Testing 6 different machine learning models to determine which is best at predicting credit risk.
-
Updated
Jan 23, 2023 - Jupyter Notebook
Testing 6 different machine learning models to determine which is best at predicting credit risk.
Established a supervised machine learning model trained and tested on credit risk data through a variety of methods to establish credit risk based on a number of factor
Uses several machine learning models to predict credit risk.
Supervised Machine Learning Project
The purpose of this analysis was to create a supervised machine learning model that could accurately predict credit risk using python's sklearn library.
Supervised Machine Learning and Credit Risk
using machine learning to assess credit risk
This repo is about Machine Learning and Classification
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
Predicts credit risk of individuals based on information within their application utilizing supervised machine learning models
Add a description, image, and links to the naive-random-oversampler topic page so that developers can more easily learn about it.
To associate your repository with the naive-random-oversampler topic, visit your repo's landing page and select "manage topics."