- there are 3 functions in this project - best function, rankhospital function and rankall function
- best function takes 2 argument- the 2-character abbreviated name of a state and an
#outcome name(ailment) and returns the hospital Name with the least mortality for the specified ailment
- rankhospital function takes 3 arguments: the 2-character abbreviated name of a
state (state), an outcome (outcome), and the ranking of a hospital in that state for that outcome (num) and returns returns a character vector with the name of the hospital that has the ranking specified by the num argument
-rankall function takes two arguments: an outcome name (outcome) and a hospital ranking (num) and returns data frame containing the names of the hospitals that are the best in their respective states for outcome (ailment) specified
- In this project, I tried to automate the loan eligibility process for a finance company that deals with home loans
- I used the data(details) of the customer gotten through the filling of the application form on the company’s website to train the model.
- I did some Exploratory Data Analysis as well as data cleaning using Python
- Thereafter I created a machine learning model(the decision tree classifier seemed to be the best model) that can sucessfully predicts the loan eligibility of a customer