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...

Analysis of ‘Automobile Customer’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/automobile-customer on 28 January 2022.

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

Context

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits.

Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. Companies also hope to gain a deeper understanding of their customers' preferences and needs with the idea of discovering what each segment finds most valuable to more accurately tailor marketing materials toward that segment.

Content

An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4 and P5). After intensive market research, they’ve deduced that the behavior of new market is similar to their existing market.

In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D ). Then, they performed segmented outreach and communication for different segment of customers. This strategy has work exceptionally well for them. They plan to use the same strategy on new markets and have identified 2627 new potential customers.

The dataset provides the details of the existing and potential customers of the company based on the purchase history and the corresponding segments they have been classified into.

Variable description

• CustomerID : unique customer ID
• Gender : gender of the customer
• Married : marital status of the customer
• Age : age of the customer
• Graduated : specifies whether the customer a graduate?
• Profession : profession of the customer
• WorkExperience : work experience of the customer in years
• SpendingScore : spending score of the customer
• FamilySize : number of family members of the customer (including the customer)
• Category : anonymised category for the customer
• Segmentation : (target variable) customer segment of the customer

Inspiration

The dataset is ideal for anyone looking to practice customer segmentation.

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

Dataset

This dataset was created by Khushi Shah

Contents

**New to machine learning and data science? No question is too basic or too simple. Use this place to post any first-timer clarifying questions for the classification algorithm or related to datasets ** !This file contains demographics about customer and whether that customer clicked the ad or not . You this file to use classification algorithm to predict on the basis of demographics of customer as independent variable

This data set contains the following features:

This data set contains the following features:

1. 'User ID': unique identification for consumer
2. 'Age': cutomer age in years
3. 'Estimated Salary': Avg. Income of consumer
4. 'Gender': Whether consumer was male or female
5. 'Purchased': 0 or 1 indicated clicking on Ad

Analysis of ‘Customer Clustering’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dev0914sharma/customer-clustering on 28 January 2022.

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

Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Using the above data companies can then outperform the competition by developing uniquely appealing products and services. You are owing a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. You want to understand the customers like who are the target customers so that the sense can be given to marketing team and plan the strategy accordingly.

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

Description: 👉 Download the dataset here This dataset is specifically designed for the classification of e-commerce products based on their images, forming a critical part of an experimental study aimed at improving product categorization using computer vision techniques. Accurate categorization is essential for e-commerce platforms as it directly influences customer satisfaction, enhances user experience, and optimizes sales by ensuring that products are presented in the correct categories.… See the full description on the dataset page: https://huggingface.co/datasets/gtsaidata/E-commerce-Product-Image-Classification-Dataset.

🇬🇧 English:

This synthetic dataset was designed for those who want to practice customer churn prediction using structured tabular data. It includes 1,000 customer records, each containing features such as age, service tenure, service type, monthly fee, and churn status.

Use this dataset to:

• Build classification models like Logistic Regression, Random Forest, or XGBoost
• Explore churn-related patterns (e.g. short tenure, high price, mobile users)
• Simulate real-world business scenarios without needing real customer data

Features:

• customer_id: Unique customer ID (e.g. C1001 to C2000)
• age: Age of the customer
• tenure: Number of months the customer has been active
• service_type: Type of service used (internet, mobile, tv, bundle)
• monthly_fee: Monthly subscription fee
• churn: Whether the customer has left the service (1 = Yes, 0 = No)

🇹🇷 Türkçe:

Bu sentetik veri seti, müşteri kaybı (churn) tahmini üzerine çalışmak isteyen araştırmacılar ve öğrenciler için oluşturulmuştur. 1.000 müşteriye ait yaş, hizmet süresi, hizmet türü, aylık ödeme ve abonelik durumuna dair sahte ancak gerçekçi veriler içerir.

Bu veri seti sayesinde:

• Logistic Regression, Random Forest, XGBoost gibi sınıflandırma modelleri uygulanabilir
• Churn davranışına etki eden faktörler incelenebilir (örneğin kısa üyelik, yüksek fiyat, mobil kullanıcılar)
• Gerçek müşteri verilerine erişim gerekmeden iş senaryoları çalışılabilir

🧾 Değişkenler:

• customer_id: Müşteri kimliği (ör. C1001 – C2000)
• age: Müşteri yaşı
• tenure: Kaç aydır hizmet aldığı
• service_type: Aldığı hizmet türü (internet, mobile, tv, bundle)
• monthly_fee: Aylık ödeme miktarı
• churn: Hizmeti bırakıp bırakmadığı (1 = Evet, 0 = Hayır)

Dataset Card for "ecommerce-customer-support"

More Information needed

The Customer Analytics Platform (CAP) market is experiencing robust growth, projected to reach a market size of $12.45 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 19.01%. This expansion is fueled by several key drivers. The increasing adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both small and medium-sized enterprises (SMEs) and large enterprises. Furthermore, the rising demand for personalized customer experiences and the need for data-driven decision-making are pushing businesses to invest heavily in CAPs. Advanced analytical capabilities, such as predictive modeling and AI-powered insights, enable businesses to better understand customer behavior, improve marketing effectiveness, and enhance customer retention. The diverse range of solutions, including social media analytics, web analytics, and voice of the customer (VOC) tools, caters to a broad spectrum of business needs. While data security and privacy concerns present a challenge, the industry is actively addressing these concerns through robust security measures and compliance with data protection regulations. The competitive landscape is dynamic, with established players like Adobe, IBM, and Salesforce competing alongside specialized analytics providers. The market's segmentation across deployment types (on-premise, cloud), solutions, organization size, service models, and end-user industries reflects the diverse applications of CAPs across various sectors. Looking ahead to 2033, the CAP market is poised for continued expansion, driven by technological advancements, growing data volumes, and the increasing adoption of advanced analytics techniques. The North American market currently holds a significant share, but regions like Asia and Europe are expected to witness substantial growth due to increasing digitalization and rising adoption rates among businesses in these regions. Companies are increasingly leveraging CAPs to optimize their customer journeys, personalize marketing campaigns, and improve operational efficiency. The integration of CAPs with other enterprise systems, such as CRM and ERP, further enhances their value and contributes to their widespread adoption. The focus on improving customer lifetime value and driving revenue growth makes CAPs a strategic investment for businesses across various industries. Recent developments include: February 2024: Accenture has reached an agreement to acquire GemSeek, a provider of customer experience analytics. GemSeek aids global businesses in comprehending their customers through insights, analytics, and AI-driven predictive models. This acquisition highlights Accenture Song's continued investment in data and AI capabilities. Accenture Song, recognized as the world's largest tech-powered creative group, aims to leverage these capabilities to assist clients in expanding their businesses and maintaining relevance with their customers., January 2024: MX Technologies, Inc. unveiled its new Customer Analytics tool, tailored for financial service providers. This tool harnesses advanced transaction data and insightful consumer analytics. With these capabilities, financial institutions can boost deposits and engagement, pinpoint cross-sell opportunities, optimize ROI on marketing endeavors, and foresee and mitigate customer churn.. Key drivers for this market are: Rising Demand for Improved Customer Satisfaction, Increase in Social Media Concern to Address Customer Behavior. Potential restraints include: Rising Demand for Improved Customer Satisfaction, Increase in Social Media Concern to Address Customer Behavior. Notable trends are: Growing Retail Sector to Drive Market Growth.

The Image Recognition and Classification Technology market is experiencing robust growth, driven by increasing adoption across diverse sectors. While the exact market size for 2025 isn't provided, considering the rapid advancements in AI and machine learning, coupled with the expanding applications in areas like healthcare, retail, and autonomous vehicles, a reasonable estimation for the 2025 market size would be around $15 billion. This significant market value reflects the increasing demand for efficient and accurate image analysis solutions. Assuming a conservative Compound Annual Growth Rate (CAGR) of 20% for the forecast period (2025-2033), the market is projected to reach approximately $80 billion by 2033. This substantial growth is fueled by several key drivers, including the decreasing cost of computing power, the proliferation of high-resolution cameras in various devices, and the development of sophisticated algorithms capable of handling complex image data. Technological advancements such as deep learning and convolutional neural networks are significantly improving the accuracy and speed of image recognition and classification, further accelerating market expansion. The market segmentation reveals a diverse landscape. Object detection, facial recognition, and OCR (Optical Character Recognition) are prominent segments, each catering to specific needs across various applications. The IT & Telecom sector is currently a leading adopter, leveraging the technology for applications such as security, customer identification, and data analysis. However, other sectors like healthcare (medical image analysis, disease diagnosis), retail (inventory management, customer behavior analysis), and transportation & logistics (autonomous driving, package sorting) are experiencing rapid growth and are poised to become significant market drivers in the coming years. While data privacy concerns and the need for robust data security present some restraints, the overall market outlook remains overwhelmingly positive, anticipating sustained growth throughout the forecast period due to continuous innovation and expanding applications.

Success demands customer-centric models – a shift towards this approach is essential in order to create products that consumers want to buy and brands that consumers want to associate themselves with. Attitudinal traits are rarely mutually exclusive – products or services that cater to a trend crossover offer consumers multiple benefits and will be attractive to a wider range of consumers. Trust is key. A sense of honesty from an insurance provider is not only the most widely sought factor among consumers – regardless of customer group – but also drives among the strongest sources of sentiment. Read More

This statistic shows the United States alternative medicine industry market segmentation in 2011, by client age and gender. Women aged 30 to 69 make up 26 percent of the alternative medicine industry.

To increase the accuracy of the telco churn prediction models, I merged the following two datasets (using ID as index):

https://www.kaggle.com/blastchar/telco-customer-churn https://www.kaggle.com/ylchang/telco-customer-churn-1113

The additional features of the second dataset (like satisfaction score, total revenues and cltv) can increase the accuracy of the models.

I cleaned the merged dataset and also added the clean version here ("Telco_customer_churn_cleaned.csv").

Inspiration:

The dataset can be used for EDA, classification, churn prediction, segmentation etc.

This table contains 63 series, with data for years 2007 - 2012 (not all combinations necessarily have data for all years), and was last released on 2015-06-22. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), North American Industry Classification System (NAICS) (3 items: Consumer goods and general rental; General rental centres; Consumer goods rental ...), Industry expenditures (21 items: Total operating expenses; Professional and business services fees; Salaries; wages and benefits; Commissions paid to non-employees ...).

The Life and Health (L&H) Insurance industry is experiencing a rapid transformation driven by the increasing adoption of data analytics. The market, valued at $2647.3 million in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 9.2% from 2025 to 2033. This robust growth is fueled by several key factors. Firstly, the need for improved risk assessment and underwriting is pushing insurers to leverage advanced analytics for predictive modeling. This allows for more accurate pricing, reduced fraud, and better customer segmentation. Secondly, demographic profiling enabled by data analytics helps insurers tailor products and services to specific customer needs, leading to increased customer satisfaction and retention. Data visualization tools further enhance decision-making by providing clear and concise insights into complex datasets, facilitating better strategy development and operational efficiency. Finally, the rise of Insurtech companies and the increasing availability of sophisticated software solutions are accelerating the adoption of data analytics across the L&H insurance sector. The competitive landscape is shaped by a mix of established players like Deloitte, SAP AG, and IBM, alongside specialized Insurtech firms offering innovative data analytics solutions. The segmentation of the market reveals significant opportunities across various applications and types. Predictive analysis, demographic profiling, and data visualization are the most prominent application segments, reflecting the industry's focus on risk management, customer understanding, and improved operational efficiency. The service and software segments represent the primary delivery models for data analytics solutions. While North America currently holds a dominant market share, regions like Asia-Pacific are experiencing rapid growth, driven by increasing digitalization and a rising middle class with growing insurance needs. Regulatory changes promoting data sharing and increased customer data privacy awareness are likely to influence market dynamics in the coming years. The key challenges include data security concerns, the need for skilled data scientists, and the integration of legacy systems with new data analytics platforms. Successfully navigating these challenges will be crucial for insurers to fully capitalize on the transformative potential of data analytics.

Performance measures for different datasets.

Rust Classification

## Overview

Rust Classification is a dataset for classification tasks - it contains Rust annotations for 528 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Customer Experience Software market has emerged as a vital component in today's competitive business landscape, empowering organizations to enhance interaction with their customers across various touchpoints. This software encompasses an array of tools designed to track customer satisfaction, gather feedback, ma

Dataset

This dataset was created by Sumeet Sawant

Contents

It contains the following files:

The Data Classification market is experiencing robust growth, projected at a 24% Compound Annual Growth Rate (CAGR) from 2025 to 2033. This surge is driven by increasing regulatory compliance mandates (GDPR, CCPA, etc.), the escalating volume of sensitive data generated across diverse industries, and the rising adoption of cloud computing and remote work models. The need to secure intellectual property, prevent data breaches, and maintain customer trust fuels demand for sophisticated data classification solutions. Software solutions dominate the market, owing to their scalability and ease of integration, followed by services which cater to customization and implementation needs. Cloud deployment is rapidly gaining traction due to its flexibility and cost-effectiveness compared to on-premise solutions. Access management and governance & compliance management are the leading application segments, reflecting the critical importance of controlling data access and ensuring adherence to regulatory frameworks. The BFSI (Banking, Financial Services, and Insurance) sector is a key adopter of data classification technologies due to the highly sensitive nature of financial data, followed closely by healthcare and government. While the market faces restraints such as the complexity of implementation and integration challenges for some organizations, the overall growth trajectory remains highly positive due to the continued evolution of data security and privacy concerns. The North American market currently holds a significant share due to early adoption of advanced technologies and robust regulatory frameworks. However, the Asia-Pacific region is poised for significant growth in the coming years, driven by increasing digitalization and rising awareness of data security risks. Major players like Amazon Web Services, Microsoft, and IBM are leading the market with comprehensive solutions, but smaller, specialized companies are also making inroads with niche offerings. The market is witnessing a shift towards AI and machine learning-powered solutions that automate data classification and improve accuracy. This innovation is enhancing efficiency and reducing manual effort, ultimately driving market expansion. The increasing focus on data security and privacy will sustain the growth momentum of the data classification market throughout the forecast period. Key drivers for this market are: , Government Regulations and Compliance for Privacy & Data Security; Concern for Data Theft due to Mismanagement; Surge in Analytics Applications with Stored Data. Potential restraints include: , Government Regulations and Compliance for Privacy & Data Security; Concern for Data Theft due to Mismanagement; Surge in Analytics Applications with Stored Data. Notable trends are: Surge in Data Security Solutions for Increased Malware Infection Rates in Computers.