null_value_handling.py

import pandas as pd 
import streamlit as st 
# import simple imputer, iter imputer , knn inputer 
from sklearn.model_selection import train_test_split as tts 
from sklearn.experimental import enable_iterative_imputer 
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer 
import evaluationer 
# import label, ohe,ordinal encoder 
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder 
 
# creating a function for null_handling with different methods for null value imputing, categorical columns encoding and evaluation 
 
null_value_handling_method_num_cols = ["KNN Imputed","SI Mean Imputed","SI Median Imputed","SI Most Frequent Imputed","Iter Imputed"] 
null_value_handling_method_cat_cols = ["SI Most Frequent Imputed (categorical)"] 
 
# dict for null value handling method num cols  
 
dict1 = {"KNN Imputed" :KNNImputer(n_neighbors = 5),"SI Mean Imputed":SimpleImputer(strategy = "mean"),"SI Median Imputed":SimpleImputer(strategy = "median"), 
 "SI Most Frequent Imputed":SimpleImputer(strategy = "most_frequent"),"Iter Imputed":IterativeImputer(max_iter = 200,random_state= 42)} 
 
dict2 = {"SI Most Frequent Imputed (categorical)":SimpleImputer(strategy = "most_frequent")} 
 
# creating dataframe from dict1 and dict2 
num_nvh_method_df = pd.DataFrame(data=dict1.values(), index=dict1.keys()) 
cat_nvh_method_df = pd.DataFrame(data=dict2.values(), index=dict2.keys()) 
 
num_imputed_dict = {"KNN Imputed":[],"SI Mean Imputed":[],"SI Median Imputed":[],"SI Most Frequent Imputed":[],"Iter Imputed":[]} 
 
cat_imputed_dict = {"SI Most Frequent Imputed (categorical)":[],"Iter Imputed":[]} 
 
num_imputed_df = pd.DataFrame(data = num_imputed_dict.values(),index = num_imputed_dict.keys()) 
 
cat_imputed_df = pd.DataFrame(data = cat_imputed_dict.values(),index = cat_imputed_dict.keys()) 
 
final_df = [] 
def null_handling(X,clean_num_nvh_df,clean_num_nvh_df_cat): 
 num_nvh_method = clean_num_nvh_df.columns #KNN Imputed","SI Mean Imputed","SI Media 
 cat_nvh_method = clean_num_nvh_df_cat.columns 
 for method in num_nvh_method: 
 X[clean_num_nvh_df[method].dropna().values] = num_nvh_method_df.loc[method].values[0].fit_transform(X[clean_num_nvh_df[method].dropna().values]) 
 
 for method in cat_nvh_method: 
 X[clean_num_nvh_df_cat[method].dropna().values] = cat_nvh_method_df.loc[method].values[0].fit_transform(X[clean_num_nvh_df_cat[method].dropna().values]) 
 
 final_df = X 
 
 return final_df