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

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.

Apply machine learning to solve the challenge of credit risk

Utilize machine learning models in assessing credit risks for an individual

The project focuses on handling imbalanced data using techniques like RandomUnderSampler and TomekLinks, while exploring various models such as CART, Random Forest, GBM, and LightGBM. The BalancedRandomClassifier, optimized through hyperparameter tuning, achieved an 80% recall on high-risk customers with an accuracy of 74%.

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

Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.

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.

Use different techniques to train and evaluate different machine learning models to predict credit risk with unbalanced classes

Credit_Risk_Analysis using Machine Learning

Machine learning models for predicting credit risk in LendingClub dataset.

An analysis on 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).

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

Utilizing machine learning to examine deforestation rates in the undeveloped region of Paraguay's Chaco