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)
def logistic_regression(X_train, X_test, y_train, y_test):
    #Scaling the data
    scaler = StandardScaler()
    StandardScaler(copy=True, with_mean=True, with_std=True)
    scaler.fit(X_train)
    scaler.transform(X_train)
    scaler.fit(X_test)
    scaler.transform(X_test)
    #Logistic regression
    model1=LogisticRegression(random_state=9).fit(X_train,y_train)
    ypred=model1.predict(X_test)
    #Confusion matrix
    result=confusion_matrix(y_test, ypred)
    return(result)

logistic_regression(X_train, X_test, y_train, y_test)