I'm new to data science, and am working on projects to develop my skills. I'm currently working on the Loan Prediction practice competition offered by Analytics Vidhya.The goal is to build a predictive model that will determine whether a loan will be approved or not.
So far, my models (logistic regression, random forest) do not perform any better than simply using the applicant's credit history as a predictor.
I've read through the provided tutorial (I don't have high enough rep to include all the links):
https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/ which details the same results. The tutorial mentions that feature engineering will be important for this (and most) projects. I've found a few other resources for feature engineering (
http://data-informed.com/how-to-improve-machine-learning-tricks-and-tips-for-feature-engineering/ and http://adataanalyst.com/machine-learning/comprehensive-guide-feature-engineering/ ) being the most helpful) but I'm still unable to build a model with higher accuracy.
The variables included in the data set are:
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)
I've first multiplied LoanAmount by 1000, to have the value in dollars.
I've tried creating the following new features:
TotalIncome = ApplicantIncome + CoapplicantIncome
Monthly_Payment = LoanAmount / Loan_Amount_Term
Payment_Ratio = Monthly_Payment / TotalIncome
household_size = the number of people in the household, derived from marital status and number of dependents
I also found that many of the continuous variables (LoanAmount, TotalIncome, Payment_Ratio) are right-skewed and have transformed them by taking the log.
None of these changes have had any effect. It seems that no matter what variables I include in my model, it will not achieve better accuracy than the model that only considers Credit_History.
Are there any obvious features I've overlooked?
What should I try next?