Python数据分析代写 | Customer Product Data Assignment 1


You are provided with more than 1 year of customer product data. The objective is to predict
what accounts a customer will acquire in the next month, 2016-05. The dataset is split into
training and tests sets. You will use the training data, ranging from 2015-01 to 2016-04, to build
your model. You will make your predictions on 2016-05, which can be found in the test set. This
dataset doesn’t contain any real bank data.
The dataset contains monthly information for the following:
1) Customer identifiers
2) Customer characteristics: demographic, geographic, etc.
3) Customer’s accounts
Submission Files
For every user in 2016-05, you must predict a ‘;’ delimited list of the products they added. Please
submit your prediction in the following format:

Submissions are evaluated according to the Mean Average Precision @ 7 (MAP@7):
where |U| is the number of rows (users in two time points), P(k) is the precision at cut-off k, n is
the number of predicted products, and m is the number of added products for the given user at
that time point. If m = 0, the precision is defined to be 0. Please note that the order of your
predictions for each customer matters. Your predictions must be arranged in descending order. In
other words, the products you predict a customer will add should be rank ordered in descending
order by their likelihood of being added. For a detailed explanation of how the MAP evaluation
works, please visit this link: