The final method for diversifying our results is Maximal Marginal Relevance (MMR). MMR tries to minimize redundancy and maximize the diversity of results in text summarization tasks. Fortunately, a keyword extraction algorithm called EmbedRank has implemented a version of MMR that allows us to use it for diversifying our keywords/keyphrases.,For Ben, a blend of cigarette smoke and hair spray is the smell of business. It’s because his mom used to run a hair salon in their kitchen, and that’s how Ben was exposed to entrepreneurship as a kid.,To discuss and practise with the plotnine package, we are using the Olympics data 1896–2016. It’s a cleaned dataset but has several columns with different scale measurement, so theoretically we can create a lot of data visualization and insights from the data.,Plotnine is the implementation of the R package ggplot2 in Python. It replicates the syntax of R package ggplot2 and visualizes the data with the concept of the grammar of graphics. It creates a visualization based on the abstraction of layers. When we are making a bar plot, we will build the background layer, then the main layer of the bar plot, the layer that contains title and subtitle, and etc. It is like when we are working with Adobe Photoshop. The plotnine package is built on top of Matplotlib and interacts well with Pandas. If you are familiar with the ggplot2, it can be your choice to hand-on with plotnine. The template of ggplot2 is as follows.,Ben’s first hire and (supposedly technical) co-founder, Dan Kurzius, lied about knowing how to code on his interview. After he got the job, he put together the prototype for MailChimp using “HTML for Dummies” books. Dan tried so hard he actually produced (or stole) clean, functional code which amazed Ben. Only 10 years later did the co-founder tell Ben about his improvisation.,ggplot(data = )
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