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Dream Housing Finance company offers home loans to customers. The company takes into account personal information(demographic variables, historical loan status or economic variables) to either approve or deny the loan request for a particular amount and term.

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Loan-Prediction

A Classification Problem which predicts if a loan will get approved or not.

Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. The company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.

Dataset- The data has 614 rows and 13 columns.

Dataset Description-

Variable

Description

Loan_ID - Unique Loan ID

Gender - Male/ Female

Married- Applicant married (Y/N)

Dependents - Number of dependents

Education - Applicant Education (Graduate/ Under Graduate)

Self_Employed - Self employed (Y/N)

ApplicantIncome - Applicant income

CoapplicantIncome - Coapplicant income

LoanAmount - Loan amount in thousands

Loan_Amount_Term - Term of loan in months

Credit_History - credit history meets guidelines

Property_Area - Urban/ Semi Urban/ Rural

Loan_Status - Loan approved (Y/N) (Trarget Variable)

Link for the dataset: https://www.kaggle.com/leonbora/analytics-vidhya-loan-prediction

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Dream Housing Finance company offers home loans to customers. The company takes into account personal information(demographic variables, historical loan status or economic variables) to either approve or deny the loan request for a particular amount and term.

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