build.py

# %load q02_data_cleaning_all/build.py
# Default Imports
import sys, os
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname('__file__'))))
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split as tts
from greyatomlib.logistic_regression_project.q01_outlier_removal.build import outlier_removal

loan_data = pd.read_csv('data/loan_prediction_uncleaned.csv')
loan_data = loan_data.drop('Loan_ID', 1)
loan_data = outlier_removal(loan_data)

from sklearn.preprocessing import Imputer

# imp_mean = Imputer(missing_values='NaN',strategy='mean')
# imp_mean.fit(loan_data['LoanAmount'])
# loan_data['LoanAmount'] = imp_mean.transform(loan_data['LoanAmount'])
def data_cleaning(loan_data):
    loan_data['LoanAmount'] = (loan_data['LoanAmount']).fillna(loan_data['LoanAmount'].mean())

    loan_data['Gender']=loan_data['Gender'].fillna(loan_data['Gender'].mode)
    # loan_data[['Gender', 'Married', 'Dependents', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History']]
    loan_data['Married']=loan_data['Married'].fillna(loan_data['Married'].mode)
    loan_data['Dependents']=loan_data['Dependents'].fillna(loan_data['Dependents'].mode)
    loan_data['Self_Employed']=loan_data['Self_Employed'].fillna(loan_data['Self_Employed'].mode)
    loan_data['Loan_Amount_Term']=loan_data['Loan_Amount_Term'].fillna(loan_data['Loan_Amount_Term'].mode)
    loan_data['Credit_History']=loan_data['Credit_History'].fillna(loan_data['Credit_History'].mode)
    X = loan_data.drop(['Loan_Status'],axis=1)
    y = loan_data['Loan_Status']
    X_train,X_test,y_train,y_test = tts(X,y,test_size=0.25,random_state=9)
    return X,y,X_train,X_test,y_train,y_test