API reference

One line feature engineering:

import pandas as pd 
from AutoFeatureEnginerring import AutoFeatureEngineering
data = pd.read_csv(r"load_csv")

# one line!
out = AutoFeatureEngineering(data).auto_fe()

Args	Description
int_imputation_method	Imputing numerical values. Default method is 'mean'. There are certain methods available! knnImputer or mice
obj_imputation_method	Imputing categorical values. Default method is 'most frequent'

Return	Description
dataframe	Imputed dataframe

Customizable options:

import pandas as pd 
from AutoFeatureEnginerring import AutoFeatureEngineering

# get the data 
data = pd.read_csv(r"https://raw.githubusercontent.com/RAravindDS/auto_fe/main/research/train.csv")

# impute with your own algorithm!
out_df = auto_fe_obj.auto_fe(int_imputation_method = 'KnnImputer')  # this will impute the values with KnnImputer 
out_df = auto_fe_obj.auto_fe(int_imputation_method = 'mice') # this will impute the values with mice imputer  with default values!

Bonus Methods!

example

import pandas as pd 
from AutoFeatureEnginerring import AutoFeatureEngineering

# get the data 
data = pd.read_csv(r"https://raw.githubusercontent.com/RAravindDS/auto_fe/main/research/train.csv")

# it will grab the numerical columns
auto_fe_obj.int_columns()  # It will grab the numerical columns! 

# it will grab the categorical columns
auto_fe_obj.string_columns()

# grabbing missing values based on datatype 
categorical_missing_col, numerical_missing_col, non_missing_col = auto_fe_obj.get_missing_values()   # It will return the dataframe!
# it contains missing values based on datatypes!