Project Explanation Data Collection The dataset is collected from Kaggle. The dataset which we get from kaggle consists of two CSV(Comma Separated Values) files. One is Train Data (loan_sanction_train.csv)
Another is Test Data (loan_sanction_test.csv) Loading the collected data
The CSV data is loaded with the help of read_csv method in pandas library.
test= pd.read_csv('/content/loan_sanction_test.csv')
train= pd.read_csv('/content/loan_sanction_train.csv')
The Training data consists of 614 applicant samples and 12 features. The 12 features are Loan_ID, Gender, Married, Dependents, Education, Self_Employed, ApplicanIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term, Credit_History and Property Area.
Dependents
The Dependents feature is a discrete kind of quantitative data. From my thought, dependents feature refer to the number of children of applicant. For 15 applicants, Dependents is not mentioned in the data. There are 4 unique values present in this feature. They are 0, 1, 2, and 3+
