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The end track is very segmented, and the speed of product iteration and promotion is also very slow, which creates a large number of job opportunities. View details> The problem of many companies is that a group of engineers are immersed in studying these so-called big data models, but this group of people are out of touch with the business. The essence of the prediction model is whether the user feature engineering can effectively represent the user's intention. The user features that engineers can think of are nothing more than user browsing behavior, such as the number of openings, browsing time, and number of pages browsed; user purchasing behavior, such as adding to cart, collection, purchase, consumption frequency, and consumption interval time.



Can these features effectively predict a user's iran telephone code purchasing behavior? Obviously not. For example, how can this set of data modeling user operations accurately issue coupons by building a t model? Modeling these shallow user behavior data will produce purely speculative results. From the perspective of data logic, the more users collect and add to cart, the higher the probability of purchase. However, from the perspective of the user's real scenario, whether the user makes a purchase is affected by multiple factors such as price comparison on competing platforms and whether the user is in a good mood.



These characteristics are precisely what the operations students who are closest to the business and the user should mine. I believe that many operations students do not have the ability to mine and model data. The goal of this article is to simply introduce how operations students can use tools to implement big data mining and modeling work so as to further enhance their own value. Let's take a look at the practical construction of the model. The first step is to sort out the user feature engineering. In addition to the user's browsing behavior, consumption behavior, preference behavior characteristics, it is more important to mine the user's trend behavior, such as the trend of consumption interval period, the number of consumption categories, the amount of consumption, etc.