def missing_value_treatment(self, min_threshold):
# Identify na values exist and add them to a list
missing_value_feats = f.get_missing_value_feats(self.ds1_df)
print(missing_value_feats)
# Calculate Missing Value percentage and Visualize
missing_values_perc_df = f.missing_val_perc(missing_value_feats,
self.ds1_df)
val = missing_values_perc_df[0].sort_values(ascending=False)
f.plot_bar(val.index, (50, 10), val)
# Check direct imputations such as remove the records for attributes which contain less than 5% of null values or remove
# attributes which contain more than 65% of null values.
self.ds1_df = f.impute_values(self.ds1_df,
missing_value_feats,
min_threshold,
action=True)
self.ds1_df.reset_index(drop=True)
# How row in dataframe having more than x% NaN values
na_row_cnt = f.get_rowcnt_most_missing_val(self.ds1_df, 30)
print('No of rows having more than 30% NA Values', na_row_cnt)
# Identify na values exist and add them to a list
missing_value_feats = f.get_missing_value_feats(self.ds1_df)
print(missing_value_feats)
missing_value_feats = f.get_missing_value_feats(x_df)
missing_value_feats
# Calculate Missing Value percentage and Visualize
missing_values_perc_df = f.missing_val_perc(missing_value_feats, x_df)
val = missing_values_perc_df[0].sort_values(ascending=False)
f.plot_bar(val.index, (50, 10), val)
#################### REMOVING THE VALUES DIRECTLY ##########################
# Check direct imputations such as remove the records for attributes which contain less than 5% of null values or remove
# attributes which contain more than 65% of null values.
imp_df = f.impute_values(x_df, missing_value_feats, 65, action=True)
imp_df.reset_index(drop=True)
# How row in dataframe having more than x% NaN values
na_row_cnt = f.get_rowcnt_most_missing_val(imp_df, 30)
# Identify na values exist and add them to a list
missing_value_feats = f.get_missing_value_feats(imp_df)
missing_value_feats
##################### OUTLIERS TREATMENT ###################################
# Find the num and cat feats for imp_df
num_feats_imp_df, cat_feats_imp_df = f.distinct_feats(imp_df)
num_feats_imp_df.remove('SK_ID_CURR')
num_feats_imp_df.remove('TARGET')
print(len(num_feats_imp_df), len(cat_feats_imp_df))
# All Normalized treatment is same : Irrespective of Skew and Kurt Identify outliers and fill with its boundary value
normalized_feats = [