balancedrandomforestclassifier | Topic

Topic: "balancedrandomforestclassifier"

cp-PYFOREST/PYFOREST-ML

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

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AJMnd/Credit_Risk_Analysis

An analysis on credit risk

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stephperillo/Credit_Risk_Analysis

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).

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daiphuongngo/Porto-Seguro-Safe-Driver-Prediction-Imbalanced-Comparison-Classifiers

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aysecnkci/banking-risk-analysis-imbalanced-data

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%.

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caseygomez/Credit_Risk_Analysis

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

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lingumd/Credit_Risk_Analysis

Machine learning models for predicting credit risk in LendingClub dataset.

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SohrabRezaei/Credit-Risk-Analysis

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.

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