Splitting Dataset
Python3
from sklearn.model_selection import train_test_split X = data.drop(['Loan_Status'],axis=1) Y = data['Loan_Status'] X.shape,Y.shape X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.4, random_state=1) X_train.shape, X_test.shape, Y_train.shape, Y_test.shape |
Output:
((598, 11), (598,)) ((358, 11), (240, 11), (358,), (240,))
Loan Approval Prediction using Machine Learning
LOANS are the major requirement of the modern world. By this only, Banks get a major part of the total profit. It is beneficial for students to manage their education and living expenses, and for people to buy any kind of luxury like houses, cars, etc.
But when it comes to deciding whether the applicant’s profile is relevant to be granted with loan or not. Banks have to look after many aspects.
So, here we will be using Machine Learning with Python to ease their work and predict whether the candidate’s profile is relevant or not using key features like Marital Status, Education, Applicant Income, Credit History, etc.
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