BigData News Thursday, March 8 Machine learning, Machine learning tools, Social media & more…
BigData News TLDR / Table of Contents
- Benchmarking 20 Machine Learning Models Accuracy and Speed
- As Machine Learning tools become mainstream, and ever-growing choice of these is available to data scientists and analysts, the need to assess those best suite…
- Machine Learning, Machine Learning tools, Machine Learning models, additional Machine Learning, multinomial datasets
- The Customer Voice: Bringing Value to Online Data
- To effectively use the massive amount of consumer data available via social media, companies need to be able to filter, capture and analyze actionable data.
- social media, data, data collection, customer feedback, social media platforms
- Electric Vehicles for Smarter Cities: The Future of Energy and Mobility
- This report examines the major trends affecting the transformation of energy and mobility systems, with a special focus on cities. Topics addressed include: electrification, decentralization and digitalization of the energy system, along with the shift towards electric, shared and autonomous mobility.
- autonomous mobility, mobility systems, best experience, major trends, special focus
- As Machine Learning tools become mainstream, and ever-growing choice of these is available to data scientists and analysts, the need to assess those best suited becomes challenging.
- Inthis study, 20 Machine Learning models were benchmarked for their accuracy and speed performance on a multi-core hardware, when applied to 2 multinomial datasets differing broadly in size and complexity.
- Suggestions for additional Machine Learning, pertinent datasets and which recommender to benchmark are welcome.
- Second, they should ensure that their data collection strategies are executed in a manner that makes key insights about their target customer accessible to any stakeholder: their pain points, brand expectations, tastes, and what they value in a product or service.
- While a wealth of consumer profiling data does not come without its merits, its an organized team, well-implemented technology and the capacity to put consumer insights at the heart of a social listening strategy that will ensure that datas value.
- Turning social intelligence data into actionable customer insights is an ongoing process because customer experience changes in real time.
- While end-of-project objectives should inform the data collection and filtration process, customer feedback must inform strategies as they develop.
- With a customer-oriented team, an effective strategy and well-integrated technologies, data-driven companies can ensure that their social data collection is as relevant and productive as possible.
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