Logistic Regression (Model Building and Fitting)

If you have reached here so that means you have tackled the most difficult task for this data, and believe me, I appreciate your efforts.

So, what next? Here we go...

Write a functionlogistic_regression that :

• Build the logistic regression model with random_state=9.
• Fit that model on the test part.
• Gives the confusion matrix as evaluation metric of how good fit model is.

Hint :Perform the feature scaling on testing and training data set on variables(ApplicantIncome, CoapplicantIncome, LoanAmount) using standardScaler() package from sklearn.preprocessing.

Parameters:

Parameter	dtype	argument type	default value	description
X_train	Numpy arrays for training any format acceptable by sklearn	scaled X_train
X_test	Numpy arrays for testing any format acceptable by sklearn	scaled X_test
y_train	Numpy arrays for training any format acceptable by sklearn	y_train
y_test	Numpy arrays for testing any format acceptable by sklearn	y_test

Returns:

Parameter	dtype	description
cm	array of matrix	Confusion matrix to evaluate your model