Data Science Interview | CRISP-ML (Q) | Part 1 | Network Analytics-Hyperparameter

Network analytics helps understand interconnected entities, using vertices and edges, and plays a crucial role in GIS or geospatial analytics, like Google Maps.
The most critical library for network analytics in Python is Network X, which allows for manipulation and analysis of complex networks, including graphs and multi-graphs.
Network analytics aids in weighting nodes based on their connections and is heavily used in marketing analytics. It is applied in domains like telecom, transportation, water systems, social networks, and more.
One historical application was during the US 2012 elections campaign, where geospatial analytics accurately predicted Barack Obama’s win. Another application is “community detection” used in sectors like telecommunications and banking to identify defaulters or fraudsters.
Network analytics is computationally demanding, and technologies like the Spark framework with its GraphX feature are tailored for handling this type of data efficiently.

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