build.py

# %load q02_data_cleaning_all_2/build.py
# Default Imports
import pandas as pd
import numpy as np
from greyatomlib.logistic_regression_project.q02_data_cleaning_all.build import data_cleaning
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)
X, y, X_train, X_test, y_train, y_test = data_cleaning(loan_data)


# Write your solution here :
def data_cleaning_2(X_train, X_test, y_train, y_test):
    
    CCL=['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area']
    X_train=pd.get_dummies(X_train,columns=CCL, drop_first=True)
    X_test=pd.get_dummies(X_test,columns=CCL, drop_first=True)
    
    numeric_column=['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount']
    for c in numeric_column:
        X_train[c] = np.sqrt(X_train[c])
        X_test[c] = np.sqrt(X_test[c])
    return X_train, X_test, y_train, y_test

# data_cleaning_2(X_train, X_test, y_train, y_test)