Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression.
Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"
Data contain the information about user (Graduation Status, Maritial Status, Taxable Income, City Population of user, Work Experience , Urban- Is that person belong to an urban area or not)
-> Perform Data pre-prossing
-> check for normality in data
-> Correlation
-> label encoding
-> Model building
-> Finding important features
-> Apply on new data set