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$H_0$ : Number of bathrooms and bedrooms in home, as well as square footage will not be leading factors in predicting property value -
$H_a$ : Number of bathrooms, bedrooms and square footage will have a strong reciprocy for predicting property value. -
$H_a$ : Using recursive feature elimination from SK.learn will proove a different variation of features that will predict features mentioned above but not discluding other possible features included in the dataset
| Unit-Title | Description |
|---|---|
| id | primary-key / index |
| bathroomcnt | Number of restrooms in unit (including half-baths and quarter-baths) |
| bedroomcnt | Number of bedrooms in unit |
| calculatedfinishedsquarefeet | Square footage of the property |
| roomcnt | Number of rooms in unit |
| structuretaxvaluedollarcnt | property value |
| taxamount | The amount the homeowner was taxed |
| transactiondate | The date of the last transaction |
- Link for google presentation HERE.
- GitHub repository above
- env.py file is required with log-in credentials to the database used
- My SQL query is in the zillow_wrangle.py file in the repository above
- random state used in the split my data function is 830