The Ecommerce_Customer schema has four variables, Time On App, Time On Website, Length Of Membership, and Yearly Amount Spent. I want to see the distribution of these data. Incorta let me preview the data and show max and min data of each using its Preview function. Here are the steps I used to create a histogram in Incorta. First, I used the bin function in Incorta divided into different levels. Here is the documentation for the bin function. https://docs.incorta.com/4.5/r-bin Here is the result of the bin function. I divided the average of session length into 6 levels. If the length is less than 30, it will be labeled as 'SLV1', and if the length is greater than 30 but less than 32, it will be labeled as 'SLV2', by basically, according to the min and max value. I see the minimum value is close to 30, and the maximum value close to 38. I decided to use 2 minutes as the interval and created the formula using the bin function. I'm grouping...
In the previous blog, I was Using Time Series Analysis By Prophet. https://suziepyspark.blogspot.com/2021/03/using-time-series-analysis-by-prophet.html In this blog, I will use Pickle to Save and use the model in Incorta. In machine learning, we often need to store the trained model so that we can directly read the model when making a decision without retraining the model, which greatly saves time. The pickle module provided by Python solves this problem well. It can serialize objects and save them to disk and read them out when needed. Any object can be serialized. Below is how to use the model: Below is how to save the model: Reference: "Pickle Serialization Study Notes - Programmer Sought". Programmersought.Com, 2021, https://www.programmersought.com/article/15805994125/. "Save Model For Python · Issue #725 · Facebook/Prophet". Github, 2021, https://github.com/facebook/prophet/issues/725.
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