要求： 根据以往的电话营销的数据，对用户进行分析，提取有价值的信息，并预测是否响应电话营销。 其他相关说明： 1.The zip file includes two datasets:
- bank-additional-full.csv with all examples, ordered by date (from May 2008 to November 2010).
- bank-additional.csv with 10% of the examples (4119), randomly selected from bank-additional-full.csv. The smallest dataset is provided to test more computationally demanding machine learning algorithms The binary classification goal is to predict if the client will subscribe a bank term deposit (variable y). 2.Number of Instances: 41188 for bank-additional-full.csv 3.Number of Attributes: 20 + output attribute. 4.Attribute information: Input variables:
1 - age (numeric) 2 - job : type of job (categorical: 'admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown') 3 - marital : marital status (categorical: 'divorced', 'married', 'single', 'unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown') 5 - default: has credit in default? (categorical: 'no', 'yes', 'unknown') 6 - housing: has housing loan? (categorical: 'no', 'yes', 'unknown') 7 - loan: has personal loan? (categorical: 'no', 'yes', 'unknown')
8 - contact: contact communication type (categorical: 'cellular', 'telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon', 'tue', 'wed', 'thu', 'fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure', 'nonexistent', 'success')
16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: "yes", "no") 5.Missing Attribute Values: There are several missing values in some categorical attributes, all coded with the "unknown" label. These missing values can be treated as a possible class label or using deletion or imputation techniques.