Bank Customer Segmentation

Most banks have a large customer base - with different characteristics in terms of age, income, values, lifestyle, and more. Customer segmentation is the process of dividing a customer dataset into specific groups based on shared traits.

According to a report from Ernst & Young, “A more granular understanding of consumers is no longer a nice-to-have item, but a strategic and competitive imperative for banking providers. Customer understanding should be a living, breathing part of everyday business, with insights underpinning the full range of banking operations.

About this Dataset

This dataset consists of 1 Million+ transaction by over 800K customers for a bank in India. The data contains information such as - customer age (DOB), location, gender, account balance at the time of the transaction, transaction details, transaction amount, etc.

Interesting Analysis Ideas

The dataset can be used for different analysis, example -

1. Perform Clustering / Segmentation on the dataset and identify popular customer groups along with their definitions/rules
2. Perform Location-wise analysis to identify regional trends in India
3. Perform transaction-related analysis to identify interesting trends that can be used by a bank to improve / optimi their user experiences
4. Customer Recency, Frequency, Monetary analysis
5. Network analysis or Graph analysis of customer data.

E-Commerce Customer Segmentation Dataset This synthetic dataset contains information about 20 customers of an e-commerce platform, designed for customer segmentation and classification tasks.

Dataset Overview Each record represents a unique customer with demographic and behavioral features that help classify them into different customer segments.

Features: customer_id: Unique identifier for each customer

age: Age of the customer (years)

annual_income_k$: Annual income in thousands of dollars

spending_score: A score between 0 and 100 indicating customer spending habits (higher means more spending)

membership_years: Length of membership in years

segment: Customer segment label; possible values are:

Low (low-value customers)

Medium (medium-value customers)

High (high-value customers)

Potential Use Cases Customer segmentation

Targeted marketing campaigns

Customer lifetime value prediction

Behavioral analytics and profiling

Clustering and classification algorithm testing

Dataset Size 20 samples

6 columns

License This dataset is provided under the Apache 2.0 License.

Here's a step-by-step guide on how to approach user segmentation for FitTrackr:

Define your segmentation goals: Start by determining what you want to achieve with user segmentation. For example, you might want to identify the most engaged users, understand the demographics of your user base, or target specific user groups with personalized promotions.

Gather data: Collect relevant data about your app users. This can include demographic information (age, gender, location), app usage data (frequency of app usage, time spent on different features), user behavior (types of workouts, goals set, achievements unlocked), and any other relevant data points available to you.

Identify relevant segmentation variables: Based on the goals you defined, identify the key variables that will help you segment your user base effectively. For FitTrackr, potential variables could include age, gender, fitness goals (e.g., weight loss, muscle gain), workout preferences (e.g., cardio, strength training), and user engagement level.

Segment the user base: Use clustering techniques or segmentation algorithms to divide your user base into distinct segments based on the identified variables. You can employ methods such as k-means clustering, hierarchical clustering, or even machine learning algorithms like decision trees or random forests.

Analyze and profile each segment: Once the segmentation is done, analyze each segment to understand their characteristics, preferences, and needs. Create detailed user profiles for each segment, including demographic information, app usage patterns, fitness goals, and any other relevant attributes. This will help you tailor your marketing messages and app features to each segment's specific requirements.

Develop targeted strategies: Based on the insights gained from user profiles, develop targeted marketing strategies and app features for each segment. For example, if you have a segment of users who primarily focus on weight loss, you might create personalized workout plans or send them motivational content related to weight management.

Implement and evaluate: Implement the targeted strategies and monitor their effectiveness. Continuously evaluate and refine your segmentation approach based on user feedback, engagement metrics, and the achievement of your goals.

Hotel customer dataset with 31 variables describing a total of 83,590 instances (customers). It comprehends three full years of customer behavioral data. In addition to personal and behavioral information, the dataset also contains demographic and geographical information. This dataset contributes to reducing the lack of real-world business data that can be used for educational and research purposes. The dataset can be used in data mining, machine learning, and other analytical field problems in the scope of data science. Due to its unit of analysis, it is a dataset especially suitable for building customer segmentation models, including clustering and RFM (Recency, Frequency, and Monetary value) models, but also be used in classification and regression problems.

Living Identity™ Asia delivers 401M verified profiles across 7 high-growth Asian markets: Bangladesh, Indonesia, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. This dataset combines identity, lifestyle, demographic, and location signals — ideal for KYC, segmentation, and marketing expansion.

➤ Optimized For: ・Real-time KYC and identity verification ・Location-based audience analytics ・Data-driven market expansion strategy ・Cross-sell/upsell strategy based on lifestyle and affluence ・Customer segmentation and campaign design

➤ Designed For: Marketing & Media Agencies Plan hyper-targeted, region-specific campaigns

Retailers, E-Commerce & Payment Firms Expand across Asia using verified consumer intelligence

Customer Analytics & Intelligence Teams Enrich identity data with lifestyle and location layers

Audience Modeling & AI Teams Train segmentation and targeting models with ground-truth attributes

Financial Services Firms Improve onboarding, scoring, and customer profiling in underbanked markets

➤ Key Highlights: ・401M+ structured profiles across 7 countries ・6 months of refreshed historical activity ・Geo-coded data with lifestyle and demographic detail ・Core identity fields: name, ID, phone, email, address, government ID (where available) ・Delivered securely via on-premise systems

Delivered by 1datapipe®, the global leader in structured identity and lifestyle intelligence. Pricing and additional samples available upon request.

Project Overview: Customer Segmentation Using K-Means Clustering

Introduction In this project, I analysed customer data from a retail store to identify distinct customer segments. The dataset includes key attributes such as age, city, and total sales of the customers. By leveraging K-Means clustering, an unsupervised machine learning technique, I aim to group customers based on their age and sales metrics. These insights will enable the creation of targeted marketing campaigns tailored to the specific needs and behaviours of each customer segment.

Objectives - Cluster Customers: Use K-Means clustering to group customers based on age and total sales. - Analyse Segments: Examine the characteristics of each customer segment. - Targeted Marketing: Develop strategies for personalized marketing campaigns targeting each identified customer group.

Data Description The dataset comprises:

• Age: The age of the customers.
• City: The city where the customers reside.
• Total Sales: The total sales generated by each customer.

Methodology - Data Preprocessing: Clean and preprocess the data to handle any missing or inconsistent entries. - Feature Selection: Focus on age and total sales as primary features for clustering. - K-Means Clustering: Apply the K-Means algorithm to identify distinct customer segments. - Cluster Analysis: Analyse the resulting clusters to understand the demographic and sales characteristics of each group. - Marketing Strategy Development: Create targeted marketing strategies for each customer segment to enhance engagement and sales.

Expected Outcomes - Customer Segments: Clear identification of customer groups based on age and purchasing behaviour. - Insights for Marketing: Detailed understanding of each segment to inform targeted marketing efforts. - Business Impact: Enhanced ability to tailor marketing campaigns, potentially leading to increased customer satisfaction and sales.

By clustering customers based on age and total sales, this project aims to provide actionable insights for personalized marketing, ultimately driving better customer engagement and higher sales for the retail store.

Background COVID-19 pandemic had unprecedented global impact on health and society, highlighting the need for a detailed understanding of SARS-CoV-2 evolution in response to host and environmental factors. This study investigates the evolution of SARS-CoV-2 via mutation dynamics, focusing on distinct age cohorts, geographical location, and vaccination status within the Indian population, one of the nations most affected by COVID-19. Methodology Comprehensive dataset, across diverse time points during the Alpha, Delta, and Omicron variant waves, captured essential phases of the pandemic’s footprint in India. By leveraging genomic data from Global Initiative on Sharing Avian Influenza Data (GISAID), we examined the substitution mutation landscape of SARS-CoV-2 in three demographic segments: children (1–17 years), working-age adults (18–64 years), and elderly individuals (65+ years). A balanced dataset of 69,975 samples was used for the study, comprising 23,325 samples from each group. This design ensured high statistical power, as confirmed by power analysis. We employed bioinformatics and statistical analyses, to explore genetic diversity patterns and substitution frequencies across the age groups. Principal findings The working-age group exhibited a notably high frequency of unique substitutions, suggesting that immune pressures within highly interactive populations may accelerate viral adaptation. Geographic analysis emphasizes notable regional variation in substitution rates, potentially driven by population density and local transmission dynamics, while regions with more homogeneous strain circulation show relatively lower substitution rates. The analysis also revealed a significant surge in unique substitutions across all age groups during the vaccination period, with substitution rates remaining elevated even after widespread vaccination, compared to pre-vaccination levels. This trend supports the virus's adaptive response to heightened immune pressures from vaccination, as observed through the increased prevalence of substitutions in important regions of SARS-CoV-2 genome like ORF1ab and Spike, potentially contributing to immune escape and transmissibility. Conclusion Our findings affirm the importance of continuous surveillance on viral evolution, particularly in countries with high transmission rates. This research provides insights for anticipating future viral outbreaks and refining pandemic preparedness strategies, thus enhancing our capacity for proactive global health responses.

A. SUMMARY This dataset represents the COVID-19 vaccinations given to residents of San Francisco. All vaccines given to SF residents are included, no matter where the vaccination took place (the vaccine may have been administered in San Francisco or outside of San Francisco). The data are broken down by multiple demographic stratifications. This dataset also includes COVID-19 vaccinations given to SF residents by the San Francisco Department of Public Health (SFDPH). Data provides counts for residents who have received at least one dose, residents who have completed a primary vaccine series, residents who have received one or two monovalent (not bivalent) booster doses, and residents who have received a bivalent booster dose. A primary vaccine series is complete after an individual has received all intended doses of the initial series. There are one, two, and three dose primary vaccine series. B. HOW THE DATASET IS CREATED Information on doses administered to those who live in San Francisco is from the California Immunization Registry (CAIR2), run by the California Department of Public Health (CDPH). The information on individuals’ city of residence, age, race, and ethnicity are also recorded in CAIR and are self-reported at the time of vaccine administration. In order to estimate the percent of San Franciscans vaccinated, we provide the 2016-2020 American Community Survey (ACS) population estimates for each demographic group. C. UPDATE PROCESS Updated daily via automated process D. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Before analysis, you must filter the dataset to the desired stratification of data using the "overall_segment" column. For example, filtering "overall_segment" to "All SF Residents by Age Bracket, Administered by All Providers" will filter the data to residents whose vaccinations were administered by any provider. You can then further segment the data and calculate percentages by Age Brackets. If you filter "overall_segment" to "All SF Residents by Race/Ethnicity, Administered by DPH Only", you will see the race/ethnicity breakdown for residents who received vaccinations from the San Francisco Department of Public Health (SFDPH). If you filter "overall_segment" to "All SF Residents by Age Group, Administered by All Providers" you will see vaccination counts of various age eligibility groups that were administered by any provider. To count the number of individuals vaccinated (with any primary series dose), use the "total_recipients" column. To count the number of individuals who have completed their primary vaccine series, use the "total_series_completed" column. To count the number of primary series doses administered (1st, 2nd, 3rd, or single doses), use the "total_primary_series_doses" column. To count the number of individuals who received one or two monovalent (not bivalent) booster doses, use the "total_booster_recipients" and "total_2nd_booster_recipients" columns. To count the number of individuals who received their first bivalent booster dose, use the "total_bivalent_booster_recipients" column. To count the number of monovalent (not including bivalent) or bivalent booster doses administered, use the "total_booster_doses" or "total_bivalent_booster_doses" columns. E. ARCHIVED DATA A previous version of this dataset was archived on 10/27/2022. For historical purposes, you can access the archived dataset at the following link: ARCHIVED: COVID-19 Vaccine Doses Given to San Franciscans by Demographics F. CHA

The US Consumer Household Database — Weekly Refreshed is AmeriList’s premier consumer dataset, built for marketers, agencies, and enterprises that demand accurate, scalable, and timely U.S. consumer data. Covering over 200 million households nationwide and enriched with 200+ lifestyle, demographic, and behavioral attributes, this file is one of the most complete and frequently updated consumer databases available today.

Why Choose This Database?

Today’s marketing success depends on reaching the right audience at the right time. With this dataset, you gain: - Nationwide coverage of U.S. households (≈95%). - Unmatched attribute depth including age, income, marital status, homeownership, and lifestyle interests. - Freshness you can trust with weekly updates to keep your campaigns aligned with real-world consumer changes. - Multi-channel readiness with delivery via CSV, API, SFTP, or cloud integrations (AWS, GCP, Azure).

Key Features - 200M+ U.S. households for broad reach. - 200+ attributes spanning demographics, lifestyle, purchase signals, and household composition. - Household-level granularity with linkable fields for segmentation and modeling. - Evaluation samples under NDA to test match rates and validate quality.

Use Cases This dataset powers a wide range of data-driven marketing strategies:

• Direct Mail Lists: Reach targeted households with personalized campaigns.
• CRM Enrichment: Append missing consumer attributes to strengthen customer records.
• Audience Segmentation: Build granular segments for more relevant messaging.
• Look-Alike Modeling: Expand your audience with predictive targeting.
• Digital Marketing: Activate high-value segments across social, programmatic, and CTV campaigns.

Industries That Benefit

• Retail & E-commerce: Personalize offers for higher conversions.
• Financial Services & Insurance: Target households by income, homeownership, or life stage.
• Healthcare & Wellness: Engage consumers with relevant health and lifestyle attributes.
• Automotive: Reach in-market households for new and used vehicles.
• Real Estate & Home Services: Connect with homeowners, renters, and movers.

Licensing & Access

The US Consumer Household Database is offered via 12-month subscription, with continuous weekly updates included. Evaluation samples are available under NDA. Flexible licensing models ensure it fits enterprises of all sizes.

Why AmeriList? For over 20 years, AmeriList has been a trusted leader in direct marketing data solutions. Our expertise in consumer databases, mailing lists, and CRM enrichment ensures not only the accuracy of the data but also the strategic value it delivers. With a focus on quality, compliance, and ROI, AmeriList helps brands and agencies unlock the full potential of consumer marketing.

Living Identity™ LATAM delivers 379M verified identity and lifestyle profiles across Brazil, Mexico, and Ecuador. This dataset combines core identity fields with geo-coded behavioral, demographic, and affluence signals — enabling precise audience analytics, segmentation, and KYC compliance. Designed for agencies, retailers, and financial institutions expanding across LATAM, the data is privacy-first, updated monthly, and securely hosted on-premise.

➤ Optimized For: ・Strategic marketing and audience segmentation ・Real-time KYC and identity verification ・Location-based targeting and behavioral modeling ・Market expansion planning in LATAM ・Predictive analytics using lifestyle and mobility signals

➤ Designed For: Marketing & Media Agencies Target LATAM audiences with data-driven precision using lifestyle, mobility, and demographic overlays.

Retailers & E-Commerce Platforms Launch smarter campaigns and geospatial expansion using verified identity + behavior data.

Financial Institutions & Fintechs Enable digital onboarding, KYC, and enrichment for emerging LATAM markets.

Analytics & AI Teams Train segmentation and targeting models with consumer-level identity and lifestyle attributes.

Audience Intelligence & Research Firms Run advanced modeling using behavioral segmentation across key LATAM demographics.

➤ Key Highlights: ・379M verified profiles across Brazil, Mexico, and Ecuador ・Includes ID, contact info, mobility, affluence, and lifestyle attributes ・Geo-coded and updated monthly ・Hosted on-premise, fully compliant with GDPR, LGPD, and PDPA ・Ideal for KYC, marketing, segmentation, and consumer intelligence

Delivered by 1datapipe®, the global leader in structured identity and lifestyle intelligence. Pricing and additional samples available upon request.

This first-party, anonymized mobile app usage dataset provides session-level behavioral intelligence across millions of devices in the APAC region. Designed for AdTech and MarTech applications, it delivers deep insights into how users interact with apps, their install and engagement behavior, device characteristics, and location patterns — all refreshed daily and privacy-compliant.

Core Features • MAID-based behavioral dataset with detailed app install, session, and engagement insights. • Location intelligence (country, region, city-level) for geo-based targeting. • Device intelligence (model, OS, carrier, user agent) for premium vs budget segmentation. • Freshness: Daily refreshed, session-level data. • Consent-first data collection, anonymized and compliant with GDPR/CCPA.

🎯 Key Use Cases 1. Precision Audience Building • Build custom segments based on real app usage and session frequency. • Example: “Users with 20+ sessions on Swiggy, Zomato & Blinkit in metro cities.” • Identify cohorts like brand switchers, category enthusiasts, or premium buyers. 2. Media Planning & Reach Forecasting • Estimate addressable audience size per app or category. • Cross-app overlap analysis (e.g., “60% of Hotstar users also have Prime Video”). • City or region-level reach availability for campaign planning. 3. Competitive Intelligence • Track competitor app adoption and engagement over time. • Measure user migration and churn trends between brands. • Generate market share insights based on install base. 4. Campaign Optimization • Build lookalike audiences from high-value converters. • Enable retargeting based on recency and frequency of app usage. • Exclude audiences already using a client’s app. 5. Creative Optimization • Analyze language preferences, device segments, and time-of-day usage to localize creatives and optimize ad delivery windows.

🏆 Competitive Advantages • Broader visibility than walled gardens like Meta or Google. • Richer insights than survey or panel data — 110M+ users vs. 100K samples. • Pre-install intent signals not captured by MMPs. • Real-time, session-level granularity unavailable in aggregator datasets.

🌍 Industries Served • Advertising & Media Agencies • DSPs & Ad Tech Platforms • Consumer Insights & Analytics Firms • Brand Marketing Teams • Market Research Companies

Pairwise comparisons of age groups using chi-square statistics.

Standardized synonymous and non-synonymous substitution counts across genes in different groups.

Kolmogorov-Smirnov test results for temporal distribution.

🛒 E-Commerce Customer Behavior and Sales Dataset 📊 Dataset Overview This comprehensive dataset contains 5,000 e-commerce transactions from a Turkish online retail platform, spanning from January 2023 to March 2024. The dataset provides detailed insights into customer demographics, purchasing behavior, product preferences, and engagement metrics.

🎯 Use Cases This dataset is perfect for:

Customer Segmentation Analysis: Identify distinct customer groups based on behavior Sales Forecasting: Predict future sales trends and patterns Recommendation Systems: Build product recommendation engines Customer Lifetime Value (CLV) Prediction: Estimate customer value Churn Analysis: Identify customers at risk of leaving Marketing Campaign Optimization: Target customers effectively Price Optimization: Analyze price sensitivity across categories Delivery Performance Analysis: Optimize logistics and shipping 📁 Dataset Structure The dataset contains 18 columns with the following features:

Order Information Order_ID: Unique identifier for each order (ORD_XXXXXX format) Date: Transaction date (2023-01-01 to 2024-03-26) Customer Demographics Customer_ID: Unique customer identifier (CUST_XXXXX format) Age: Customer age (18-75 years) Gender: Customer gender (Male, Female, Other) City: Customer city (10 major Turkish cities) Product Information Product_Category: 8 categories (Electronics, Fashion, Home & Garden, Sports, Books, Beauty, Toys, Food) Unit_Price: Price per unit (in TRY/Turkish Lira) Quantity: Number of units purchased (1-5) Transaction Details Discount_Amount: Discount applied (if any) Total_Amount: Final transaction amount after discount Payment_Method: Payment method used (5 types) Customer Behavior Metrics Device_Type: Device used for purchase (Mobile, Desktop, Tablet) Session_Duration_Minutes: Time spent on website (1-120 minutes) Pages_Viewed: Number of pages viewed during session (1-50) Is_Returning_Customer: Whether customer has purchased before (True/False) Post-Purchase Metrics Delivery_Time_Days: Delivery duration (1-30 days) Customer_Rating: Customer satisfaction rating (1-5 stars) 📈 Key Statistics Total Records: 5,000 transactions Date Range: January 2023 - March 2024 (15 months) Average Transaction Value: ~450 TRY Customer Satisfaction: 3.9/5.0 average rating Returning Customer Rate: 60% Mobile Usage: 55% of transactions 🔍 Data Quality ✅ No missing values ✅ Consistent formatting across all fields ✅ Realistic data distributions ✅ Proper data types for all columns ✅ Logical relationships between features 💡 Sample Analysis Ideas Customer Segmentation with K-Means Clustering

Segment customers based on spending, frequency, and recency Sales Trend Analysis

Identify seasonal patterns and peak shopping periods Product Category Performance

Compare revenue, ratings, and return rates across categories Device-Based Behavior Analysis

Understand how device choice affects purchasing patterns Predictive Modeling

Build models to predict customer ratings or purchase amounts City-Level Market Analysis

Compare market performance across different cities 🛠️ Technical Details File Format: CSV (Comma-Separated Values) Encoding: UTF-8 File Size: ~500 KB Delimiter: Comma (,) 📚 Column Descriptions Column Name Data Type Description Example Order_ID String Unique order identifier ORD_001337 Customer_ID String Unique customer identifier CUST_01337 Date DateTime Transaction date 2023-06-15 Age Integer Customer age 35 Gender String Customer gender Female City String Customer city Istanbul Product_Category String Product category Electronics Unit_Price Float Price per unit 1299.99 Quantity Integer Units purchased 2 Discount_Amount Float Discount applied 129.99 Total_Amount Float Final amount paid 2469.99 Payment_Method String Payment method Credit Card Device_Type String Device used Mobile Session_Duration_Minutes Integer Session time 15 Pages_Viewed Integer Pages viewed 8 Is_Returning_Customer Boolean Returning customer True Delivery_Time_Days Integer Delivery duration 3 Customer_Rating Integer Satisfaction rating 5 🎓 Learning Outcomes By working with this dataset, you can learn:

Data cleaning and preprocessing techniques Exploratory Data Analysis (EDA) with Python/R Statistical analysis and hypothesis testing Machine learning model development Data visualization best practices Business intelligence and reporting 📝 Citation If you use this dataset in your research or project, please cite:

E-Commerce Customer Behavior and Sales Dataset (2024) Turkish Online Retail Platform Data (2023-2024) Available on Kaggle ⚖️ License This dataset is released under the CC0: Public Domain license. You are free to use it for any purpose.

🤝 Contribution Found any issues or have suggestions? Feel free to provide feedback!

📞 Contact For questions or collaborations, please reach out through Kaggle.

Happy Analyzing! 🚀

Keywords: e-c...

As of 10/27/2022, this dataset will no longer update. To continue to access updated vaccination metrics given to SF residents, including newly added bivalent boosters, please navigate to the following page: COVID-19 Vaccinations Given to SF Residents by Demographics.

A. SUMMARY This dataset represents doses of COVID-19 vaccine administered in California to residents of San Francisco. All vaccines given to people who live in San Francisco are included, no matter where the vaccination took place (the vaccine may have been administered in San Francisco or outside of San Francisco). The data are broken down by multiple demographic stratifications.

B. HOW THE DATASET IS CREATED Information on doses administered to those who live in San Francisco is from the California Immunization Registry (CAIR), run by the California Department of Public Health (CDPH). The information on individuals’ city of residence, age, race, and ethnicity are also recorded in CAIR and are self-reported at the time of vaccine administration.

In order to estimate the percent of San Franciscans vaccinated, we provide the same 2019 five-year American Community Survey population estimates that are used in our public dashboards.

C. UPDATE PROCESS Updated daily via automated process

D. HOW TO USE THIS DATASET Before analysis, you must filter the dataset to the desired stratification of data using the OVERALL_SEGMENT column.

For example, filtering OVERALL_SEGMENT to "Ages 5+ by Age Bracket, Administered by All Providers" will filter the data to residents 5 and over whose vaccinations were administered by any provider. You can then further segment the data and calculate percentages by Age Brackets.

If you filter OVERALL_SEGMENT to "Ages 65+ by Race/Ethnicity, Administered by DPH Only", you will see the race/ethnicity breakdown for residents aged 65+ who received vaccinations from San Francisco’s Department of Public Health (DPH).

This dataset provides comprehensive customer data suitable for segmentation analysis. It includes anonymized demographic, transactional, and behavioral attributes, allowing for detailed exploration of customer segments. Leveraging this dataset, marketers, data scientists, and business analysts can uncover valuable insights to optimize targeted marketing strategies and enhance customer engagement. Whether you're looking to understand customer behavior or improve campaign effectiveness, this dataset offers a rich resource for actionable insights and informed decision-making.

Key Features:

Anonymized demographic, transactional, and behavioral data. Suitable for customer segmentation analysis. Opportunities to optimize targeted marketing strategies. Valuable insights for improving campaign effectiveness. Ideal for marketers, data scientists, and business analysts.

Usage Examples:

Segmenting customers based on demographic attributes. Analyzing purchase behavior to identify high-value customer segments. Optimizing marketing campaigns for targeted engagement. Understanding customer preferences and tailoring product offerings accordingly. Evaluating the effectiveness of marketing strategies and iterating for improvement. Explore this dataset to unlock actionable insights and drive success in your marketing initiatives!

Background and objectivesThe scarcity of data for training deep learning models in pediatrics has prompted questions about the feasibility of employing CNNs trained with adult images for pediatric populations. In this work, a pneumonia classification CNN was used as an exploratory example to showcase the adaptability and efficacy of such models in pediatric healthcare settings despite the inherent data constraints.MethodsTo develop a curated training dataset with reduced biases, 46,947 chest X-ray images from various adult datasets were meticulously selected. Two preprocessing approaches were tried to assess the impact of thoracic segmentation on model attention outside the thoracic area. Evaluation of our approach was carried out on a dataset containing 5,856 chest X-rays of children from 1 to 5 years old.ResultsAn analysis of attention maps indicated that networks trained with thorax segmentation placed less attention on regions outside the thorax, thus eliminating potential bias. The ensuing network exhibited impressive performance when evaluated on an adult dataset, achieving a pneumonia discrimination AUC of 0.95. When tested on a pediatric dataset, the pneumonia discrimination AUC reached 0.82.ConclusionsThe results of this study show that adult-trained CNNs can be effectively applied to pediatric populations. This could potentially shift focus towards validating adult models over pediatric population instead of training new CNNs with limited pediatric data. To ensure the generalizability of deep learning models, it is important to implement techniques aimed at minimizing biases, such as image segmentation or low-quality image exclusion.

Unlock powerful B2B marketing with the AmeriList U.S. Business Database, your gateway to connecting with over 20 million public and private companies across the U.S. and Canada.

Whether your goal is lead generation, account-based marketing, email campaigns, sales outreach, or market analysis, this database gives you the depth, accuracy, and segmentation you need to reach key decision makers efficiently.

AmeriList is a proven leader in direct marketing and data services since 2002. We combine multiple data sources, rigorous verification processes, and ongoing hygiene services to deliver one of the most dependable B2B data assets in the market.

Key Features & Data Coverage:

1. Extensive Coverage & Business Universe
2. Access to over 20 million U.S. and Canadian business profiles (public and private)
3. Annual telephone verification ensures current, accurate contact information

4. Aggregated from multiple trusted sources: Yellow Pages, white pages, SEC filings, government records, trade publications, etc.

5. Rich Firmographic & Demographic Selects For precise targeting, you can filter and segment by:

6. SIC & NAICS codes (industry classification)

7. Business size: employee count, sales volume, year established

8. Executive names, titles, decision makers

9. Public vs private status, location, executive roles, and more

10. Data Quality & Hygiene Services Your success hinges on clean data. AmeriList offers:

11. List hygiene services including merge/purge, data suppression, deceased handling, DMA suppression, etc.

12. Address correction & postal accuracy via NCOA, LACS, DSF2, CASS, ZIP+4 processing

13. Data enhancement services to append missing emails, phone numbers, firmographics, and demographic data

14. Specialty & Vertical Lists: In addition to the main business database, you can access more than 65,000 specialty mailing lists (e.g. auto owners, executives on the go, brides-to-be, healthcare professionals, etc.).

15. Some niche examples: dentists, lawyers, real estate professionals, contractors, home-based businesses (SOHO), credit-seeking businesses, start-ups, and more.

16. SOHO (Home-based Businesses) database: reach entrepreneurs running their business from home with selective targeting on industry, revenue, email, etc.

17. Booming Start-Ups database: newly formed, rapidly growing businesses that may be highly responsive to service providers.

18. Credit-Seeking Businesses list: businesses actively seeking financing, great for loan, leasing, or financial service vendors.

19. Channel & Delivery Options:

20. Receive your data in flexible formats (electronic lists, print, mail house fulfillment)

21. Ready for postal, telemarketing, or email campaigns depending on your strategy

22. Turnaround and fulfillment options are competitive, with support from AmeriList’s list services team

Benefits & Use Cases:

✔ Boost Sales & Lead Generation: Use the database to identify potential customers in your target verticals, then build campaigns to reach them via email, direct mail, phone, or multi-channel strategies.

✔ Precision Targeting & Better ROI: Eliminate guesswork, segment by industry, revenue, business size, location, executive role, and more. Your marketing budgets go further with high-conversion prospects.

✔ Decision-Maker Access: Reach business owners, executives, and purchasing managers directly with accurate contact details that cut through gatekeepers

✔ Market Expansion & Competitive Intelligence: Find new markets or underserved geographies. Analyze competitive landscapes and business trends across industries.

✔ List Maintenance & Data Refresh: Ensure that your internal CRM or lead lists stay clean, up-to-date, and enriched, reducing bounce rates, undeliverables, and wasted outreach.

✔ Specialized Campaigns & Niche Targeting: Tap into industry-specific, interest-based, or buyer-behavior lists (e.g. credit-seeking businesses, start-ups, niche professionals) to tailor outreach campaigns.

Why Choose AmeriList:

• Quality-backed accuracy, every business record is phone-verified annually for freshness and deliverability.
• Wide multisource aggregation, combinations of public records, private data providers, trade publications, etc., offering superior coverage and depth.
• Comprehensive hygiene and data enrichment included options that many providers sell à la carte.
• Large specialty and vertical list portfolio, 65,000+ specialty lists to pair or layer with your base business data.
• Longstanding reputation, trusted by enterprises, agencies, and direct marketers since 2002.
• Flexible delivery and support, choose formats and fulfillment options that match your marketing infrastructure.

The AmeriList U.S. Business Database is the ultimate resource for marketers, sales teams, and agencies looking to connect with verified companies and decision makers across every industry. With over 20 million U.S. businesses, rich firmographics, executive contacts, and advanced segmentation options, this B2B database ...

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.