Linear Regression model to solve dataset from kaggle, about predicting house prices from certain parameters
It turns out that one of the most significant parameters are the number of bathroms, where the increase of 1 bathroom will make the price of the house becomes $45.672 more expensive
and the increase of 1 unit of the area of the waterfront effect the house price of $561.826 more expensive.
details are follows :-
bathrooms 45672.237710 bedrooms -36679.605508 condition 28288.759794 floors 5909.918291 grade 94572.606770 lat 590935.193409 long -224812.219949 sqft_above 73.680688 sqft_basement 35.262752 sqft_living 108.943439 sqft_living15 22.608347 sqft_lot 0.131337 sqft_lot15 -0.354817 view 57268.309540 waterfront 561826.556859 yr_built -2664.064631 yr_renovated 13.720421 zipcode -555.650236