- Clean the dataset of all the null values and make sure that there were no datatypes did not match up correctly
- Continue going through the data and clearing outliers and checking for multicollinearity.
- Focused on features that will sell like:
- Price/Sqft
- 3 bedrooms/ 2 baths
- Quality of homes
- Variables used for the model were:
- Continuous variables
- price_per_sqft
- sqft_living
- sqft_lot15
- Categorical variables
- quality
- bathrooms
- bedrooms
- view
- condition
- floors
- waterfront
- Continuous variables
- After determining the values that were categorical and continuous I ran an OLS stats model to check for how well the variables will fit to the model.
- 0.974
- Houses with 3 bedrooms and 2 bathrooms are being sold at a high quality mark of 8.
- The average condition of a house is 3, which is the middle of the way of best condition.
- Houses can be sold for an average price of 450,000 at midway condition and descent quality.
- The zipcodes that adhere to these conditions are:
- 98125
- 98059
- 98028
- 98034
- 98144
- 98011
- 98136
- 98072
- 98065
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