<jupyter_start><jupyter_text>Get meaningful features from cleaned data- First let's import the cleaned dataset<jupyter_code>import pandas as pd
df = pd.read_pickle("../kaggle-california-housing-data/df_imputed.pickle")<jupyter_output><empty_output><jupyter_text>Get the categorical columns to encode- These are the columns with categorical data    - MSSubClass    - MSZoning    - Street    - Alley    - LotShape    - LandContour    - Utilities    - LotConfig    - LandSlope    - Neighborhood    - Condition1    - Condition2    - BldgType    - HouseStyle    - OverallQual (Check)    - OverallCond (Check)    - YearBuilt (Decide)    - YearModAdd (Decide)    - RoofStyle    - RoofMatl    - Exterior1st    - Exterior2nd    - MasVnrType    - ExterQual    - ExterCond    - Foundation    - BsmtQual    - BsmtCond    - BsmtExposure    - BsmtFinType1    - BsmtFinType2    - Heating    - HeatingQC    - CentralAir    - Electrical    - KitchenQual    - Functional    - FireplaceQu    - GarageType    - GarageYrBlt (Check)    - GarageFinish    - GarageQual    - GarageCond    - PavedDrive    - PoolQC    - Fence    - MiscFeature    - MoSold (Check)    - YrSold (Check)    - SaleType    - SaleCondition<jupyter_code># What to do with year? Makes sense to keep as numerical? 
# Or combine features into YrMonth
# Does month make sense as numerical?<jupyter_output><empty_output>
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