The complete Data Science pipeline on a simple problem. Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas.
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.
It’s a classification problem , given information about the application we have to predict whether the they’ll be to pay the loan or not.
We’ll start by exploratory data analysis , then preprocessing , and finally we’ll be testing different models such as Logistic regression and decision trees.
The data consists of the following rows:
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 of dollars Loan_Amount_Term : Term of loan in months Credit_History : credit history meets guidelines yes or no Property_Area : Urban/ Semi Urban/ Rural Loan_Status : Loan approved (Y/N) this is the target variable
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