@Matrixzhu
2023-02-27T23:21:56.000000Z
字数 1655
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Dataset
The topic is based on the forecast of bank product subscription, and we want you to predict whether customers will buy bank products. In the process of communicating with customers, we record the number of times we contacted customers, the duration of the last contact, and the time interval of the last contact. At the same time, we saved the basic information of customers in the banking system, including: age, occupation, marriage , Whether there was a breach of contract before, whether there was a mortgage, etc. In addition, we also counted the current market situation: employment, consumption information, interbank dismantling rate, etc.
To DO: Predict whether the user will purchase the product
field | illustrate |
---|---|
age | age |
job | Occupation: admin, unknown, unemployed, management… |
marital | Marriage: married, divorced, single |
education | Education level: high school, professional course, university... |
default | Credit Card Default: yes or no |
housing | Whether there is a mortgage: yes or no |
loan | Loan record: yes, no, unknown |
contact | Contact: unknown, telephone, cellular |
month | Month of last contact: jan, feb, mar, … |
day_of_week | Day of the week of last contact: mon, tue, wed, thu, fri |
duration | Length of last contact (seconds) |
campaign | Number of times the customer was contacted during the event |
pdays | The number of days since the last contact with the customer |
previous | The number of days since the last contact with the customer |
poutcome | Results of previous campaigns: unknown, other, failure, success |
emp_var_rate | Employment Change Rate (Quarterly Indicator) |
cons_price_index | Employment Change Rate (Quarterly Indicator) |
cons_conf_index | Consumer Confidence Index (Monthly Indicator) |
lending_rate3m | Interbank Offered Rate 3-Month Rate (Daily Indicator) |
nr_employed | Number of employees (quarterly indicator) |
subscribe | Whether the customer made a purchase: yes or no |