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.

Tapestry segment descriptions can be found here..http://www.esri.com/library/brochures/pdfs/tapestry-segmentation.pdf For more than 30 years, companies, agencies, and organizations have used segmentation to divide and group their consumer markets to more precisely target their best customers and prospects. This targeting method is superior to using “scattershot” methods that might attract these preferred groups. Segmentation explains customer diversity, simplifies marketing campaigns, describes lifestyle and lifestage, and incorporates a wide range of data. Segmentation systems operate on the theory that people with similar tastes, lifestyles, and behaviors seek others with the same tastes—“like seeks like.” These behaviors can be measured, predicted, and targeted. Esri’s Tapestry Segmentation system combines the “who” of lifestyle demography with the “where” of local neighborhood geography to create a model of various lifestyle classifications or segments of actual neighborhoods with addresses—distinct behavioral market segments. The tapestry segmentation is almost comical in the sense that it trys to describe such small details of individuals daily lives just by analyzing the data provided on your CENSUS form. These segements are not only ideal for marketing and targeting lifestyles within a geographic location, but they are fun to read. Take the time to find out which segment you live in!

ABSTRACT Objective: To investigate socioeconomic and demographic differences regarding population aging in municipalities of the state of Pará, Brazil. Method: Ecological study with secondary demographic, socioeconomic and health data from the 144 municipalities of the state of Pará, Brazil. Data were treated with segmentation analysis, the Mann-Whitney U test and logistic regression models, with a significance level of p ≤ 0.05. Results: Segmentation analysis provided a single variable to describe aging in the municipalities of Pará and originated two clusters, the high and low aging rate ones, with 104 (72.22%) and 40 (27.78%) municipalities in each, respectively. The fitted model revealed an association between aging and per capita income (p = 0.021), vulnerability to poverty (p = 0.003), rich to poor ratio (p = 0.012) and density of people (p = 0.019). Conclusion: There is heterogeneity in the population aging among the municipalities of Pará, mainly regarding socioeconomic conditions and number of people living in the municipalities.

This dataset contains 500,000 records of customer purchase behavior for an e-commerce platform. It can be used for predictive modeling tasks such as

• Predicting whether a customer will make a purchase (PurchaseStatus)
• Customer segmentation and profiling
• Analyzing the impact of demographic, behavioral, and engagement factors on purchases
• The dataset is suitable for classification (purchase prediction) and exploratory data analysis (EDA).

Attribute Explanation

1. Age (int): Customer’s age in years. Range: 15–81.
2. AnnualIncome (float): Customer’s annual income in USD. Range: 11,966 – 204,178.
3. NumberOfPurchases (int): Total purchases made by the customer. Can indicate engagement level.
4. TimeSpentOnWebsite (float): Average time (in minutes) spent on the website per visit.
5. CustomerTenureYears (float): Duration (in years) since the customer joined the platform.
6. LastPurchaseDaysAgo (int): Number of days since the customer’s most recent purchase.
7. Gender (categorical: Male/Female): Customer’s gender.
8. ProductCategory (categorical: Fashion, Electronics, Furniture, Groceries, Sports, etc.): Most frequently purchased product category.
9. PreferredDevice (categorical: Mobile/Desktop/Tablet): Device most often used by the customer.
10. Region (categorical: North, South, East, West): Geographic region of the customer.
11. ReferralSource (categorical: Organic, Paid Ads, Referral, Social Media, Email): How the customer discovered the platform.
12. CustomerSegment (categorical: Regular, Premium, VIP): Business-defined customer classification.
13. LoyaltyProgram (binary: 0/1): Whether the customer is enrolled in a loyalty program.
14. DiscountsAvailed (int): Number of discounts/coupons redeemed by the customer.
15. SessionCount (int): Number of sessions/visits recorded for the customer.
16. CustomerSatisfaction (int: 1–5): Customer satisfaction rating (1 = very dissatisfied, 5 = very satisfied).
17. PurchaseStatus (target variable, binary: 0/1): Purchase successful or not

1) Data Introduction • The Consumer Behavior and Shopping Habits Dataset is a tabular collection of customer demographics, purchase history, product preferences, shopping frequency, and online and offline purchasing behavior.

2) Data Utilization (1) Consumer Behavior and Shopping Habits Dataset has characteristics that: • Each row contains detailed consumer and transaction information such as customer ID, age, gender, purchased goods and categories, purchase amount, region, product attributes (size, color, season), review rating, subscription status, delivery method, discount/promotion usage, payment method, purchase frequency, etc. • Data is organized to cover a variety of variables and purchasing patterns to help segment customers, establish marketing strategies, analyze product preferences, and more. (2) Consumer Behavior and Shopping Habits Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze demographics and purchasing patterns to define different customer groups and use them to develop customized marketing strategies. • Product and service improvement: Based on purchase history, review ratings, discount/promotional responses, etc., it can be applied to product and service improvements such as identifying popular products, managing inventory, and analyzing promotion effects.

Overview The Mall Customers Dataset provides data on 200 individuals who visit a mall, including demographic information, annual income, and spending habits. This dataset is useful for exploratory data analysis, customer segmentation, and clustering tasks (e.g., K-means clustering).

Dataset Summary - Rows: 200 - Columns: 5 - No missing values

Columns Description - CustomerID: A unique identifier for each customer (integer). - Genre: The gender of the customer (Male/Female). - Age: The age of the customer (integer). - Annual Income (k$): Annual income of the customer in thousands of dollars (integer). - Spending Score (1-100): A score assigned by the mall based on customer behavior and spending patterns (integer).

Potential Use Cases - Customer Segmentation: Group customers based on their income and spending habits. - Behavioral Analysis: Explore how factors like gender, age, and income influence spending scores. - Clustering: Apply algorithms such as K-means to identify clusters of customers with similar characteristics. - Targeted Marketing Campaigns: Use the insights to create personalized promotions for different customer segments.

Exploratory Questions - What is the relationship between annual income and spending score? - Does gender or age influence spending behavior? - Which customers have high spending scores but low incomes, or vice versa?

Suggested Analysis Techniques - EDA: Visualize income distribution, age groups, and spending patterns. - Clustering Algorithms: Use K-means or hierarchical clustering for segmentation. - Correlation Analysis: Investigate correlations between age, income, and spending score.

Licensing & Citation - License: Open for public use, suitable for educational and research purposes. - Citation: If you use this dataset in your project or research, please reference this dataset appropriately.

This dataset provides a great starting point for hands-on learning in customer analytics, marketing strategy, and machine learning. Perfect for beginners and data enthusiasts looking to explore clustering or segmentation techniques!

Overview This product, with over 100 actual and modelled variables, is designed to help you gain better insight into your customers and prospects. The Enhance dataset provides users with a set of predictive and descriptive attributes which support more informed, targeted and relevant marketing to consumers.

What is it? Enhance Core is an individual level data set, containing self-declared, freely given socio-demographic data on over 90m individuals. The data is obtained from a range of sources, including; Satisfaction & Lifestyle surveys, Website Registrations, Newsletter & Service subscriptions, Offers & Competition websites and public Social Media feeds.

Use cases -Using key information, appended from Enhance, to create personalised messaging for direct mail & digital marketing campaigns - Using Profiling & Predictive messaging to identify important cohorts within the customer base, and those that can be “Forgotten” - Seeing how the current customer base compares to the UK base, so you can identify which potential audiences you are missing and also those that your business excels in. - Segment your customers into distinct groups so that you can offer them the right products through the most appropriate channels

Additional Insights Enhance Core, Property & Geo (Individual, Property & Postcode level data) can all be used modularly, allowing you to understand the full picture of your customer base, considering not only their individual variance but also where they live & those around them.

Context:

This dataset provides detailed insights into consumer behaviour and shopping patterns across various demographics, locations, and product categories. It contains 3,900 customer records with 18 attributes that describe purchase details, shopping habits, and preferences.

The dataset includes information such as:

• Customer demographics (age, gender, location)
• Product details (item purchased, category, size, color, season)
• Purchase information (amount spent in USD, payment method, shipping type)
• Shopping behaviour (frequency of purchases, previous purchases, subscription status, discount usage, promo codes)
• Customer feedback (review ratings)

This dataset can be used to explore consumer decision-making and market trends, including:

• How age, gender, or location influence shopping preferences.
• The relationship between discounts, promo codes, and purchase amounts.
• Which product categories and colors are most popular in different seasons.
• Patterns in payment method usage (e.g., PayPal vs. Credit Card).
• How subscription and loyalty behaviours affect shopping frequency.

Researchers, data analysts, and students can use this dataset to practice customer segmentation, predictive modelling, recommendation systems, and market basket analysis. It also serves as a valuable resource for learning techniques in exploratory data analysis (EDA), machine learning, and business analytics.

Dataset Glossory(Column wise)

Customer ID: A unique identifier assigned to each customer. It helps distinguish one shopper’s data from another without revealing their personal identity.

Age: The age of the customer in years, which can provide insights into generational shopping habits and how preferences differ across age groups.

Gender: Indicates whether the customer is male or female, allowing analysis of gender-based buying trends and preferences in product categories.

Item Purchased: The specific product that the customer bought, giving a direct view of consumer demand and popular items in the dataset.

Category: The broader classification of the purchased item, such as clothing or footwear, which helps in grouping products and understanding category-level trends.

Purchase Amount (USD): The total money spent on the purchase in U.S. dollars, which reflects customer spending power and the value of each transaction.

Location: The state or region where the customer resides, useful for identifying geographical shopping patterns and regional differences in consumer behaviour.

Size: The size of the purchased item (e.g., S, M, L), which helps reveal customer preferences in apparel and how sizing impacts sales.

Color: The chosen color of the purchased item, offering insights into which colors are more appealing to consumers during different seasons or product categories.

Season: The season (Winter, Spring, etc.) in which the purchase was made, showing how customer demand changes across seasonal trends.

Review Rating: A numerical score reflecting the customer’s satisfaction with the product, valuable for measuring quality perception and post-purchase behaviour.

Subscription Status: Indicates whether the customer has an active subscription with the store, which may influence loyalty, discounts, and purchase frequency.

Shipping Type: The delivery option chosen by the customer, such as free shipping or express, which highlights convenience preferences and urgency of purchase.

Discount Applied: Shows whether a discount was used during the purchase, allowing analysis of how discounts affect buying decisions and sales growth.

Promo Code Used: Specifies if the customer used a promotional code, useful for understanding the impact of marketing strategies on purchase behaviour.

Previous Purchases: The number of items the customer has bought before, reflecting their shopping history and overall loyalty to the store.

Payment Method: The mode of payment used (Credit Card, PayPal, etc.), which sheds light on financial behaviour and preferred transaction methods.

Frequency of Purchases: Indicates how often the customer engages in purchasing activities, a critical metric for assessing customer loyalty and lifetime value.

Acknowledgment

Special thanks to Sir Sourav Banerjee Associate Data Scientist at CogniTensor

Kolkata, West Bengal, India

Demographic and clinical characteristics of the younger cohort. M:F = Male:Female; CIS = Clinically Isolated Syndrome; RMS = Relapsing-Remitting Multiple Sclerosis; EDSS = Expanded Disability Status Scale.

We'll customize a Uber Eats dataset to align with your unique requirements, incorporating data on restaurant types, menu items, pricing, delivery times, customer ratings, demographic insights, and other relevant metrics.

Leverage our Uber Eats datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and delivery trends, facilitating refined menu offerings and optimized delivery strategies. Tailor your access to the complete dataset or specific subsets according to your business needs.

Popular use cases include optimizing menu offerings based on consumer insights, refining marketing strategies through targeted customer segmentation, and identifying and predicting trends to maintain a competitive edge in the food delivery market.

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.

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

** Inputs related to Analysis for additional reference:** 1. Why do we need customer Segmentation? As every customer is unique and can be targeted in different ways. The Customer segmentation plays an important role in this case. The segmentation helps to understand profiles of customers and can be helpful in defining cross sell/upsell/activation/acquisition strategies. 2. What is RFM Segmentation? RFM Segmentation is an acronym of recency, frequency and monetary based segmentation. Recency is about when the last order of a customer. It means the number of days since a customer made the last purchase. If it’s a case for a website or an app, this could be interpreted as the last visit day or the last login time. Frequency is about the number of purchases in a given period. It could be 3 months, 6 months or 1 year. So we can understand this value as for how often or how many customers used the product of a company. The bigger the value is, the more engaged the customers are. Alternatively We can define, average duration between two transactions Monetary is the total amount of money a customer spent in that given period. Therefore big spenders will be differentiated with other customers such as MVP or VIP. 3. What is LTV and How to define it? In the current world, almost every retailer promotes its subscription and this is further used to understand the customer lifetime. Retailer can manage these customers in better manner if they know which customer is high life time value. Customer lifetime value (LTV) can also be defined as the monetary value of a customer relationship, based on the present value of the projected future cash flows from the customer relationship. Customer lifetime value is an important concept in that it encourages firms to shift their focus from quarterly profits to the long-term health of their customer relationships. Customer lifetime value is an important metric because it represents an upper limit on spending to acquire new customers. For this reason it is an important element in calculating payback of advertising spent in marketing mix modelling. 4. Why do need to predict Customer Lifetime Value? The LTV is an important building block in campaign design and marketing mix management. Although targeting models can help to identify the right customers to be targeted, LTV analysis can help to quantify the expected outcome of targeting in terms of revenues and profits. The LTV is also important because other major metrics and decision thresholds can be derived from it. For example, the LTV is naturally an upper limit on the spending to acquire a customer, and the sum of the LTVs for all of the customers of a brand, known as the customer equity, is a major metric forbusiness valuations. Similarly to many other problems of marketing analytics and algorithmic marketing, LTV modelling can be approached from descriptive, predictive, and prescriptive perspectives. 5. How Next Purchase Day helps to Retailers? Our objective is to analyse when our customer will purchase products in the future so for such customers we can build strategy and can come up with strategies and marketing campaigns accordingly. a. Group-1: Customers who will purchase in more than 60 days b. Group-2: Customers who will purchase in 30-60 days c. Group-3: Customers who will purchase in 0-30 days 6. What is Cohort Analysis? How it will be helpful? A cohort is a group of users who share a common characteristic that is identified in this report by an Analytics dimension. For example, all users with the same Acquisition Date belong to the same cohort. The Cohort Analysis report lets you isolate and analyze cohort behaviour. Cohort analysis in e-commerce means to monitor your customers’ behaviour based on common traits they share – the first product they bought, when they became customers, etc. - - to find patterns and tailor marketing activities for the group.

Transaction data has been provided for the period of 1st Jan 2019 to 31st Dec 2019. The below data sets have been provided. Online_Sales.csv: This file contains actual orders data (point of Sales data) at transaction level with below variables. CustomerID: Customer unique ID Transaction_ID: Transaction Unique ID Transaction_Date: Date of Transaction Product_SKU: SKU ID – Unique Id for product Product_Description: Product Description Product_Cateogry: Product Category Quantity: Number of items ordered Avg_Price: Price per one quantity Delivery_Charges: Charges for delivery Coupon_Status: Any discount coupon applied Customers_Data.csv: This file contains customer’s demographics. CustomerID: Customer Unique ID Gender: Gender of customer Location: Location of Customer Tenure_Months: Tenure in Months Discount_Coupon.csv: Discount coupons have been given for different categories in different months Month: Discount coupon applied in that month Product_Category: Product categor...

📌 About the Dataset This dataset contains 50,000 customer transactions from Walmart, capturing essential details about consumer shopping behavior. It includes demographic information, product categories, purchase amounts, discounts, and ratings, making it useful for data analysis, customer segmentation, and sales forecasting.

📊 Dataset Features Customer_ID – Unique identifier for each customer. Age – Age of the customer. Gender – Gender of the customer (Male/Female/Other). City – City where the purchase was made. Category – Product category (e.g., Electronics, Clothing, Groceries). Product_Name – Name of the purchased product. Purchase_Date – Date of purchase. Purchase_Amount – Total amount spent on the purchase. Payment_Method – Mode of payment (Credit Card, Cash, Digital Wallet, etc.). Discount_Applied – Whether a discount was applied (Yes/No). Rating – Customer rating of the purchase (1-5). Repeat_Customer – Whether the customer has purchased before (Yes/No). 🔍 Potential Use Cases ✅ Customer Segmentation – Grouping customers based on age, gender, and purchase patterns. ✅ Market Basket Analysis – Identifying frequently purchased products together. ✅ Sales Forecasting – Predicting future sales trends using time-series analysis. ✅ Customer Loyalty Analysis – Understanding repeat customer behavior. ✅ Discount Impact Analysis – Evaluating how discounts influence purchasing decisions. ✅ Product Performance Evaluation – Analyzing ratings and sales of different products.

Ecosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, Google Earth, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to detect species and track their population dynamics remains under-explored. We introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving two major Mediterranean invasive plant taxa with contrasting growth forms. This dataset contains the step-by-step protocol to perform retrospective image analysis using Google Earth Imagery, including writen protocols, videotutorials and the data. A ReadMe is found in the folder explaining all folder's contents, whereas a WatchMe has been recorded to perform an analogous function in the Youtube playlist including all videotutorials: https://www.youtube.com/playlist?list=PL_LKE-yTi9kBXfw_qDdJCQ3Sxu2fjGvDD Our pipeline opens new avenues for cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene.The repository contains 4 folders (Data, Code, Protocols and Videos), acompaigned by a ReadMe.txt file with further details about the contents.

Face Detection - Object Detection & Face Recognition Dataset

The dataset is created on the basis of Selfies and ID Dataset

The dataset is a collection of images (selfies) of people and bounding box labeling for their faces. It has been specifically curated for face detection and face recognition tasks. The dataset encompasses diverse demographics, age, ethnicities, and genders.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F01348572e2ae2836f10bc2f2da381009%2FFrame%2050%20(1).png?generation=1699439342545305&alt=media" alt="">

The dataset is a valuable resource for researchers, developers, and organizations working on age prediction and face recognition to train, evaluate, and fine-tune AI models for real-world applications. It can be applied in various domains like psychology, market research, and personalized advertising.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, …, photo_15_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, …, photo_15_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

🧩 This is just an example of the data. Leave a request here to learn more

Dataset structure

• images - contains of original images of people
• labels - includes visualized labeling for the original images
• annotations.xml - contains coordinates of the bbox, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F19e61b2d0780e9db80afe4a0ce879c4b%2Fcarbon.png?generation=1699440100527867&alt=media" alt="">

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, object detection dataset, deep learning datasets, computer vision datset, human images dataset, human faces dataset

The Credit Card Transactions Dataset provides detailed records of credit card transactions, including information about transaction times, amounts, and associated personal and merchant details. This dataset has over 1.85M rows.

How This Dataset Can Be Used:

Fraud Detection : Use machine learning models to identify fraudulent transactions by examining patterns in transaction amounts, locations, and user profiles. Enhancing fraud detection systems becomes feasible by analyzing behavioral patterns.

Customer Segmentation : Segment customers based on spending patterns, location, and demographics. Tailor marketing strategies and personalized offers to these different customer segments for better engagement.

Transaction Classification : Classify transactions into categories such as grocery or entertainment to understand spending behaviors. This helps in improving recommendation systems by identifying transaction categories and preferences.

Geospatial Analysis : Analyze transaction data geographically to map spending patterns and detect regional trends or anomalies based on latitude and longitude.

Predictive Modeling : Build models to forecast future spending behavior using historical transaction data. Predict potential fraudulent activities and financial trends.

Behavioral Analysis : Examine how factors like transaction amount, merchant type, and time influence spending behavior. Study the relationships between user demographics and transaction patterns.

Anomaly Detection : Identify unusual transaction patterns that deviate from normal behavior to detect potential fraud early. Employ anomaly detection techniques to spot outliers and suspicious activities.

Changes in standard deviation AoHU by demographic factors.

The goal of this project is to provide all the materials to the community to resolve the problem of echocardiographic image segmentation and volume estimation from 2D ultrasound sequences (both two and four-chamber views). To this aim, the following solutions were set-up introduction of the largest publicly-available and fully-annotated dataset for 2D echocardiographic assessment (to our knowledge). The CAMUS dataset, containing 2D apical four-chamber and two-chamber view sequences acquired from 500 patients, is made available for download.

Dataset Properties

The overall CAMUS dataset consists of clinical exams from 500 patients, acquired at the University Hospital of St Etienne (France) and included in this study within the regulation set by the local ethical committee of the hospital after full anonymization. The acquisitions were optimized to perform left ventricle ejection fraction measurements. In order to enforce clinical realism, neither prerequisite nor data selection have been performed. Consequently,

• some cases were difficult to trace;

• the dataset involves a wide variability of acquisition settings;

• for some patients, parts of the wall were not visible in the images;

for some cases, the probe orientation recommendation to acquire a rigorous four-chambers view was simply impossible to follow and a five-chambers view was acquired instead. This produced a highly heterogeneous dataset, both in terms of image quality and pathological cases, which is typical of daily clinical practice data.

The dataset has been made available to the community HERE. The dataset comprises : i) a training set of 450 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing set composed of 50 new patients. The raw input images are provided through the raw/mhd file format.

Study population

Half of the dataset population has a left ventricle ejection fraction lower than 45%, thus being considered at pathological risk (beyond the uncertainty of the measurement). Also, 19% of the images have a poor quality (based on the opinion of one expert), indicating that for this subgroup the localization of the left ventricle endocarium and left ventricle epicardium as well as the estimation of clinical indices are not considered clinically accurate and workable. In classical analysis, poor quality images are usually removed from the dataset because of their clinical uselessness. Therefore, those data were not involved in this project during the computation of the different metrics but were used to study their influence as part of the training and validation sets for deep learning techniques.

Involved systems

The full dataset was acquired from GE Vivid E95 ultrasound scanners (GE Vingmed Ultrasound, Horten Norway), with a GE M5S probe (GE Healthcare, US). No additional protocol than the one used in clinical routine was put in place. For each patient, 2D apical four-chamber and two-chamber view sequences were exported from EchoPAC analysis software (GE Vingmed Ultrasound, Horten, Norway). These standard cardiac views were chosen for this study to enable the estimation of left ventricle ejection fraction values based on the Simpson’s biplane method of discs. Each exported sequence corresponds to a set of B-mode images expressed in polar coordinates. The same interpolation procedure was used to express all sequences in Cartesian coordinates with a unique grid resolution, i.e. λ/2 = 0.3 mm along the x-axis (axis parallel to the probe) and λ/4 = 0.15 mm along the z-axis (axis perpendicular to the probe), where λ corresponds to the wavelength of the ultrasound probe. At least one full cardiac cycle was acquired for each patient in each view, allowing manual annotation of cardiac structures at ED and ES.

****This work has published to IEEE TMI journal. You must cite this paper for any use of the CAMUS database.**** - S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al. "Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography" in IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2198-2210, Sept. 2019. doi: 10.1109/TMI.2019.2900516

Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.