We have performed data analysis and data visualisation on a subset of the LendingClub dataset and then created a logical regression model to assess whether or not a new customer is likely to meet it's debt obligations(pay back the loan).

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

I will use various techniques to train and evaluate models with imbalanced classes.

Analysis of the student loans data to determine, if there are any loan characteristics that are predictive of the Early Risk Score.

Applying various sampling methods and ML to analyze credit risk

take csv file loan_data_2007_2014,loan_data_2015,loan_data_2007_2014_preprocessed,loan_data_inputs_2015,loan_data_inputs_test,loan_data_inputs_train.csv from banklife line

In this project, we wanna create Credit Risk Management by using Machine Learning, so we dig into the data. what we do for the next steps are Data Preparation, EDA(Exploratory Data Analysis), Data Visualization, Data Preprocessing (Handling Outliers, Missing Value, Feature Encoding, Standardization, and Normalization), Creating Machine Learning …

Comparing sampling techniques and classification algorithms to predict credit risk

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

Projects

This project mainly implements the Monotonic Optimal Binning(MOB) algorithm in SAS 9.4. We extend the application of this algorithm which can be applied to numerical and categorical data. In order to avoid the problem of creating too many bins, we optimize the p-value iteratively and provide bins size first binning, monotonicity first binning, a…