Get whl file using the below link.
https://www.lfd.uci.edu/~gohlke/pythonlibs/
python file_name.whl
This folder contains sample code for how to implement XGBoost regression using python in simple steps.
First install gcc using the terminal
brew install gcc@8
Then install xgboost using
pip install xgboost
This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.
It's a great dataset for evaluating simple regression models.
Rows and Columns size
21.6k x 21
19 house features plus the price and the id columns, along with 21613 observations.
1. id - a notation for a house
2. date - Date house was sold
3. price - Price is prediction target
4. bedrooms - Number of Bedrooms/House
5. bathrooms - Number of bathrooms/bedrooms
6. sqft_living - square footage of the home
7. sqft_lot - square footage of the lot
8. floors - Total floors (levels) in house
9. waterfront - House which has a view to a waterfront
10. view - Has been viewed
11. condition - How good the condition is ( Overall )
12. grade - overall grade given to the housing unit, based on King County grading system
13. sqft_above - square footage of house apart from basement
14. sqft_basement - square footage of the basement
15. yr_built - Built Year
16. yr_renovated - Year when house was renovated
17. zipcode - zip
18. lat - Latitude coordinate
19. long - Longitude coordinate
20. sqft_living15 - Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area
21. sqft_lot15 - lotSize area in 2015(implies-- some renovations)