ade-irawan / dummy-datasets

dummy-datasets

1. hr_data.csv
   Why are our best and most experienced employees leaving prematurely?
   A data frame with 14999 rows and 10 variables
   

   Details
   

    satisfaction_level: Level of satisfaction (0-1)
    last_evaluation: Time since last performance evaluation (in Years)
    number_project: Number of projects completed while at work
    average_montly_hours: Average monthly hours at workplace
    time_spend_company: Number of years spent in the company
    Work_accident: Whether the employee had a workplace accident
    left: Whether the employee left the workplace or not (1 or 0) Factor
    promotion_last_5years: Whether the employee was promoted in the last five years
    sales Department: in which they work for
    salary: Relative level of salary (high)
   

   source: https://www.rdocumentation.org/packages/breakDown/versions/0.2.1/topics/HR_data

2. melb_data.csv
   Why are our best and most experienced employees leaving prematurely?
   A data frame with 14999 rows and 10 variables
   

   Details
   

    Rooms: Number of rooms
    Price: Price in dollars
    Method: S - property sold; SP - property sold prior; PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed. N/A - price or highest bid not available.
    Type: br - bedroom(s); h - house,cottage,villa, semi,terrace; u - unit, duplex; t - townhouse; dev site - development site; o res - other residential.
    SellerG: Real Estate Agent
    Date: Date sold
    Distance: Distance from CBD
    Regionname: General Region (West, North West, North, North east …etc)
    Propertycount: Number of properties that exist in the suburb.
    Bedroom2 : Scraped # of Bedrooms (from different source)
    Bathroom: Number of Bathrooms
    Car: Number of carspots
    Landsize: Land Size
    BuildingArea: Building Size
    CouncilArea: Governing council for the area
   

   source: https://www.kaggle.com/datasets/dansbecker/melbourne-housing-snapshot