📞 Bank Marketing (Term Deposit Subscription) Dataset

Source : UCI Machine Learning Repository – Bank Marketing (#222)

A Portuguese retail bank’s phone-based marketing campaigns (May 2008 → Nov 2010).
The task is to predict whether a client will subscribe to a term deposit (target y).

1 · Background

• Each row records the outcome of the last phone call (plus client history).
• Multiple calls to the same client may appear across campaigns.
• The original authors showed that data-driven targeting boosts campaign ROI – see the reference paper below.

2 · Files in this Kaggle release

Need the enriched “bank-additional” version with 20 predictors? Grab it from the UCI link.

3 · Data Dictionary (17 predictors + target)

*⚠️ duration is only known after the call ends; include it only for benchmarking, not for live prediction.

4 · Quick Start in Python

import pandas as pd

df = pd.read_excel('/kaggle/input/bank-marketing/bank_marketing.xlsx')
print(df.shape)     # (45211, 17)
df.head()

Prefer pip? Fetch directly from ucimlrepo:
'''
!pip install ucimlrepo
from ucimlrepo import fetch_ucirepo
bm = fetch_ucirepo(id=222)
X, y = bm.data.features, bm.data.targets
'''

## 5 · Use-Cases & Ideas 

| 🛠️ ML Task       | Why it’s interesting                                              |
|--------------------------|----------------------------------------------------------------------------------------------------------------|
| Binary classification  | Classic imbalanced dataset – try **SMOTE**, cost-sensitive learning, threshold tuning             |
| Feature engineering   | Combine `pdays`, `campaign`, `previous` into a **contact-intensity score**                   |
| Model interpretability  | Use **SHAP** / **LIME** to explain “yes” predictions                              |
| Time-aware validation  | Data are date-ordered → split train/test chronologically to avoid leakage                   |

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## 6 · Credits & Citations 

> **Creators :** **Sérgio Moro, Paulo Rita, Paulo Cortez** 
> **Original paper :** 
> Moro S., Cortez P., Rita P. (2014). 
> *A data-driven approach to predict the success of bank telemarketing campaigns.* 
> *Decision Support Systems.* [[PDF]](https://www.semanticscholar.org/paper/cab86052882d126d43f72108c6cb41b295cc8a9e)

If you use this dataset, please cite:

Moro, S., Rita, P., & Cortez, P. (2014). Bank Marketing [Dataset].
UCI Machine Learning Repository. https://doi.org/10.24432/C5K306


---

## 7 · License 

This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)**. 
You are free to share & adapt, **provided you credit the original creators**.

Bank marketing campaigns dataset analysis # Opening a Term Deposit

It is a dataset that describing Portugal bank marketing campaigns results. Conducted campaigns were based mostly on direct phone calls, offering bank client to place a term deposit. If after all marking afforts client had agreed to place deposit - target variable marked 'yes', otherwise 'no'

Sourse of the data https://archive.ics.uci.edu/ml/datasets/bank+marketing

Citation Request:

This dataset is public available for research. The details are described in 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

1. Title: Bank Marketing (with social/economic context)

2. Sources Created by: Sérgio Moro (ISCTE-IUL), Paulo Cortez (Univ. Minho) and Paulo Rita (ISCTE-IUL) @ 2014

3. Past Usage:

   The full dataset (bank-additional-full.csv) was described and analyzed in:

   S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems (2014), doi:10.1016/j.dss.2014.03.001.

4. Relevant Information:

   This dataset is based on "Bank Marketing" UCI dataset (please check the description at: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing). The data is enriched by the addition of five new social and economic features/attributes (national wide indicators from a ~10M population country), published by the Banco de Portugal and publicly available at: https://www.bportugal.pt/estatisticasweb. This dataset is almost identical to the one used in Moro et al., 2014. Using the rminer package and R tool (http://cran.r-project.org/web/packages/rminer/), we found that the addition of the five new social and economic attributes (made available here) lead to substantial improvement in the prediction of a success, even when the duration of the call is not included. Note: the file can be read in R using: d=read.table("bank-additional-full.csv",header=TRUE,sep=";")

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

1. Number of Instances: 41188 for bank-additional-full.csv

2. Number of Attributes: 20 + output attribute.

3. Attribute information:

   For more information, read [Moro et al., 2014].

   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)

   1515 - 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 - h...

About Dataset

There has been a revenue decline in the Portuguese Bank and they would like to know what actions to take. After investigation, they found that the root cause was that their customers are not investing enough for long term deposits. So the bank would like to identify existing customers that have a higher chance to subscribe for a long term deposit and focus marketing efforts on such customers.

Data Set Information

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 to the same client was required, in order to access if the product (bank term deposit) would be subscribed ('yes') or not ('no') subscribed.

There are two datasets: train.csv with all examples (32950) and 21 inputs including the target feature, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]

test.csv which is the test data that consists of 8238 observations and 20 features without the target feature

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

The dataset contains train and test data. Features of train data are listed below. And the test data have already been preprocessed.

Features

Target variable (desired output):

Brazilian banking institution, carried out through telephone calls. The main objective is to predict whether a customer will subscribe to a term deposit, identified by the TARGET variable. This dataset is notable for having no missing values ​​in either the categorical or numeric columns, which facilitates the analysis and modeling process. Data Description The data covers a variety of demographic and customer contact-related attributes. Demographic attributes include: Age: Age of the client. Occupation: Type of work the client does (e.g., "admin.", "worker", "entrepreneur", etc.). Marital status: The customer's marital status (e.g., "single", "married", "divorced"). Education: Level of education achieved by the client. Balance: Average annual balance in euros. Home Loan: Indicates whether the customer has a home loan. Personal Loan: Indicates whether the customer has a personal loan. Contact-related attributes include: Type of contact: Means of communication used (e.g. "mobile phone", "landline"). Day of contact: Day of the month on which the last contact was made. Month of contact: Month of the year in which the last contact was made. Duration: Duration of the last call in seconds. Additionally, the dataset includes information about previous campaigns, such as the number of contacts made, the number of days since the last contact, and the outcome of previous campaigns. Pre-processing Steps To prepare data for predictive modeling, several preprocessing steps are performed: Encoding: Label encoding is applied to all categorical columns, converting them into numeric values ​​suitable for machine learning models. Normalization: Numeric columns can be normalized to ensure that they are all on the same scale, which is especially important for algorithms that are sensitive to data scale. Data Splitting: The dataset is split into training and testing sets to evaluate the model performance. Data Files The dataset is provided in two distinct CSV files: Original Data: A CSV file containing the original data, allowing analysts to perform preprocessing and feature engineering as needed. Training-Ready Data: A CSV file containing the data already pre-processed and with the categorical columns encoded, ready to be used in training machine learning models. Applications This dataset is widely used for classification tasks to predict the probability of a customer subscribing to a term deposit. Researchers and data scientists use this data to develop models that help financial institutions target their marketing campaigns more effectively by identifying customers who are most likely to respond positively.

Results table of sales growth rate and customer satisfaction growth rate of each model algorithm.

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Regional Outlook

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The Asia Pacific region is projected to be the fastest-growing market, with an impressive CAGR of 17.2% from 2025 to 2033. This rapid growth is driven by the ongoing digital banking revolution across countries such as China, India, Singapore, and Australia. The region is witnessing massive investments in cloud infrastructure and AI-powered analytics platforms, as banks aim to capture a rapidly expanding, digitally savvy customer base. Government initiatives promoting financial inclusion and the proliferation of fintech partnerships are further catalyzing demand for marketing mix modeling solutions. As competition intensifies, banks in Asia Pacific are increasingly seeking advanced tools to differentiate their offerings, personalize customer engagement, and optimize product launches, all of which are critical for sustained growth in this dynamic market.

Emerging economies in Latin America and the Middle East & Africa are also showing promising adoption trends, albeit at a more gradual pace. While these regions currently represent a smaller share of the global market, local banks are beginning to recognize the value of data-driven marketing optimization in the face of evolving consumer preferences and regulatory changes. However, challenges such as limited digital infrastructure, data privacy concerns, and a shortage of skilled analytics professionals can hinder widespread adoption. Nevertheless, targeted government policies, international collaborations, and the entry of global technology vendors are gradually addressing these barriers, paving the way for future growth and the potential for leapfrogging traditional marketing approaches.

Report Scope

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Another key driver propelling the market is the increasing regulatory scrutiny and compliance requirements faced by financial institutions globally. Regulatory bodies are mandating more granular, accurate, and timely reporting on transactions, anti-money laundering (AML) activities, and risk exposures. Apache Hudi’s ability to efficiently manage change data capture (CDC), maintain audit trails, and provide point-in-time querying capabilities makes it an ideal solution for banks aiming to meet these stringent requirements. The flexibility to deploy on-premises or in the cloud, combined with seamless integration into existing big data ecosystems, positions Hudi as a strategic asset for banks striving to achieve regulatory compliance while maintaining operational agility.

Additionally, the growing focus on real-time analytics and AI-driven insights is reshaping the banking landscape. Financial institutions are increasingly investing in advanced analytics to enhance customer experiences, optimize operations, and gain competitive advantages. Apache Hudi’s support for real-time data updates and low-latency querying enables banks to deliver actionable insights at the moment of need, whether for personalized marketing, risk assessment, or transaction monitoring. The synergy between Hudi-powered lakehouse tables and emerging AI/ML applications is expected to drive further innovation and unlock new revenue streams for banks, underpinning the robust growth trajectory of this market.

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According to our latest research, the Global Experimentation Platforms for Banking market size was valued at $2.1 billion in 2024 and is projected to reach $7.6 billion by 2033, expanding at a robust CAGR of 15.2% during the forecast period of 2025–2033. One of the major factors propelling the growth of this market is the rapid digital transformation initiatives within the banking sector, which demand agile, data-driven decision-making and continuous product innovation. As banks strive to enhance customer experience, optimize risk management, and comply with evolving regulations, the deployment of advanced experimentation platforms is becoming indispensable, enabling institutions to test, measure, and iterate new solutions with speed and precision.

Regional Outlook

North America continues to dominate the Experimentation Platforms for Banking market, accounting for the largest market share, estimated at over 38% of the global revenue in 2024. This leadership is attributed to the region's mature banking ecosystem, high digital adoption rates, and favorable regulatory frameworks that encourage innovation and technology integration. The presence of major technology vendors and a strong culture of early adoption among financial institutions further solidifies North America’s position. The region’s banks are leveraging experimentation platforms extensively for product development, customer experience optimization, and compliance testing to maintain their competitive edge. Furthermore, the robust investment in fintech and the proliferation of digital banking channels have accelerated the integration of these platforms into mainstream banking operations.

Asia Pacific is emerging as the fastest-growing region in the Experimentation Platforms for Banking market, projected to register a CAGR of 19.4% from 2025 to 2033. The rapid expansion is fueled by significant investments in banking infrastructure modernization, a burgeoning fintech ecosystem, and increasing regulatory support for digital transformation. Countries such as China, India, and Singapore are at the forefront, with banks adopting experimentation platforms to enhance customer engagement, streamline operations, and comply with dynamic regulatory requirements. The region’s large unbanked and underbanked populations present vast opportunities for banks to experiment with new products and digital channels, driving further market growth. Additionally, government-led initiatives promoting digital financial inclusion are acting as catalysts for adoption.

In emerging economies across Latin America, the Middle East, and Africa, the adoption of experimentation platforms in banking is gaining momentum but faces unique challenges. While there is a growing recognition of the value these platforms bring in terms of product innovation and risk mitigation, factors such as limited IT infrastructure, skills shortages, and regulatory complexities can hinder widespread adoption. Local banks are gradually investing in cloud-based experimentation solutions to overcome infrastructure constraints, but the pace of adoption remains uneven. Nevertheless, as these regions continue to attract foreign investment and regulatory reforms promote digital banking, the long-term outlook remains positive, with localized demand for experimentation platforms expected to rise steadily.

Report Scope

Data Description

This dataset contains information about clients from a bank's marketing campaign, focusing on various demographic and contact-related attributes. The objective is to predict whether a client will subscribe to a term deposit (TARGET).

The dataset Have no missing values in categorical columns and Numeric columns.

Preprocessing Steps

Encoding:

• Label Encoding is applied to all categorical columns, converting them into numeric values suitable for machine learning models.

Data Files:

• Original Data CSV: A CSV file containing Original Data you can do preprocessing and Feature Engineering.
• Ready to Train Model CSV: A CSV file containing data preprocessed and Label Encoded Ready to train MODEL.

New Features

Feature Information

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Regional Outlook

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Report Scope

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Emerging economies in Latin America, the Middle East, and Africa are gradually embracing the 100W PD Power Bank market, albeit facing unique challenges. While localized demand is growing due to increased smartphone and laptop usage, adoption is hampered by issues such as limited access to high-speed internet, lower purchasing power, and inconsistent regulatory frameworks. Nonetheless, policy reforms aimed at improving digital infrastructure and the entry of affordable product lines are beginning to close the gap. In these regions, partnerships between global brands and local distributors are crucial for overcoming logistical hurdles and educating consumers about the benefits of high-wattage power banks, paving the way for steady market growth.

Report Scope

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Country Wise Outlook

Competitive Outlook

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Report Scope

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Philippines CB: OI: NI: TI: Marketing to Market Unrealized Gain/Loss data was reported at -0.213 PHP bn in Dec 2024. This records an increase from the previous number of -0.610 PHP bn for Sep 2024. Philippines CB: OI: NI: TI: Marketing to Market Unrealized Gain/Loss data is updated quarterly, averaging 0.031 PHP bn from Mar 2008 (Median) to Dec 2024, with 68 observations. The data reached an all-time high of 2.081 PHP bn in Sep 2021 and a record low of -2.011 PHP bn in Dec 2010. Philippines CB: OI: NI: TI: Marketing to Market Unrealized Gain/Loss data remains active status in CEIC and is reported by Bangko Sentral ng Pilipinas. The data is categorized under Global Database’s Philippines – Table PH.KB071: Income Statement: Commercial Banks.