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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Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.
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As AI becomes more ubiquitous, it’s also become more autonomous — able to act on its own without human supervision. This demonstrates progress, but it also introduces concerns around control over AI. The AI Arms Race has driven organizations everywhere to deliver the most sophisticated algorithms around, but this can come at a price, ignoring cultural and ethical values that are critical to responsible AI. Here are five predictions on what we should expect to see in AI in 2021:
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