The customer churn dataset is a collection of customer data that focuses on predicting customer churn, which refers to the tendency of customers to stop using a company's products or services. The dataset contains various features that describe each customer, such as their credit score, country, gender, age, tenure, balance, number of products, credit card status, active membership, estimated salary, and churn status. The churn status indicates whether a customer has churned or not. The dataset is used to analyze and understand factors that contribute to customer churn and to build predictive models to identify customers at risk of churning. The goal is to develop strategies and interventions to reduce churn and improve customer retention

This Synthetic Customer Churn Prediction Dataset has been designed as an educational resource for exploring data science, machine learning, and predictive modelling techniques in a customer retention context. The dataset simulates key attributes relevant to customer churn analysis, such as service usage, contract details, and customer demographics. It allows users to practice data manipulation, visualization, and the development of models to predict churn behaviour in industries like telecommunications, subscription services, or utilities.

Dataset Features:

• Customer_Id: Unique identifier for each customer (not included in this dataset for privacy).
• Gender: Gender of the customer (e.g., "Male," "Female").
• Partner: Whether the customer has a partner (e.g., "Yes," "No").
• Dependents: Whether the customer has dependents (e.g., "Yes," "No").
• Tenure (Months): The number of months the customer has been with the company.
• PhoneService: Whether the customer has a phone service (e.g., "Yes," "No").
• MultipleLines: Whether the customer has multiple phone lines (e.g., "Yes," "No phone service").
• InternetService: Type of internet service (e.g., "DSL," "Fiber optic," "No").
• OnlineSecurity: Whether the customer has online security services (e.g., "Yes," "No," "No internet service").
• OnlineBackup: Whether the customer has online backup services (e.g., "Yes," "No," "No internet service").
• DeviceProtection: Whether the customer has device protection services (e.g., "Yes," "No," "No internet service").
• TechSupport: Whether the customer has tech support services (e.g., "Yes," "No," "No internet service").
• StreamingTV: Whether the customer has streaming TV services (e.g., "Yes," "No," "No internet service").
• StreamingMovies: Whether the customer has streaming movies services (e.g., "Yes," "No," "No internet service").
• Contract: Type of contract the customer has (e.g., "Month-to-month," "One year," "Two year").
• PaperlessBilling: Whether the customer uses paperless billing (e.g., "Yes," "No").
• PaymentMethod: The payment method used by the customer (e.g., "Electronic check," "Credit card," "Bank transfer").
• MonthlyCharges: Monthly charges billed to the customer.
• TotalCharges: Total charges incurred by the customer over their tenure.
• Churn: Whether the customer has churned (e.g., "Yes," "No").

Distribution:

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Usage:

This dataset is useful for a variety of applications, including:

• Customer Behavior Analysis: To understand factors influencing customer retention and churn.
• Educational Training: To practice data cleaning, feature engineering, and visualization techniques in customer analytics.
• Predictive Modeling: To build machine learning models for predicting customer churn based on service usage patterns and demographic information.

Coverage:

This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising real patient privacy.

License:

CCO (Public Domain)

Who can use it:

• Data scientists and enthusiasts: For developing customer analytics skills and predictive modelling expertise.
• Business analysts: To understand customer churn drivers and improve retention strategies.
• Educators and students: For teaching and learning applications in data science and machine learning.

This dataset had adapted from 'Credit Card Churn Prediction: https://www.kaggle.com/datasets/anwarsan/credit-card-bank-churn ' for visualization in our university project. We have modified customer information, spending behavior, and also added revenue targets.

Scenario 🕶️ In 2019, the marketing team launched a campaign to attract millennial customers (born 1980-1996) with the goal of increasing revenue and enhancing the brand's appeal to a younger audience.
As the BI team, your task is to create a dashboard for users. 1. The Vice President of Sales wants to view the performance of the credit business. 2. The marketing team is interested in understanding customer segments and customer spending to measure Customer Lifetime Value (CLV) and Marketing Cost per Acquired Customer (MCAC).

⚠️Note: This is just a suggestion to guide the creation of the dashboard

Example in Tableau

Executive summary https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F508a2d2d89dabdfd368743f86c2a71e1%2Fexecutive%20overview.JPG?generation=1696110593484137&alt=media" alt=""> Customer behavior https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F1e4a1f62a25eab3c6707d002243894c7%2Fcustomer_behaviour.JPG?generation=1696110689732332&alt=media" alt="">

The data will be used to predict whether a customer of the bank will churn. If a customer churns, it means they left the bank and took their business elsewhere. If you can predict which customers are likely to churn, you can take measures to retain them before they do. These measures could be promotions, discounts, or other incentives to boost customer satisfaction and, therefore, retention.

The dataset contains:

10,000 rows – each row is a unique customer of the bank

14 columns:

RowNumber: Row numbers from 1 to 10,000

CustomerId: Customer’s unique ID assigned by bank

Surname: Customer’s last name

CreditScore: Customer’s credit score. This number can range from 300 to 850.

Geography: Customer’s country of residence

Gender: Categorical indicator

Age: Customer’s age (years)

Tenure: Number of years customer has been with bank

Balance: Customer’s bank balance (Euros)

NumOfProducts: Number of products the customer has with the bank

HasCrCard: Indicates whether the customer has a credit card with the bank

IsActiveMember: Indicates whether the customer is considered active

EstimatedSalary: Customer’s estimated annual salary (Euros)

Exited: Indicates whether the customer churned (left the bank)

Analysis of ‘Churn for Bank Customers’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mathchi/churn-for-bank-customers on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

• RowNumber—corresponds to the record (row) number and has no effect on the output.
• CustomerId—contains random values and has no effect on customer leaving the bank.
• Surname—the surname of a customer has no impact on their decision to leave the bank.
• CreditScore—can have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.
• Geography—a customer’s location can affect their decision to leave the bank.
• Gender—it’s interesting to explore whether gender plays a role in a customer leaving the bank.
• Age—this is certainly relevant, since older customers are less likely to leave their bank than younger ones.
• Tenure—refers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.
  • Balance—also a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.
  • NumOfProducts—refers to the number of products that a customer has purchased through the bank.
  • HasCrCard—denotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank.
  • IsActiveMember—active customers are less likely to leave the bank.
  • EstimatedSalary—as with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.
  • Exited—whether or not the customer left the bank.

Acknowledgements

As we know, it is much more expensive to sign in a new client than keeping an existing one.

It is advantageous for banks to know what leads a client towards the decision to leave the company.

Churn prevention allows companies to develop loyalty programs and retention campaigns to keep as many customers as possible.

--- Original source retains full ownership of the source dataset ---

This dataset provides simulated retail transaction data, offering valuable insights into customer purchasing behaviour and store operations. It is designed to facilitate market basket analysis, customer segmentation, and a variety of other retail analytics tasks. Each row captures detailed transaction information, including a unique identifier, the date and time of purchase, customer details, a list of purchased products, total items, total cost, payment method, and location details such as city and store type. Furthermore, it includes indicators for discounts and promotions applied, along with a customer category based on background or age group, and the season of purchase. This dataset is entirely synthetic, generated using the Python Faker library, making it a safe and versatile resource for researchers, data scientists, and analysts to develop and test algorithms, models, and analytical tools without using real customer data.

Columns

• Transaction_ID: A unique 10-digit identifier for each individual transaction, ensuring each purchase can be uniquely identified.
• Date: The precise date and time when each transaction occurred, providing a timestamp for every purchase.
• Customer_Name: The name of the customer who completed the purchase, offering a means to identify individual customers.
• Product: A detailed list of all products included in a specific transaction.
• Total_Items: The total quantity of items purchased within a single transaction.
• Total_Cost: The overall financial value of the transaction, denominated in currency.
• Payment_Method: The chosen payment method for the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The geographical location (city) where the transaction took place.
• Store_Type: The classification of the store where the purchase was made, e.g., supermarket, convenience store, department store.
• Discount_Applied: A boolean indicator (True/False) showing whether a discount was applied to the transaction.
• Customer_Category: A categorisation of the customer based on their background or age group.
• Season: The season (e.g., spring, summer, autumn, winter) in which the purchase was made.
• Promotion: The specific type of promotion applied to the transaction, if any (e.g., "None", "BOGO", "Discount on Selected Items").

Distribution

This dataset is typically provided in a CSV file format. It contains approximately 1 million individual transaction records. The data spans a time range from 2020-01-01 to 2024-05-19. There are 329,738 unique customer names and 571,947 unique product entries. Payment methods are distributed with 25% Cash, 25% Debit Card, and 50% Other. Transaction locations include Boston (10%), Dallas (10%), and other cities (80%). Store types are categorised as Supermarket (17%), Pharmacy (17%), and other types (67%). Discounts were applied to approximately 50% of the transactions.

Usage

This dataset is ideally suited for: * Market Basket Analysis: Uncovering associations between products and identifying common buying patterns. * Customer Segmentation: Grouping customers based on their purchasing behaviour to target specific offers. * Pricing Optimisation: Developing strategies to optimise pricing and identify opportunities for discounts and promotions. * Retail Analytics: Analysing overall store performance and emerging customer trends. * Algorithmic Development: Testing and refining machine learning models for retail forecasting or recommendation systems.

Coverage

The dataset's geographic coverage includes transactions from various cities, such as Boston and Dallas, representing a broad, though simulated, global scope. The time range of the transactions extends from 1st January 2020 to 19th May 2024. Demographic insights are provided through the Customer_Category column, which classifies customers based on background or age group, allowing for demographic-based analyses. As a synthetic dataset, specific real-world demographic notes are not applicable.

License

CC0

Who Can Use It

This dataset is beneficial for a wide range of users, including: * Researchers: For academic studies on consumer behaviour and retail economics. * Data Scientists: To develop and validate predictive models, such as recommender systems or churn prediction models. * Analysts: For performing in-depth retail analytics, market basket analysis, and customer segmentation to inform business decisions. * Students: As a practical, realistic dataset for learning and applying data analysis techniques in a retail context.

Dataset Name Suggestions

• Retail Transactions Dataset
• Customer Purchasing Behaviour Data
• Market Basket Analysis Data
• Synthetic Retail Transactions
• E-commerce Transaction Log

Attributes

Original Dat

📝 Overview

This dataset provides the scikit-survival 0.23.1 Python package in .whl format, enabling users to perform survival analysis using machine learning techniques. scikit-survival is a powerful library that extends scikit-learn to handle censored data, commonly encountered in medical research, reliability engineering, and event-time prediction tasks.

📥 Installation

To install the package, first, download the .whl file from this Kaggle dataset. Then, install it using pip:

pip install scikit_survival-0.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Ensure that you have Python 3.13 installed, as this wheel is built specifically for that version.

🔬 Features

• Kaplan-Meier and Cox Proportional Hazards models
• Random survival forests for non-linear survival relationships
• Concordance index for model evaluation
• Integration with scikit-learn for easy model training and validation
• Handling of right-censored data for accurate event-time predictions

🏥 Use Cases

• Medical research: Predict patient survival times based on clinical features.
• Reliability engineering: Estimate the lifespan of mechanical components.
• Churn prediction: Analyze customer retention and attrition timelines.
• Credit risk modeling: Assess time until loan default.