1) Data Introduction • The Direct Marketing Campaigns (Bank Marketing) Dataset is a dataset built to predict time deposits (deposit) based on customer characteristics and campaign history in Portuguese banks' phone-based direct marketing campaigns.

2) Data Utilization (1) Direct Marketing Campaigns (Bank Marketing) Dataset has characteristics that: • Consisting of 41,188 rows, individual case data for calls made to customers during each row marketing campaign. • This dataset contains 21 columns (characteristics) that provide detailed information about each phone and attributes related to customers and campaigns. (2) Direct Marketing Campaigns (Bank Marketing) Dataset can be used to: • Marketing Campaign Performance Forecasting and Customer Targeting: Using customer characteristics and historical campaign data, it can be used to predict customers who are likely to sign up for time deposits and to establish effective marketing targeting strategies. • Customer Behavior Analysis and Marketing Strategy Optimization: You can optimize marketing strategies by analyzing campaign response patterns, characteristics by customer group, and correlations with economic indicators, and use them for customer segmentation and customized product suggestions.

Abstract: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y). https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15100277%2F173368c0e01d219e35c1de9ba3f1bfd8%2F-------.JPG?generation=1684180716985041&alt=media" alt="">

Attribute Information:

Input variables: -> bank client data: 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') -> related with the last contact of the current campaign: 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.

->0ther attributes: 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')

->Social and economic context attributes 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')

Relevant Tasks;

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15100277%2F45abc3004ad8be49e4b43f9f92de47da%2FFE.png?generation=1684181245860633&alt=media" alt="">

Relevant Papers;

This dataset is related to direct marketing campaigns conducted by a Portuguese banking institution, with campaigns relying on phone calls. Often multiple contacts with the same client were necessary to determine whether they would subscribe ('yes') or not ('no') to a bank term deposit. The dataset includes four files:

1. bank-additional-full.csv: Contains all 41,188 examples with 20 input features, organized chronologically from May 2008 to November 2010, closely aligned with the data analyzed in [Moro et al., 2014].
2. bank-additional.csv: A subset of 4,119 examples (10% of the full data), randomly selected, with 20 input features.
3. bank-full.csv: The older version of the dataset, comprising all examples (41,188) with 17 input features, also organized chronologically.
4. bank.csv: A 10% random subset of the older version, containing 4,119 examples and 17 input features.

The smaller subsets are designed for testing computationally intensive machine learning algorithms (e.g., SVM). The primary classification objective is to predict whether a client will subscribe to a term deposit ('yes' or 'no'), based on the target variable y.

The data is related to direct marketing campaigns of a Portuguese banking institution.

The marketing campaigns were based on phone calls. Often, more than one contact with the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

There are two datasets:

1) bank-full.csv with all examples, ordered by date (May 2008 to November 2010). 2) bank.csv with 10% of the examples (4521), randomly selected from bank-full.csv

The smallest dataset is provided to test more computationally demanding machine learning algorithms (e.g. SVM).

Goal: The classification goal is to predict if the client will subscribe to a term deposit (variable y).

Number of Instances: 45211 for bank-full.csv (4521 for bank.csv)

Number of Attributes: 16 + output attribute.

Attribute information:

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

Input variables:

Customer 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:

contact: contact communication type (categorical: "unknown","telephone","cellular") day: last contact day of the month (numeric) month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") duration: last contact duration, in seconds (numeric)

Other attributes:

Campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 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) previous: number of contacts performed before this campaign and for this client (numeric) poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")

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

Missing Attribute Values: None

Dataset

This dataset was created by yassine sadiki

Contents

Bank Marketing

Abstract: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).

Data Set Information: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

Attribute Information:

Bank client data:

• Age (numeric)
• Job : type of job (categorical: 'admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown')
• Marital : marital status (categorical: 'divorced', 'married', 'single', 'unknown' ; note: 'divorced' means divorced or widowed)
• Education (categorical: 'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown')
• Default: has credit in default? (categorical: 'no', 'yes', 'unknown')
• Housing: has housing loan? (categorical: 'no', 'yes', 'unknown')
• Loan: has personal loan? (categorical: 'no', 'yes', 'unknown')

Related with the last contact of the current campaign:

• Contact: contact communication type (categorical: 'cellular','telephone')
• Month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
• Day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
• 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.

Other attributes:

• Campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
• 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)
• Previous: number of contacts performed before this campaign and for this client (numeric)
• Poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

Social and economic context attributes

• Emp.var.rate: employment variation rate - quarterly indicator (numeric)
• Cons.price.idx: consumer price index - monthly indicator (numeric)
• Cons.conf.idx: consumer confidence index - monthly indicator (numeric)
• Euribor3m: euribor 3 month rate - daily indicator (numeric)
• Nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target):

• y - has the client subscribed a term deposit? (binary: 'yes', 'no')

Analysis Steps:

• Atribute information Analysis.
• Machine Learning (Logistic Regression, KNN, SVM, Decision Tree,
  Random Forest, Naive Bayes)
• Deep Learning (ANN)

Source:

• Dataset from : http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

Citation Request: This dataset is public available for research. The details are described in [Moro et al., 2011]. Please include this citation if you plan to use this database:

[Moro et al., 2011] 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, Guimarães, Portugal, October, 2011. EUROSIS.

Available at: [pdf] http://hdl.handle.net/1822/14838 [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt

1. Title: Bank Marketing

2. Sources Created by: Paulo Cortez (Univ. Minho) and Sérgio Moro (ISCTE-IUL) @ 2012

3. Past Usage:

   The full dataset was described and analyzed in:

   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, Guimarães, Portugal, October, 2011. EUROSIS.

4. Relevant Information:

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

   There are two datasets: 1) bank-full.csv with all examples, ordered by date (from May 2008 to November 2010). 2) bank.csv with 10% of the examples (4521), randomly selected from bank-full.csv. The smallest dataset is provided to test more computationally demanding machine learning algorithms (e.g. SVM).

   The classification goal is to predict if the client will subscribe a term deposit (variable y).

5. Number of Instances: 45211 for bank-full.csv (4521 for bank.csv)

6. Number of Attributes: 16 + output attribute.

7. 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")

8. Missing Attribute Values: None

Context

The dataset contains information about marketing campaigns that were conducted via phone calls from a Portuguese banking institution to their clients. Purpose of these campaigns is to prompt their clients to subscribe for a specific financial product of the bank (term deposit). After each call was conducted, the client had to inform the institution about their intention of either subscribing to the product (indicating a successful campaign) or not (unsucessful campaign). The final output of this survey will be a binary result indicating if the client subscribed ('yes') to the product or not ('no').

The dataset has 41188 rows (instances of calls to clients) and 21 columns (variables) which are describing certain aspects of the call. Please note that there are cases where the same client was contacted multiple times - something that practically doesn't affect the analysis as each call will be considered independent from another even if the client is the same.

Content

The predictor variables (features) contained in the dataset can be divided into the following five sections:

1. Variables that describing attributes related directly to the client: a. age b. job: type of job (e.g. 'admin', 'technician', 'unemployed', etc) c. marital: marital status ('married', 'single', 'divorced', 'unknown') d. education: level of education ('basic.4y', 'high.school', 'basic.6y', 'basic.9y','professional.course', 'unknown','university.degree','illiterate') e. default: if the client has credit in default ('no', 'unknown', 'yes') f. housing: if the client has housing a loan ('no', 'unknown', 'yes') g. loan: if the client has a personal loan ? ('no', 'unknown', 'yes')

2. Variables related to the last contact of the current campaign: a. contact: type of communication ('telephone', 'cellular') b. month: month of last contact c. day_of_week: day of last contact d. duration: call duration (in seconds)

3. Other variables related to the campaign(s): a. campaign: number of contacts performed during this campaign and for this client b. pdays: number of days passed by after the client was last contacted from a previous campaign c. previous: number of contacts performed before this campaign and for this client d. poutcome: outcome of previous marketing campaign ('nonexistent', 'failure', 'success')

4. Socioeconomic variables: a. emp.var.rate: employement variation rate - quarterly indicator b. cons.price.idx: consumer price index - monthly indicator c. cons.conf.idx: consumer confidence index - monthly indicator d. euribor3m: euribor 3 month rate - daily indicator e. nr.employed: number of employees - quarterly indicator

Of course, the dataset also containts the variable subscribed which is the target variable, indicating if the client subscribed to the product ('yes') or not ('no').

Acknowledgements

1. Georgios Spyrou Github (Email: georgios.spyrou1@gmail.com)
2. UCI Machine learning repository

Bank dataset from 2014. The dataset contains 41188 records of direct marketing campaigns of a Portuguese banking institution corresponding to each client contacted. We chose 6 numeric attributes (age, duration, euribor of 3 month rate, no. of employees, consumer price index and number of contacts performed during the campaign) as features. We set the number of clusters to K = 10, and impose the target proportions of three groups U = [0.28, 0.61, 0.11] within each cluster.

Data Set Information:

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Attribute Information:

Input variables:

bank client data:

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')

related with the last contact of the current campaign:

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.

other attributes:

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')

social and economic context attributes

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')

Relevant Papers:

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]

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

This dataset consists of direct marketing campaigns by a Portuguese banking institution using phone calls. The campaigns aimed to sell subscriptions to a bank term deposit. The classification goal is to predict if the client will subscribe a term deposit (variable y).

Turkey Payment Card Transaction: Direct Marketing data was reported at 103,871.000 TRY th in 01 May 2020. This records an increase from the previous number of 85,111.000 TRY th for 24 Apr 2020. Turkey Payment Card Transaction: Direct Marketing data is updated weekly, averaging 68,361.000 TRY th from Mar 2014 (Median) to 01 May 2020, with 322 observations. The data reached an all-time high of 350,496.000 TRY th in 21 Jul 2017 and a record low of 4,622.000 TRY th in 04 Mar 2016. Turkey Payment Card Transaction: Direct Marketing data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

Lower prices produce higher demand… or do they? A bank’s direct marketing to holders of “free” checking accounts show that a large discount on 60% APR overdrafts reduces overdraft usage, especially when bundled with a discount on debit card or auto-debit transactions. In contrast, messages mentioning overdraft availability without mentioning price increase usage. Neither change persists long after messages stop. These results do not square easily with classical models of consumer choice and firm competition. Instead they support behavioral models where consumers both underestimate and are inattentive to overdraft costs, and firms respond by shrouding overdraft prices in equilibrium.

CONTEXT

1. The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y)

2. add 10 Questions for DATA EDA

data source : link

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This dataset is about the direct phone call marketing campaigns, which aim to promote term deposits among existing customers, by a Portuguese banking institution from May 2008 to November 2010. It is publicly available in the UCI Machine Learning Repository, which can be retrieved from http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#.

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