Finance17 cols · 201 rows
This dataset provides daily aggregated cash withdrawal and deposit activity for each ATM, including transaction counts, total and average amounts, unique card usage, and a flag for suspicious activity. The data is ideal for analyzing ATM usage patterns, forecasting cash demand, and monitoring for potential fraud across different locations. Detailed location fields enable geographic and branch-level insights.
| location_country | total_withdrawal_count | suspicious_activity_flag |
|---|
| USA | 96 | false |
| Canada | 32 | false |
| USA | 57 | false |
atm_idlocation_idlocation_street_address+11 more
Finance19 cols · 200 rows
This dataset provides comprehensive performance metrics for individual bank branches, including account activity, transaction volumes, and customer feedback scores over specific reporting periods. It enables detailed analysis of operational efficiency, customer satisfaction, and issue resolution, supporting data-driven management decisions and branch benchmarking.
| branch_name | address_country | accounts_opened |
|---|
| Downtown Financial | USA | 400 |
| Bayview Branch | USA | 380 |
| Lakeshore Center | USA | 305 |
branch_idbranch_manageraddress_street+13 more
Other15 cols · 200 rows
This dataset provides anonymized, daily foot traffic counts for physical retail locations, enriched with store details, holiday indicators, weather conditions, and special event annotations. It enables robust sales forecasting, staffing optimization, and marketing campaign impact analysis by correlating footfall with external factors and store attributes.
| store_name | day_of_week | is_holiday |
|---|
| FreshMart Downtown | Friday | false |
| MegaMart Uptown | Saturday | false |
| Boutique Parisienne | Friday | true |
foot_traffic_idstore_idstore_street_address+9 more
Other20 cols · 200 rows
This dataset provides detailed, time-stamped records of local food delivery orders, including customer demographics, restaurant information, ordered items, payment methods, and delivery outcomes. It enables granular analysis of demand patterns, peak ordering times, and customer preferences, supporting operational optimization and targeted marketing strategies.
| customer_gender | restaurant_name | delivery_status |
|---|
| female | Sushi Zen | delivered |
| male | Burger House | delivered |
| prefer_not_to_say | Cafe Paris | pending |
order_idorder_datetimecustomer_id+14 more
Healthcare20 cols · 200 rows
This dataset provides detailed historical sales and inventory records for top pharmacy products, including batch-level expiration and restock information, store locations, and transaction details. It enables precise inventory optimization, demand forecasting, and proactive management of product expirations across multiple pharmacy locations.
| product_name | category | store_name |
|---|
| Ibuprofen 200mg | analgesic | HealthFirst Pharmacy |
| Amoxicillin 500mg | antibiotic | Care Max |
| Vitamin D3 1000IU | supplement | PharmaTrust |
sale_idproduct_idmanufacturer+14 more
Manufacturing9 cols · 200 rows
This dataset provides detailed, shift-level defect counts for each product type and manufacturing line, enabling granular quality control analysis and predictive modeling. With fields for defect categories, production volumes, and calculated defect rates, it supports both operational monitoring and advanced AI-driven analytics to improve manufacturing processes.
| shift | defect_type | defect_rate |
|---|
| Morning | Surface | 0.01 |
| Afternoon | Mechanical | 0 |
| Night | Electrical | 0.12632 |
production_lineproduct_typetotal_units_produced+3 more
Finance18 cols · 200 rows
This dataset provides granular, transaction-level data on digital wallet usage, including top-ups, peer-to-peer transfers, and merchant payments. It features rich contextual information such as user, wallet, merchant, device, and location details, making it ideal for payment analytics, fraud detection, and fintech product development.
| transaction_type | status | merchant_name |
|---|
| top-up | completed | – |
| transfer | pending | – |
| merchant-payment | completed | Tesco Express |
transaction_iduser_idtransaction_datetime+12 more
Finance28 cols · 200 rows
This dataset contains rich, structured information about bank customers, including demographics, account details, product holdings, financial metrics, and segmentation labels. It is ideal for financial institutions seeking to personalize marketing, manage risk, and identify cross-selling opportunities through data-driven customer segmentation and profiling.
| first_name | last_name | account_type |
|---|
| Alex | Jones | checking |
| Meera | Singh | student |
| Riley | Brown | savings |
customer_idgenderdate_of_birth+22 more
E-commerce12 cols · 200 rows
This dataset contains detailed e-commerce product reviews, including review text, star ratings, sentiment labels, and metadata such as helpful votes and verified purchase status. It is ideal for training and evaluating sentiment analysis models, understanding customer feedback, and powering recommendation engines. The rich structure supports both NLP research and practical business applications.
| product_name | review_title | sentiment_label |
|---|
| Wireless Bluetooth Earbuds | Amazing Sound Quality! | positive |
| Stainless Steel Water Bottle | Keeps Drinks Cold For Hours | positive |
| Foldable Laptop Stand | Makes Working Easier | positive |
review_idproduct_iduser_id+6 more
Human Resources17 cols · 110 rows
This dataset provides detailed, standardized employee performance review records, including department, job level, review periods, reviewer information, and multiple scoring dimensions. It enables organizations to analyze workforce performance, identify development needs, and support data-driven promotion and talent management decisions across departments and job levels.
| employee_name | department | reviewer_name |
|---|
| Carlos Mendoza | Engineering | Julia Müller |
| Amina El-Sayed | Marketing | David Song |
| Zhihao Lin | IT | Elena Petrova |
review_idemployee_idjob_level+11 more