Predict customer churn for credit card companny base on given features. You can use Machine Learning as well as Deep LEarning techniques to produce some meaningfull outputs. This dataset very basic and can be used for basic understanding.

Business Problem A business manager of a consumer credit card bank is facing the problem of customer attrition. They want to analyze the data to find out the reason behind this and leverage the same to predict customers who are likely to drop off.

Description:

This dataset contains information about bank customers and their churn status, which indicates whether they have exited the bank or not. It is suitable for exploring and analyzing factors influencing customer churn in banking institutions and for building predictive models to identify customers at risk of churning.

Features:

RowNumber: The sequential number assigned to each row in the dataset.

CustomerId: A unique identifier for each customer.

Surname: The surname of the customer.

CreditScore: The credit score of the customer.

Geography: The geographical location of the customer (e.g., country or region).

Gender: The gender of the customer.

Age: The age of the customer.

Tenure: The number of years the customer has been with the bank.

Balance: The account balance of the customer.

NumOfProducts: The number of bank products the customer has.

HasCrCard: Indicates whether the customer has a credit card (binary: yes/no).

IsActiveMember: Indicates whether the customer is an active member (binary: yes/no).

EstimatedSalary: The estimated salary of the customer.

Exited: Indicates whether the customer has exited the bank (binary: yes/no).

Usage:

• This dataset can be used for exploratory data analysis to understand the factors influencing customer churn in banks.
• It can also be used to build machine learning models for predicting customer churn based on the given features.

License:

This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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)

Dataset

This dataset was created by R. Joseph Manoj, PhD

Contents

Description:

In today's competitive telecom industry, retaining customers is crucial for business success. Customer churn, or the loss of customers, can significantly impact a company's revenue and growth. This dataset aims to provide a comprehensive set of features that can help in predicting whether a customer will churn or not.

The dataset contains various customer-related features such as demographics, account information, and service usage details. By analyzing these features, you can build machine learning models to predict customer churn, identify key factors leading to churn, and develop strategies to retain customers.

Dataset Columns:

customerID: Unique ID for each customer gender: Gender of the customer (Male/Female) SeniorCitizen: Whether the customer is a senior citizen (1, 0) Partner: Whether the customer has a partner (Yes/No) Dependents: Whether the customer has dependents (Yes/No) tenure: Number of months the customer has stayed with the company PhoneService: Whether the customer has phone service (Yes/No) MultipleLines: Whether the customer has multiple lines (Yes/No, No phone service) InternetService: Customer's internet service provider (DSL, Fiber optic, No) OnlineSecurity: Whether the customer has online security (Yes/No, No internet service) OnlineBackup: Whether the customer has online backup (Yes/No, No internet service) DeviceProtection: Whether the customer has device protection (Yes/No, No internet service) TechSupport: Whether the customer has tech support (Yes/No, No internet service) StreamingTV: Whether the customer has streaming TV (Yes/No, No internet service) StreamingMovies: Whether the customer has streaming movies (Yes/No, No internet service) Contract: The contract term of the customer (Month-to-month, One year, Two year) PaperlessBilling: Whether the customer has paperless billing (Yes/No) PaymentMethod: The customer's payment method (Electronic check, Mailed check, Bank transfer, Credit card) MonthlyCharges: The amount charged to the customer monthly TotalCharges: The total amount charged to the customer Churn: Whether the customer churned (Yes/No)

Inspiration:

This dataset provides an excellent opportunity to explore various machine learning algorithms for classification problems. You can use this dataset to:

Build and evaluate different machine learning models to predict customer churn. Perform feature engineering and selection to identify key factors contributing to churn. Develop strategies to reduce customer churn and improve customer retention.

Context : This dataset is part of a data science project focused on customer churn prediction for a subscription-based service. Customer churn, the rate at which customers cancel their subscriptions, is a vital metric for businesses offering subscription services. Predictive analytics techniques are employed to anticipate which customers are likely to churn, enabling companies to take proactive measures for customer retention.

Content : This dataset contains anonymized information about customer subscriptions and their interaction with the service. The data includes various features such as subscription type, payment method, viewing preferences, customer support interactions, and other relevant attributes. It consists of three files such as "test.csv", "train.csv", "data_descriptions.csv".

Columns :

CustomerID: Unique identifier for each customer

SubscriptionType: Type of subscription plan chosen by the customer (e.g., Basic, Premium, Deluxe)

PaymentMethod: Method used for payment (e.g., Credit Card, Electronic Check, PayPal)

PaperlessBilling: Whether the customer uses paperless billing (Yes/No)

ContentType: Type of content accessed by the customer (e.g., Movies, TV Shows, Documentaries)

MultiDeviceAccess: Whether the customer has access on multiple devices (Yes/No)

DeviceRegistered: Device registered by the customer (e.g., Smartphone, Smart TV, Laptop)

GenrePreference: Genre preference of the customer (e.g., Action, Drama, Comedy)

Gender: Gender of the customer (Male/Female)

ParentalControl: Whether parental control is enabled (Yes/No)

SubtitlesEnabled: Whether subtitles are enabled (Yes/No)

AccountAge: Age of the customer's subscription account (in months)

MonthlyCharges: Monthly subscription charges

TotalCharges: Total charges incurred by the customer

ViewingHoursPerWeek: Average number of viewing hours per week

SupportTicketsPerMonth: Number of customer support tickets raised per month

AverageViewingDuration: Average duration of each viewing session

ContentDownloadsPerMonth: Number of content downloads per month

UserRating: Customer satisfaction rating (1 to 5)

WatchlistSize: Size of the customer's content watchlist

Acknowledgments : The dataset used in this project is obtained from Data Science Challenge on Coursera and is used for educational and research purposes. Any resemblance to real persons or entities is purely coincidental.

Dataset

This dataset was created by Avinash Bhardwaz

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Sayed Ali Elsayed Hassaan

Contents

Dataset

This dataset was created by Mahmudul Haque Shawon

Released under Apache 2.0

Contents

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This dataset simulates customer behavior for a fictional telecommunications company. It contains demographic information, account details, services subscribed to, and whether the customer ultimately churned (stopped using the service) or not. The data is synthetically generated but designed to reflect realistic patterns often found in telecom churn scenarios.

Purpose:

The primary goal of this dataset is to provide a clean and straightforward resource for beginners learning about:

• Exploratory Data Analysis (EDA): Understanding customer characteristics and identifying potential drivers of churn through visualization and statistical summaries.
• Data Preprocessing: Handling categorical features (like converting text to numbers) and scaling numerical features.
• Classification Modeling: Building and evaluating simple machine learning models (like Logistic Regression or Decision Trees) to predict customer churn.

Features:

The dataset includes the following columns:

• CustomerID: Unique identifier for each customer.
• Age: Customer's age in years.
• Gender: Customer's gender (Male/Female).
• Location: General location of the customer (e.g., New York, Los Angeles).
• SubscriptionDurationMonths: How many months the customer has been subscribed.
• MonthlyCharges: The amount the customer is charged each month.
• TotalCharges: The total amount the customer has been charged over their subscription period.
• ContractType: The type of contract the customer has (Month-to-month, One year, Two year).
• PaymentMethod: How the customer pays their bill (e.g., Electronic check, Credit card).
• OnlineSecurity: Whether the customer has online security service (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support service (Yes, No, No internet service).
• StreamingTV: Whether the customer has TV streaming service (Yes, No, No internet service).
• StreamingMovies: Whether the customer has movie streaming service (Yes, No, No internet service).
• Churn: (Target Variable) Whether the customer churned (1 = Yes, 0 = No).

Data Quality:

This dataset is intentionally clean with no missing values, making it easy for beginners to focus on analysis and modeling concepts without complex data cleaning steps.

Inspiration:

Understanding customer churn is crucial for many businesses. This dataset provides a sandbox environment to practice the fundamental techniques used in churn analysis and prediction.

Dataset Description This dataset contains information about 8,500+ mobile service customers, including demographic details, device usage, billing patterns, and call behavior. The primary goal of this dataset is to enable analysis and modeling to predict customer churn — i.e., customers who decide to drop their mobile service provider.

The data includes 33 features and one binary target column (customer_dropped). This dataset is ideal for exploring churn prediction models, customer segmentation, lifetime value analysis, and marketing strategy development.

Features - customer_id: Unique identifier for each customer - age: Age of the customer - job: Occupation or profession of the customer - urban_rural: Indicates whether the customer resides in an urban or rural area - marital_status: Marital status of the customer - kids: Number of children the customer has - disposable_income: Disposable income of the customer - mobiles_changed: Number of times the customer has changed their mobile device - mobile_age: Age of the current mobile device - own_smartphone: Indicates whether the customer owns a smartphone - current_mobile_price: Price of the customer's current mobile device - credit_card_type: Type of credit card held - own_house: Indicates whether the customer owns a house - own_cr_card: Indicates whether the customer owns a credit card - monthly_bill: Monthly bill for mobile service - call_mins: Total call minutes used - basic_plan_amount: Basic mobile plan amount - extra_mins: Extra minutes used beyond the plan - roam_call_mins: Roaming call minutes - call_mins_delta: Change in call minutes compared to the previous billing period - bill_amount_delta: Change in bill amount compared to the previous billing period - incoming_call_mins: Total incoming call minutes - outgoing_calls: Number of outgoing calls - incoming_calls: Number of incoming calls - day_night_call_ratio: Ratio of call minutes during the day versus night - day_night_call_delta: Change in day vs night call minutes compared to the previous period - calls_dropped: Number of calls dropped - loyalty_months: Customer tenure in months - complaint_calls: Number of complaint calls made - promo_calls_made: Number of promotional calls made - promo_offers_accepted: Number of promotional offers accepted - new_numbers_called: Number of new contacts called - customer_dropped: Target column indicating churn (1 = churned, 0 = retained)

Use Cases - Develop machine learning models for churn prediction - Perform customer segmentation and behavioral profiling - Analyze call usage trends and billing sensitivity - Identify key drivers of customer loyalty or attrition - Design data-driven retention strategies

Description

This dataset is designed for analyzing customer behavior and predicting customer churn in a retail store. With 5,329 samples and 19 independent variables, the dataset provides a comprehensive view of various factors that influence whether a customer will continue their engagement with the store or not. The primary goal is to derive actionable insights and trends that can improve overall business performance, particularly in reducing customer churn.

Dependent Variable

Customer Churn Indicator: This binary variable indicates whether a customer has churned (i.e., stopped engaging with the retail store) or not. It serves as the target variable for the machine learning model.

Independent Variables

1. Customer Information: Customer ID: Unique identifier for each customer. Gender: Gender of the customer (Male/Female). Marital Status: Indicates whether the customer is single, married, divorced, etc. Number of Complaints: Total number of complaints filed by the customer to the retail store. Total Orders (1 month): Number of orders placed by the customer in the last month.

2. Transaction Information: Preferred Log-In Device: The type of device type used by the customer to connect to the retail store for purchases (e.g., mobile phone, computer). Payment Method: The payment method preferred by the customer (e.g., Credit Card, UPI). Product Category: The category to which the purchased products belong. Distance from Warehouse: The distance between the retail store's warehouse and the customer's location.

Objective

The main objective of analyzing this dataset is to predict customer churn and understand the factors contributing to it. By doing so, the retail store can develop targeted strategies for customer retention, optimize marketing efforts, and improve overall customer satisfaction.

Use Case

The insights gained from this analysis will be invaluable for the store's management and marketing teams. They can identify patterns and trends related to customer churn, enabling them to take proactive steps to retain valuable customers, address customer complaints effectively, and tailor marketing campaigns to specific customer segments. The ultimate goal is to enhance business performance by reducing churn and increasing customer loyalty.

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This Global Customer Churn Dataset is meticulously curated to aid in understanding and predicting customer churn Behaviour across various industries. With detailed customer profiles, including demographics, product interactions, and banking behaviors, this dataset is an invaluable resource for developing machine learning models aimed at identifying at-risk customers and devising targeted retention strategies."

Data Description:

Break down the dataset in detail, describing what each column represents:

RowNumber: A unique identifier for each row in the dataset.

CustomerId: Unique customer identification number.

Surname: The last name of the customer (for privacy reasons, consider anonymizing this data if not already done).

CreditScore: The customer's credit score at the time of data collection.

Geography: The customer's country or region, providing insights into location-based trends in churn.

Gender: The customer's gender.

Age: The customer's age, valuable for demographic analysis.

Tenure: The number of years the customer has been with the bank.

Balance: The customer's account balance.

Num Of Products: The number of products the customer has purchased or subscribed to.

HasCrCard: Indicates whether the customer has a credit card (1) or not (0).

IsActiveMember: Indicates whether the customer is an active member (1) or not (0).

EstimatedSalary: The customer's estimated salary.

Exited: The target variable, indicating whether the customer has churned (1) or not (0).

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 dataset contains synthetic data generated for customer churn analysis. It includes 1000 entries representing customer information, such as demographics, account details, subscription types, and churn status. The data is ideal for predictive modeling, machine learning algorithms, and exploratory data analysis (EDA). Features: CustomerID: A unique identifier for each customer. Gender: Male or Female. Age: Customer's age in years. Geography: Country or region of the customer (e.g., Germany, France, UK). Tenure: Number of months the customer has been with the company. Contract: Type of subscription (Month-to-month, One-year, Two-year). MonthlyCharges: The amount billed monthly. TotalCharges: The total amount billed to date. PaymentMethod: Method used for payments (e.g., Credit card, Direct debit). IsActiveMember: Whether the customer is an active member (1 = Active, 0 = Inactive). Churn: Indicates whether the customer has churned (Yes/No).

Dataset Overview for XYZ Multistate Bank:

This dataset is for XYZ Multistate Bank and contains various columns that capture key aspects of customer behavior and attributes. Each column provides valuable insights into the factors influencing customer churn, with the goal of predicting which customers are most likely to leave the bank. Below is an explanation of each column and its relevance to customer retention.

1. RowNumber:
The "RowNumber" column corresponds to the unique record number for each customer entry. It has no impact on the outcome of customer churn but is used to identify and organize data within the dataset. Since it doesn't contain any meaningful information related to customer behavior, it is not relevant for churn prediction and can be excluded in analysis.

2. CustomerId:
The "CustomerId" column consists of randomly generated identifiers for each customer. While this ID helps to uniquely distinguish each customer, it has no impact on the likelihood of a customer leaving the bank. As a categorical feature, it does not contribute to the analysis of churn and can be omitted when building predictive models.

3. Surname:
The "Surname" column holds the last names of customers. Although this information is useful for identification purposes, it does not have a direct relationship with customer churn. Since a customer's surname is not an influencing factor in their decision to stay or leave the bank, it is not considered relevant for churn prediction and can be disregarded.

4. CreditScore:
"CreditScore" is an important variable that can significantly affect customer churn. Customers with higher credit scores are generally considered more financially stable and less likely to leave the bank, as they are less likely to face issues with financial institutions. Therefore, this feature can provide valuable insights into customer retention and should be included in churn analysis.

5. Geography:
"Geography" refers to the geographical location of the customer, which can influence their likelihood of leaving the bank. Customers living in different regions may have varying experiences with the bank’s services, fees, or offerings, making this an important factor to explore. Understanding regional differences helps tailor retention strategies for specific locations and improve overall customer satisfaction.

6. Gender:
"Gender" is an interesting demographic factor to consider in churn prediction. While gender itself may not directly affect the likelihood of a customer leaving, it could correlate with other behavioral patterns or preferences that influence retention. Analyzing gender in combination with other features may reveal potential insights, making it worthwhile to examine as part of the churn model.

7. Age:
The "Age" column is a key factor in understanding customer behavior. Typically, older customers are less likely to churn because they tend to be more established with their financial institutions and may have a greater sense of loyalty. In contrast, younger customers may be more likely to switch banks, especially if they are seeking better services or offers. This feature is essential for predicting churn and should be analyzed in detail.

8. Tenure:
"Tenure" refers to the number of years a customer has been with the bank. Longer-tenured customers are often more loyal and less likely to leave the bank. The correlation between tenure and churn is strong, as established relationships tend to make customers less susceptible to leaving. This is a critical factor for churn prediction and should be given high consideration when modeling customer retention.

9. Balance:
The "Balance" column reflects the amount of money a customer holds in their bank account. Customers with higher balances are typically more invested in the bank and are less likely to leave. In contrast, customers with low balances may be more willing to switch to other financial institutions offering better rates or services. This feature plays a significant role in churn prediction, as financial stakes are directly tied to loyalty.

10. NumOfProducts:
"NumOfProducts" refers to the number of products (e.g., savings accounts, loans, credit cards) that a customer has with the bank. Customers with multiple products are usually more invested in the bank, making them less likely to leave. The greater the number of products, the higher the customer's commitment to the bank, making this feature highly relevant in understanding churn patterns and developing retention strategies.

11. HasCrCard:
"HasCrCard" indicates whether or not a customer holds a credit card with the bank. Having a credit card typically reduces the likelihood of customer churn, as credit cards are a widely used financial product that locks customers into a long-term relatio...

The Customer Churn Classification dataset is a vital resource for businesses seeking to understand and predict customer churn, a critical metric that represents the rate at which customers stop doing business with a company over a given period. Understanding churn is essential for any customer-focused company, as retaining customers is generally more cost-effective than acquiring new ones. The dataset is designed to provide a detailed view of customer characteristics and behaviors that could potentially lead to churn, allowing companies to take preemptive action to improve customer retention.

Breakdown of Dataset Features This dataset includes several features, each contributing valuable information for analyzing customer behaviors and identifying potential churn risks:

Customer ID: A unique identifier for each customer. This column is useful for keeping track of individual customers without revealing personal details like names or contact information. It is essential for organizing data and ensuring that individual records can be tracked over time.

Surname: This column contains the surname of the customer. While it might not directly influence churn, it could be used in personalized marketing strategies. For example, companies could address customers by their last names in emails or other forms of communication to foster a sense of personal connection.

Credit Score: A key financial indicator, the credit score reflects a customer's creditworthiness and financial health. A low credit score might indicate a higher likelihood of churn, as these customers may be more prone to financial difficulties or more likely to switch to competitors offering better financial terms.

Geography: The geographical location of customers. This feature helps businesses understand regional patterns in customer behavior, such as churn rates varying between different countries or cities. Geographic data might reveal that certain areas have more competitive markets, which could lead to higher churn.

Gender: This feature identifies the gender of customers, which can be useful in understanding churn trends across different demographics. Some studies suggest that churn rates can differ between men and women due to varying expectations, needs, and preferences in service.

Age: Age plays a significant role in customer churn, as different age groups tend to have distinct purchasing habits and loyalty tendencies. Younger customers might be more open to exploring competitor options, while older customers might exhibit more loyalty but could churn if they feel underappreciated.

Tenure: This feature reflects how long a customer has been with the company. Longer tenure typically correlates with greater loyalty, as these customers have built a more robust relationship with the company. However, if long-tenured customers churn, it could signal deeper issues with service quality or product offerings.

Balance: The account balance of customers, which provides insight into their financial involvement with the company. Customers with higher balances may be less likely to churn, as they are more financially invested in the company, while customers with lower balances may have less at stake and are more likely to switch to competitors.

Number of Products Held: The number of products or services the customer is subscribed to. Generally, customers who use multiple products are more likely to remain loyal, as switching would involve more effort and a higher cost in terms of time and disruption to their routine.

Credit Card Status: This feature identifies whether the customer has a credit card issued by the company. Customers who own a credit card might have a stronger financial relationship with the company and, as a result, could exhibit lower churn rates. However, if customers are dissatisfied with their credit card, it might lead to a higher chance of churn.

Active Membership Status: Indicates whether the customer is actively using their membership or account. Customers with active accounts are usually more engaged with the company's products or services and are less likely to churn. In contrast, customers with inactive memberships might be at risk of churn due to disinterest or dissatisfaction.

Estimated Salary: A customer's estimated salary provides an indication of their financial well-being. Higher-income customers may have different expectations of service quality and could churn if they feel that the company isn't meeting their standards. Conversely, lower-income customers might be more sensitive to pricing and more prone to switch for better deals.

Exited: This is the target column, which indicates whether the customer has churned (1 for churned and 0 for not churned). This is the dependent variable that is predicted based on the other features, and it forms the basis of churn prediction models.

Importance of Churn Prediction The Custo...

Telecom Customer Churn Prediction: Case Study Problem Statement

Introduction The telecom industry is highly competitive, and customer churn is a significant concern. Retaining existing customers is often more cost-effective than acquiring new ones. This case study aims to build a predictive model to identify customers who are likely to churn, allowing the company to take proactive measures to retain them.

Objective The primary objective is to develop a machine learning model that can predict customer churn with high accuracy. The model will use historical data to identify patterns or characteristics of customers who have churned in the past.

Data Two datasets are provided: 1. train.csv: This dataset contains historical data, including whether or not a customer has churned. Features include customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, and Churn. 2. active_customers.csv: This dataset contains data for active customers without the 'Churn' label. The objective is to predict the likelihood of these active customers churning in the near future.

Guidelines

Data Exploration 1. Perform initial data exploration to understand the data types, missing values, and summary statistics. 2. Visualize the data to identify patterns and correlations.

Data Preprocessing 1. Handle missing values if any. 2. Convert categorical variables into numerical form. 3. Normalize/Standardize numerical features if necessary.

Feature Engineering 1. Create new features that might help improve the model. 2. Select relevant features based on statistical tests.

Model Building 1. Split the train.csv data into training and validation sets. 2. Try different algorithms like Logistic Regression, Random Forest, and Gradient Boosting to train the model. 3. Tune hyperparameters for better performance.

Model Evaluation 1. Evaluate the model using metrics like accuracy, precision, recall, F1-score, and ROC-AUC. 2. Use cross-validation for more robust model evaluation.

Prediction 1. Use the final model to predict the 'Churn' label for the active_customers.csv dataset.

Data Dictionary 1. customerID: Unique identifier for each customer 2. gender: Gender of the customer (Male/Female) 3. SeniorCitizen: Whether the customer is a senior citizen or not (1 for Yes, 0 for No) 4. Partner: Whether the customer has a partner (Yes/No) 5. Dependents: Whether the customer has dependents (Yes/No) 6. tenure: Number of months the customer has been with the company 7. PhoneService: Whether the customer has a phone service (Yes/No) 8. MultipleLines: Whether the customer has multiple lines (Yes/No/No phone service) 9. InternetService: Type of internet service the customer has (DSL, Fiber optic, No) 10. OnlineSecurity: Whether the customer has online security feature (Yes/No/No internet service) 11. OnlineBackup: Whether the customer has online backup feature (Yes/No/No internet service) 12. DeviceProtection: Whether the customer has device protection feature (Yes/No/No internet service) 13. TechSupport: Whether the customer has tech support feature (Yes/No/No internet service) 14. StreamingTV: Whether the customer has streaming TV feature (Yes/No/No internet service) 15. StreamingMovies: Whether the customer has streaming movies feature (Yes/No/No internet service) 16. Contract: Type of contract the customer has (Month-to-month, One year, Two year) 17. PaperlessBilling: Whether the customer has opted for paperless billing (Yes/No) 18. PaymentMethod: Method of payment (Electronic check, Mailed check, Bank transfer, Credit card) 19. MonthlyCharges: Monthly charges for the customer 20. TotalCharges: Total charges for the customer till date 21. Churn: Whether the customer churned or not (Yes is 1/No is 0)

This is a sample dataset of Telco Customer Churn. It's inspired by the original dataset of "Telco customer churn (11.1.3+)" from IBM Business Analytics Community. This sample dataset is being cleaned and aggregated from the original dataset. It would be good for telco customer churn analysis or prediction by the classification or regression model for experiment and learning purposes.

Column Description: * customerID: A unique ID that identifies each customer. * gender: The customer’s gender: Male (1), Female (0). * SeniorCitizen: Indicates if the customer is 65 or older: No (0), Yes (1). * Partner: Service contract is resold by the partner: No (0), Yes (1). * Dependents: Indicates if the customer lives with any dependents: No (0), Yes (1). * Tenure: Indicates the total amount of months that the customer has been with the company. * PhoneService: Indicates if the customer subscribes to home phone service with the company: No (0), Yes (1). * MultipleLines: Indicates if the customer subscribes to multiple telephone lines with the company: No (0), Yes (1). * InternetService: Indicates if the customer subscribes to Internet service with the company: No (0), DSL (1), Fiber optic (2). * OnlineSecurity: Indicates if the customer subscribes to an additional online security service provided by the company: No (0), Yes (1), NA (2). * OnlineBackup: Indicates if the customer subscribes to an additional online backup service provided by the company: No (0), Yes (1), NA (2). * DeviceProtection: Indicates if the customer subscribes to an additional device protection plan for their Internet equipment provided by the company: No (0), Yes (1), NA (2). * TechSupport: Indicates if the customer subscribes to an additional technical support plan from the company with reduced wait times: No (0), Yes (1), NA (2). * StreamingTV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: No (0), Yes (1), NA (2). The company does not charge an additional fee for this service. * StreamingMovies: Indicates if the customer uses their Internet service to stream movies from a third party provider: No (0), Yes (1), NA (2). The company does not charge an additional fee for this service. * Contract: Indicates the customer’s current contract type: Month-to-Month (0), One Year (1), Two Year (2). * PaperlessBilling: Indicates if the customer has chosen paperless billing: No (0), Yes (1). * PaymentMethod: Indicates how the customer pays their bill: Bank transfer - automatic (0), Credit card - automatic (1), Electronic cheque (2), Mailed cheque (3). * MonthlyCharges: Indicates the customer’s current total monthly charge for all their services from the company. * TotalCharges: Indicates the customer’s total charges. * Churn: Indicates if the customer churn or not: No (0), Yes (1).