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fanninni
fanninni

fanninni

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From Solve Business Problems with Data Science by James Chen

When transforming data into a dataframe, sometimes we need to rely on experiences and domain expertise to engineer new variables from a number of currently available variables. For example, in transactional data, customers may have purchased different quantities and different…

From Solve Business Problems with Data Science by James Chen

Since we have extracted data from three distinctive sources above, it is highly likely that the formats are very different, and hence we need to compile the data to a per customer level dataframe. That is, each observation,…

From Solve Business Problems with Data Science by James Chen

… the observations with missing values may be an easy solution, we risk losing valuable information. As a result, it is highly recommended to use more feasible approaches, such as using regression to predict missing values.

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Top Takeaways from Andy Grove’s High Output Management

Ian Tien