Often we want to predict discrete outcomes in our data. Can an email be designated as spam or not spam? Was a transaction fraudulent or valid?
One of the most common questions we have of our data is evaluating the value of something. How many items will we sell next month? How much does it cost to produce them? How much revenue will we make over the year?
One of the most common analyses we perform is to look for patterns in data. What market segments can we divide our customers into? How do we find clusters of individuals in a network of users?
Machine Learning can often be a black box. To gain actionable insights, its helpful to know how a variable influences a model. Here we outline 5 ways to assess feature importance to affecting the probability of an outcome.
How to discover your product's version of Facebook's "7 friends in 10 days" and learn the thresholds in user activity you need to activate a user throughout their customer journey.
When importing data into your data warehouse, you will almost certainly encounter data quality errors at many steps of the ETL pipeline. How do you catch these errors proactively, and ensure data quality in your data warehouse?