- Python (Jupyter Notebook)
- Pandas
- Decision Tree (Classification)
- Feature Selection and Modeling
- Model enterpretability and error analysis
- Reports and visualization
- AutoML
Performed a predictive analysis on the customer's Bank Loan Application data to predict loan status. Using python, panadas, scipy, seaborn, AutoML libraries and machine learning techinques.
Used Machine Learning techniques to accurately predict the evaluation scheme if particular loan will be 'Fully Paid' or 'Charged Off'. Which means if Bank accepts particular person's loan application will it be Fully Paid or it won't
In today's world where banking has became sophisticated like never before. However, this comes with great responsibility that Banks dont go in bad debt trap
Many times it may happen that when we apply for a Bank Loan and Bank would verify and conduct background check on us. This process can be automated with the help of Machine Learning and accurately predict the evaluation scheme if particular loan will be 'Fully Paid' or 'Charged Off'. Which means if Bank accepts particular person's loan application will it be Fully Paid or it won't
This could be very important for banking system to predict the results based upon customer's data and his/her finiancial details which could be a major factor for deciding whether or not loan application should be accepted or not for particular customer to automate Bank Loan Application Process
In this notebook I will be using H2O AutoML library to examine and evaluate scheme for 'Loan Status' prediction and identify better suitable model to go with and try to fine tune in order to get better performance metrics and results
