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hr_data.csv
Why are our best and most experienced employees leaving prematurely?
A data frame with 14999 rows and 10 variablesDetails
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
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melb_data.csv
Why are our best and most experienced employees leaving prematurely?
A data frame with 14999 rows and 10 variablesDetails
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PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed.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