizing urban planning and improving citizens' quality of life, with potential for further improvement as more data becomes available.</li></ul>

District Vitality Index Using Machine Learning Methods for Urban Planners

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