datasets/README.md

Demo datasets

This folder contains demo-friendly CSV datasets for testing and showcasing the data agent.

workflow_painpoints_demo.csv

• use case: Analyze delays and errors across steps in a product demo workflow.
• key columns:
  • workflow_id: Identifier for a demo workflow run.
  • step_name: Name of the workflow step (e.g. data_upload, data_cleaning).
  • step_order: Order of the step within the workflow.
  • time_spent_minutes: Time spent on the step.
  • had_error: Boolean flag indicating if an error occurred.
  • pain_point: Short description of the pain point, if any.

cafe_sales.csv

• use case: Explore point-of-sale transaction data for a small cafe.
• key columns:
  • transaction_id: Unique transaction identifier.
  • date: Transaction date.
  • product_category: High-level category (e.g. coffee, food).
  • item_name: Purchased item name.
  • quantity: Number of units sold.
  • unit_price: Price per unit.
  • total_price: Total amount for the line item.

spotify_churn_dataset.csv

• use case: Model user churn for a music streaming service.
• key columns:
  • user_id: Unique user identifier.
  • country: User’s country.
  • subscription_type: Plan type (e.g. free, premium).
  • monthly_listening_hours: Total hours listened in the last month.
  • skips_per_hour: Average track skips per hour.
  • support_tickets_last_90d: Number of support tickets opened in the last 90 days.
  • is_churned: Boolean target indicating if the user churned.

Walmart.csv

• use case: Analyze retail sales patterns across stores and departments.
• key columns:
  • store: Store identifier.
  • dept: Department identifier.
  • date: Week start date.
  • weekly_sales: Weekly sales amount.
  • is_holiday: Flag indicating if the week includes a holiday period.

customer_support_tickets.csv

• use case: Monitor support team workload, SLAs, and customer satisfaction.
• key columns:
  • ticket_id: Unique ticket identifier.
  • created_at: Ticket creation timestamp.
  • channel: Support channel (e.g. email, chat, phone, web).
  • customer_id: Customer identifier.
  • priority: Ticket priority (Low, Medium, High).
  • status: Current status (e.g. Open, Resolved, Escalated, Closed).
  • category: Ticket category (e.g. Billing, Technical Issue, Outage).
  • agent_id: Assigned agent identifier (may be empty if unassigned).
  • resolution_time_minutes: Time to resolution in minutes, if resolved.
  • satisfaction_rating: Post-resolution customer satisfaction score (1–5), if provided.

product_reviews_demo.csv

• use case: Analyze product review sentiment and quality across channels.
• key columns:
  • review_id: Unique review identifier.
  • product_id: Identifier of the reviewed product.
  • product_name: Human-readable product name.
  • customer_id: Customer identifier.
  • review_date: Date the review was created.
  • rating: Star rating (typically 1–5).
  • title: Short review title.
  • review_text: Full text of the review.
  • verified_purchase: Boolean flag indicating if the purchase was verified.
  • source: Review source (e.g. website, mobile_app, third_party).

WHI_Inflation.csv

• use case: Analyze inflation trends across countries and years, and explore relationships between inflation, economic indicators, and well-being metrics.
• key columns:
  • Country: Name of the country.
  • Year: Year of observation.
  • Headline Consumer Price Inflation: Overall consumer price inflation rate.
  • Energy Consumer Price Inflation: Inflation rate for energy-related consumer prices.
  • Food Consumer Price Inflation: Inflation rate for food-related consumer prices.
  • Official Core Consumer Price Inflation: Core inflation rate excluding volatile items such as food and energy.
  • Producer Price Inflation: Inflation rate of prices received by domestic producers.
  • GDP deflator Index growth rate: Growth rate of the GDP deflator, reflecting overall price changes in the economy.
  • Continent/Region: Geographic region or continent of the country.
  • Score: Overall well-being or happiness score associated with the country-year.
  • GDP per Capita: Gross domestic product per capita.
  • Social support: Measure of perceived social support.
  • Healthy life expectancy at birth: Expected number of healthy years at birth.
  • Freedom to make life choices: Measure of individual freedom in life decisions.
  • Generosity: Indicator of charitable behavior and generosity.
  • Perceptions of corruption: Measure of perceived corruption in government and institutions.

robotics_data.csv

• use case: Analyze the impact of robotics adoption across industries over time, focusing on productivity gains, cost savings, workforce displacement, and training requirements.
• key columns:
  • Year: Year of observation.
  • Industry: Industry sector where robots are adopted (e.g. manufacturing, healthcare, logistics).
  • Robots_Adopted: Number of robots adopted in the given industry and year.
  • Productivity_Gain: Percentage increase in productivity attributed to robot adoption.
  • Cost_Savings: Estimated cost savings resulting from automation.
  • Jobs_Displaced: Number of jobs displaced due to robotics adoption.
  • Training_Hours: Total training hours required to upskill workers for working alongside robots.

robot_inverse_kinematics_dataset.csv

• use case: Support inverse kinematics analysis and modeling for robotic manipulators by mapping end-effector positions and orientations to corresponding joint configurations.
• key columns:
  • q1: Joint angle of the first robot joint.
  • q2: Joint angle of the second robot joint.
  • q3: Joint angle of the third robot joint.
  • q4: Joint angle of the fourth robot joint.
  • q5: Joint angle of the fifth robot joint.
  • q6: Joint angle of the sixth robot joint.
  • x: X-coordinate of the end-effector position.
  • y: Y-coordinate of the end-effector position.
  • z: Z-coordinate of the end-effector position.

German_FinTechCompanies.csv

• use case: Analyze the German FinTech ecosystem by examining company status, business segments, founding information, and geographic distribution.
• key columns:
  • ID: Unique identifier for the FinTech company.
  • Name: Commonly used name of the company.
  • Status: Current operational status of the company.
  • Original German: Original German-language company name.
  • Founding year: Year the company was founded.
  • Founder: Name of the company founders.
  • Linkedin-Account Founder: LinkedIn profile of the founder(s), if available.
  • Legal Name: Official registered legal name of the company.
  • Legal form: Legal structure of the company.
  • Street: Street address of the company headquarters.
  • Postal code: Postal code of the company address.
  • City: City where the company is located.
  • Country: Country where the company is registered.
  • Register Number/ Company ID/ LEI: Official registration number or legal entity identifier.
  • Segment: Primary FinTech market segment.
  • Subsegment: More specific business subcategory within the main segment.
  • Bank Cooperation: Indicator of cooperation with banks.
  • Homepage: Official company website URL.
  • E-Mail: Contact email address of the company.
  • Insolvency: Indicator of insolvency status.
  • Liquidation: Indicator of whether the company is in liquidation.
  • Date of inactivity: Date when the company became inactive, if applicable.
  • Local court: Local court responsible for company registration.
  • Former name: Previous name of the company, if applicable.

Fintech_user.csv

• use case: Analyze user behavior, engagement, and churn patterns in a FinTech platform, including product usage, credit activity, and reward participation.
• key columns:
  • user: Unique identifier for a user.
  • churn: Indicator of whether the user has churned.
  • age: Age of the user.
  • housing: Indicator of the user’s housing status.
  • credit_score: Credit score of the user.
  • deposits: Total amount of deposits made by the user.
  • withdrawal: Total amount of withdrawals made by the user.
  • purchases_partners: Number or amount of purchases made with partner merchants.
  • purchases: Total number or amount of purchases.
  • cc_taken: Indicator of whether a credit card was taken by the user.
  • cc_recommended: Indicator of whether a credit card was recommended to the user.
  • cc_disliked: Indicator of whether the user disliked the recommended credit card.
  • cc_liked: Indicator of whether the user liked the recommended credit card.
  • cc_application_begin: Indicator of whether the user started a credit card application.
  • app_downloaded: Indicator of whether the mobile app was downloaded.
  • web_user: Indicator of whether the user uses the web platform.
  • app_web_user: Indicator of whether the user uses both app and web platforms.
  • ios_user: Indicator of whether the user uses the iOS app.
  • android_user: Indicator of whether the user uses the Android app.
  • registered_phones: Number of phone numbers registered by the user.
  • payment_type: Preferred payment method used by the user.
  • waiting_4_loan: Indicator of whether the user is waiting for a loan decision.
  • cancelled_loan: Indicator of whether the user cancelled a loan application.
  • received_loan: Indicator of whether the user received a loan.
  • rejected_loan: Indicator of whether the user’s loan application was rejected.
  • zodiac_sign: Zodiac sign of the user.
  • left_for_two_month_plus: Indicator of whether the user left for more than two months.
  • left_for_one_month: Indicator of whether the user left for one month.
  • rewards_earned: Total rewards earned by the user.
  • reward_rate: Reward rate associated with the user.
  • is_referred: Indicator of whether the user was referred by another user.

Electric_Vehicle_Population_Data.csv

• use case: Analyze the distribution and characteristics of electric vehicles across regions, including vehicle types, manufacturers, model years, and eligibility for clean fuel programs.
• key columns:
• VIN (1-10): First 10 characters of the vehicle identification number.County: County where the vehicle is registered.
• City: City where the vehicle is registered.
• State: State where the vehicle is registered.
• Postal Code: Postal (ZIP) code of the vehicle registration location.
• Model Year: Manufacturing year of the vehicle model.
• Make: Vehicle manufacturer.
• Model: Vehicle model name.
• Electric Vehicle Type: Type of electric vehicle (e.g. battery electric, plug-in hybrid).
• Clean Alternative Fuel Vehicle (CAFV) Eligibility: Eligibility status for clean alternative fuel vehicle programs.
• Electric Range: Estimated electric-only driving range of the vehicle.
• Base MSRP: Base manufacturer’s suggested retail price.
• Legislative District: Legislative district associated with the vehicle registration.
• DOL Vehicle ID: Department of Licensing vehicle identifier.
• Vehicle Location: Geographic location information for the vehicle.
• Electric Utility: Electric utility provider serving the vehicle’s location.
• 2020 Census Tract: Census tract identifier based on the 2020 census.