Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
-
Updated
Sep 12, 2021 - Jupyter Notebook
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
Built several supervised machine learning models to predict the credit risk of candidates seeking loans.
Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.
Apply machine learning to solve the challenge of credit risk
Analyzing credit card risk with machine learning models!
Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.
Train and evaluate models to determine credit card risk using a credit card dataset
Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn
We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.
Developed Machine Learning Models to Predict Credit Risk
Build and evaluate several machine learning algorithms by resampling models to predict credit risk.
Using machine learning to determine which model is best at predicting credit risk amongst random oversampling, SMOTE, ClusterCentroids, SMOTEENN, Balanced Random Forest, or Easy Ensemble Classifier (AdaBoost).
An analysis on credit risk
Credit_Risk_Analysis using Machine Learning
Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,
I am asked to resample the credit card data since it is not balanced. First, I start to split the data and perform oversampling with RandomOverSampler and SMOTE method, and I undersample with ClusterCentroids algorithm. Then, I utilize the SMOTEENN method to oversample and undersample the data. Finally, I used ensemble models.
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
Banking-Dataset-Marketing-Targets
Analyze of several Machine Learning techniques in order to help Jill decide on a most effective Machine Learning Model to analyze Credit Card Risk applications.
Add a description, image, and links to the easyensembleclassifier topic page so that developers can more easily learn about it.
To associate your repository with the easyensembleclassifier topic, visit your repo's landing page and select "manage topics."