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Unlocking the Power of Better Data Science Workflows

Smart Data Collective

Phase 3: Data Visualization. With the data analyzed and stored in spreadsheets, it’s time to visualize the data so that it can be presented in an effective and persuasive manner. Phase 4: Knowledge Discovery. When these two elements are in harmony, there are fewer delays and less risk of data corruption.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Graphs boost knowledge discovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Use Case #4: Financial Risk Detection and Prediction The financial industry is made up of a network of markets and transactions.

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Business Intelligence System: Definition, Application & Practice

FineReport

It is a process of using knowledge discovery tools to mine previously unknown and potentially useful knowledge. It is an active method of automatic discovery. Data Visualization. Data visualization can reflect business operations intuitively. How BI system solve the problem? REPORT FILLING.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Chawla et al.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk. Ribeiro, M.

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