Testing 6 different machine learning models to determine which is best at predicting credit risk.

It's a classification model that predict whether an individual will suffer from autism in future or not

Credit Risk Analysis utilizing imbalanced classification machine learning models

Supervised Machine Learning

peer-to-peer lending, use techniques to train and evaluate Machine Learning models with imbalanced classes to identify the worthy

Data preparation, Statistical reasoning, Machine Learning

In this project, I will use credit risk models to assess the credit risk using peer-to-peer lending. Algorithms such as SMOTE, Naive Random Sampling, etc.

Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn

Uses several machine learning models to predict credit risk.

Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.