1 Supervised learning
Here, you are going to use the features you generated in Assignment #3 to predict the clients response to a promotion campaign. This is a typical classification problem in the retail industry, but the formulation of the problem is similar to industries such as fraud detection, marketing and manufacturing.
The clients responses are stored in the Retail Data Response.csv file from Kaggle. The responses are binary: 0 for clients who responded negatively to the promotional campaign and 1 for clients who responded positively to the campaign.
You will explore solving the classification problem with two different sets of features (i.e. annual and monthly) and three different algorithms as shown in the image below.
1.1 Import the monthly and annual data and join
In Assignment #3, you created five different feature families that capture annual and monthly aggregations. Here, you will model the retail problem with two approaches: using annual and monthly features. Therefore, you need to create the joined tables based on the following logic:
In both the annual and monthly features approach, you need to join at the end with table #4, the clients responses. This is simply a table that contains the binary response of the client to our marketing effort as described above and that is the output or label or target that makes this a supervised learning problem.