I use linear regression to analyze house prices in northwestern countries

#Business problem -A stakeholder requires to guide home sellers on how to make the houses more valuable.

#Business Question - What can home sellers do to improve house value

The Jupyter Notebook should demonstrate an iterative approach to modeling. It begin with a basic model, and then provides justification. The provides 1-3 paragraphs discussing the final model. The deliverables should explicitly address each step of the data science process.

This project uses the King County House Sales dataset, which can be found in kc_house_data.csv in the data folder in this repo. The description of the column names can be found in column_names.md in the same folder.

-id- unique identified for a house

-Date- house was sold

-Price- is prediction target

-bedroomsNumber - of Bedrooms/House

-bathroomsNumber- of bathrooms/bedrooms

-sqft_livingsquare- footage of the home

-sqft_lotsquare- footage of the lot

-floorsTotal- floors (levels) in house

-waterfront- House which has a view to a waterfront

-view- Has been viewed

-condition- How good the condition is ( Overall )

-grade- overall grade given to the housing unit, based on King County grading system

-sqft_above- square footage of house apart from basement

-sqft_basement- square footage of the basement

-yr_built- Built Year

-yr_renovated- Year when house was renovated

-zipcode- zip

-lat- Latitude coordinate

-long- Longitude coordinate

-sqft_living15- The square footage of interior housing living space for the nearest 15 neighbors

-sqft_lot15- The square footage of the land lots of the nearest 15 neighbors

@IreneWambui

Install plotly with:

For any feedback, reach out to (@irenewambui37@gmail.com)

I am a data science student at Moringa School

-Data modelling

-Data analysis

-Researching