4. Tackling Company Bankruptcy

Activity 4.01: Feature Selection with Lasso

1. Import Lasso from the sklearn.linear_model package:

   from sklearn.linear_model import Lasso
   from sklearn.feature_selection import SelectFromModel

2. Fit the independent and dependent variables with lasso regularization for the mean_imputed_df4 DataFrame:

   features_names=X6.columns.tolist()
   lasso = Lasso(alpha=0.01 ,positive=True)
   lasso.fit(X6,y6)

3. Print the coefficients of lasso regularization:

   coef_list=sorted(zip(map(lambda x: round(x,4), \
                            lasso.coef_.reshape(-1)),\
                            features_names), reverse=True)
   coef_list [0:5]

   The output will be as follows:

   [(0.0009, 'X21'), (0.0002, 'X2'), (0.0001, ...