build.py

# %load q03_logistic_regression/build.py
# Default Imports
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from greyatomlib.logistic_regression_project.q01_outlier_removal.build import outlier_removal
from greyatomlib.logistic_regression_project.q02_data_cleaning_all.build import data_cleaning
from greyatomlib.logistic_regression_project.q02_data_cleaning_all_2.build import data_cleaning_2

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)
X_train, X_test, y_train, y_test = data_cleaning_2(X_train, X_test, y_train, y_test)


# Write your solution code here:

def logistic_regression(X_train,X_test,y_train,y_test):
    
    scale = StandardScaler()
    scale.fit(X_train[['ApplicantIncome','CoapplicantIncome','LoanAmount']])
    
    log_reg = LogisticRegression()
    log_reg.fit(X_train,y_train)
    
    y_pred = log_reg.predict(X_test)
    conf_matrix = confusion_matrix(y_test,y_pred)
       
    return conf_matrix
# logistic_regression(X_train,X_test,y_train,y_test)