mu = 12.02 and sigma = 0.40

              Total   Percent
PoolQC         2908  0.996915
MiscFeature    2812  0.964004
Alley          2719  0.932122
Fence          2346  0.804251
FireplaceQu    1420  0.486802
LotFrontage     486  0.166610
GarageCond      159  0.054508
GarageQual      159  0.054508
GarageYrBlt     159  0.054508
GarageFinish    159  0.054508
GarageType      157  0.053822
BsmtCond         82  0.028111
BsmtExposure     82  0.028111
BsmtQual         81  0.027768
BsmtFinType2     80  0.027425
BsmtFinType1     79  0.027083
MasVnrType       24  0.008228
MasVnrArea       23  0.007885
MSZoning          4  0.001371
BsmtHalfBath      2  0.000686
Utilities         2  0.000686
Functional        2  0.000686
BsmtFullBath      2  0.000686
BsmtFinSF2        1  0.000343
BsmtFinSF1        1  0.000343
Exterior2nd       1  0.000343
BsmtUnfSF         1  0.000343
TotalBsmtSF       1  0.000343
Exterior1st       1  0.000343
SaleType          1  0.000343
Electrical        1  0.000343
KitchenQual       1  0.000343
GarageArea        1  0.000343
GarageCars        1  0.000343

Most correlated features to SalePrice:
SalePrice      1.000000
TotalSF        0.825326
OverallQual    0.821405
GrLivArea      0.725211
ExterQual      0.682226
GarageCars     0.681033
TotalBaths     0.676678
KitchenQual    0.669990
GarageArea     0.656129
TotalBsmtSF    0.647563
Name: SalePrice, dtype: float64

Skew in numerical features:
There are 91 skewed numerical features to Box Cox transform

Pipeline(memory=None,
     steps=[('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
       with_scaling=True)), ('elasticnet', ElasticNet(alpha=0.0005, copy_X=True, fit_intercept=True, l1_ratio=0.9,
      max_iter=1000, normalize=False, positive=False, precompute=False,
      random_state=101010, selection='cyclic', tol=0.0001,
      warm_start=False))])
GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
             learning_rate=0.05, loss='huber', max_depth=4,
             max_features='sqrt', max_leaf_nodes=None,
             min_impurity_decrease=0.0, min_impurity_split=None,
             min_samples_leaf=15, min_samples_split=10,
             min_weight_fraction_leaf=0.0, n_estimators=3000,
             presort='auto', random_state=101010, subsample=1.0, verbose=0,
             warm_start=False)
XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.2,
       gamma=0.0, learning_rate=0.05, max_delta_step=0, max_depth=3,
       min_child_weight=1.7, missing=None, n_estimators=2200, nthread=-1,
       objective='reg:linear', reg_alpha=0.9, reg_lambda=0.6,
       scale_pos_weight=1, seed=101010, silent=1, subsample=0.5)
Pipeline(memory=None,
     steps=[('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
       with_scaling=True)), ('lasso', Lasso(alpha=0.0005, copy_X=True, fit_intercept=True, max_iter=1000,
   normalize=False, positive=False, precompute=False, random_state=101010,
   selection='cyclic', tol=0.0001, warm_start=False))])
KernelRidge(alpha=0.6, coef0=2.5, degree=2, gamma=None, kernel='polynomial',
      kernel_params=None)
AVERAGED MODELS

RMSLE: 0.1069 (+/- 0.0063)