dataset_039

葡萄牙某银行机构针对某种金融产品的电话营销活动记录。通常，需要多次联系同一客户，以便评估产品（银行定期存款）是否会被认购。该数据包含1,6394条营销活动相关信息，相关变量的解释阅读README.txt。假设银行现在希望根据相关变量来分析并预测客户是否办理定期存款（目标变量）。根据给定数据集，应用合适的分析模型，例如机器学习/统计模型，来预测营销结果。葡萄牙某银行机构针对某种金融产品的电话营销活动记录。通常，需要多次联系同一客户，以便评估产品（银行定期存款）是否会被认购。该数据包含1,6394条营销活动相关信息，相关变量的解释阅读README.txt。假设银行现在希望根据相关变量来分析并预测客户是否办理定期存款（目标变量）。根据给定数据集，应用合适的分析模型，例如机器学习/统计模型，来预测营销结果。

Background information:

The data is related with direct marketing campaigns of a banking institution on several products.
The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required,
in order to assess if the product (bank term deposit) would be (or not) subscribed.

Each data file is about a specific product.

Attribute information:

For more information, read [Moro et al., 2011].

Input variables:
# bank client data:
1 - age (numeric)
2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student",
"blue-collar","self-employed","retired","technician","services")
3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed)
4 - education (categorical: "unknown","secondary","primary","tertiary")
5 - default: has credit in default? (binary: "yes","no")
6 - balance: average yearly balance, in euros (numeric)
7 - housing: has housing loan? (binary: "yes","no")
8 - loan: has personal loan? (binary: "yes","no")
# related with the last contact of the current campaign:
9 - contact: contact communication type (categorical: "unknown","telephone","cellular")
10 - day: last contact day of the month (numeric)
11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")
12 - duration: last contact duration, in seconds (numeric)
# other attributes:
13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)
15 - previous: number of contacts performed before this campaign and for this client (numeric)
16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")

Output variable (desired target):
17 - y - has the client subscribed a term deposit? (binary: "yes","no")

Source:

Datasets adopted from
S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]

搜集汇总

背景与挑战

背景概述

该数据集记录了葡萄牙某银行机构的电话营销活动，包含16,394条数据，涉及客户基本信息、联系历史等变量，旨在预测客户是否会认购银行定期存款。数据集适用于机器学习或统计模型进行二元分类分析。