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Articles by Vladimir Zhyvov
4-Dimensional Data Visualization: Time in Bubble Charts
Bubble Charts elegantly compress large amounts of information into a single visualization, with bubble size adding a third dimension. However, comparing “before” and “after” states is often crucial. To address this, we propose adding a transition between these states, creating an intuitive user experience. Since we couldn’t find a ready-made solution, we developed our own. The challenge turned out to be fascinating and required refreshing some mathematical concepts.
4-Dimensional Data Visualization: Time in Bubble Charts
9 hours ago Dark Clouds Gather Over Tesla as Sales Plummet Worldwide Looking into this. Electric Slide While Tesla billionaire Elon Musk and his gaggle of tech lackeys gnaw at the wires of the US federal government, his … 15 hours ago I write about AI for a living — here's my 7 favorite free AI tools to try now Within the last few weeks, several tech giants have released free versions of powerful chatbots.
Simplicity Over Black Boxes
Turning complex ML models into simple, interpretable rules with Human Knowledge Models for actionable insights and easy inference Modern machine learning has delivered incredible breakthroughs in lots of fields. These successes come from advanced models that can uncover intricate patterns in massive datasets. But there’s one area where this approach falls short: easy interpretation.
Simplicity Over Black Boxes
Cover, image by Author Modern machine learning has delivered incredible breakthroughs in lots of fields. These successes come from advanced models that can uncover intricate patterns in massive datasets. But there’s one area where this approach falls short: easy interpretation. Most ML models, often referred to as "black boxes," take vast amounts of data and contain thousands of coefficients and weights.
Building Effective Metrics to Describe Users
How can numerical user metrics, such as “3 visits in the past week,” be transformed into a personalized assessment of whether this behavior is typical or unusual for the user? In almost any digital product, analysts often face the challenge of building a digital customer profile — a set of parameters that describe the customer’s state and behavior in one way or another. What are the potential applications of these metrics?
Building Effective Metrics to Describe Users
In almost any digital product, analysts often face the challenge of building a digital customer profile – a set of parameters that describe the customer’s state and behavior in one way or another. What are the potential applications of these metrics? Gaining insights into user behavior Leveraging as features in ML models Developing business rules for personalized offers A simple example is e-commerce, with metrics like those listed in the table below.
From Default Python Line Chart to Journal-Quality Infographics
Transform boring default Matplotlib line charts into stunning, customized visualizations Everyone who has used Matplotlib knows how ugly the default charts look like. In this series of posts, I’ll share some tricks to make your visualizations stand out and reflect your individual style. We’ll start with a simple line chart, which is widely used. The main highlight will be adding a gradient fill below the plot — a task that’s not entirely straightforward.
From Default Python Line Chart to Journal-Quality Infographics
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