Practical data science: correcting 10 years of messy CRM data

A session at [PyIE April Talk] Practical data science

  • jonathan sedar

A client in the energy sector wanted to create predictive behavioural models of business customers at the company level, but the CRM data was messy, often containing several sub-accounts for each business, without any grouping identifiers, and so aggregation was impossible. In this talk I describe a short project where we used text mining, a handful of unsupervised learning techniques, and pragmatic use of human skill under time & budgetary constraints, to identify the true company level structures in the CRM data.

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jonathan sedar

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Date Wed 9th April 2014

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