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

Developed Machine Learning Models to Predict Credit Risk

Apply machine learning to solve the challenge of credit risk

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

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

Built several supervised machine learning models to predict the credit risk of candidates seeking loans.

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,

Testing various supervised machine learning models to predict a loan applicant's credit risk.

Build and evaluate several machine learning algorithms by resampling models to predict credit risk.

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

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.

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).

Credit_Risk_Analysis using Machine Learning

An analysis on credit risk

Analyzing credit card risk with machine learning models!

Train and evaluate models to determine credit card risk using a credit card dataset

Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.

Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.