balanced-accuracy-scores

There are 0 repository under balanced-accuracy-scores topic.

• tienl / SupervisedML

  Supervised machine learning on lending and credit risk.

  balanced-accuracy-scoresadaboostloansriskmachine-learning
  Language:Jupyter Notebook 3

• devinaa1604 / Credit_Risk_Analysis_Using_Classification

  This project aims to predict credit risk using various ensemble machine learning techniques. I have also tried to handle imbalance by using various sampling methods.

  logistic-regressionimbalanced-learningbalanced-accuracy-scoresimbalance-classificationsmote-samplingcluster-centroidssmoteennensemble-learning
  Language:Jupyter Notebook 1

• mmsaki / credit-risks-ml

  Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.

  balanced-accuracy-scoresclassification-modelscredit-riskimbalanced-classificationimbalanced-learningloan-prediction-analysislogistic-regressionmachine-learningoversamplingpredictive-modelingresamplingsklearnsmote-oversamplersmoteenn-combinationundersampling-technique
  Language:Jupyter Notebook 1

• shaunwang1350 / CreditLoans_MachineLearning

  Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the performance of these models and made a recommendation on whether they should be used to predict credit risk.

  classification-reportcredit-risktp-fpbalanced-accuracy-scoressmote-samplingrandomoversamplercluster-centroidsrandom-forest
  Language:Jupyter Notebook 1

• karthikvadlamani / revolut-fraudsters-detection

  Performed feature engineering, cross-validation (5 fold) on baseline and cost-sensitive (accounting for class imbalance) Decision trees and Logistic Regression models and compared performance. Used appropriate performance metrics i.e., AUC ROC, Average Precision and Balanced Accuracy. Outperformed baseline model.

  auc-roc-scoreaverage-precisionbalanced-accuracy-scoresclass-imbalance-problemcross-validationdecision-treesfeature-engineeringlogistic-regressionperformance-metrics
  Language:Jupyter Notebook 0

• klaudio07 / Credit_Risk_Analysis

  Use Python to build and evaluate several machine learning models to predict credit risk for FinTech firms.

  credit-riskbalanced-accuracy-scoresfintech-firmsanalysis
  Language:Jupyter Notebook 0

• Parita2442 / Customer-Churn-Prediction

  A predictive model to anticipate customer churn in telecom. Using supervised ML techniques, it identifies at-risk customers based on usage patterns and service plans. Proactively retaining customers, reducing attrition costs.

  balanced-accuracy-scoreschurn-predictionclass-imbalancedecision-tree-classifiermachine-learningsvm-classifierxgboost-classifier
  0

• Tracari / Credit_Risk_Analysis

  Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk

  balanced-accuracy-scoresbalanced-random-forestimbalance-learningmachine-learningmachine-learning-algorithmsprecision-recallprecision-recall-curvepythonpython-libraryscikitlearn-machine-learning
  Language:Jupyter Notebook 0

• shayleaschreurs / Supervised_Learning_Regression_Model

  Module 12 - Using the imblearn , I'll use a logistic regression model to compare 2 versions of a dataset. First, I’ll use the original data. Next, I’ll resample the data by using RandomOverSampler. In both cases, I’ll get the count of the target classes, train a logistic regression classifier, calculate the balanced accuracy score, generate a con

  balanced-accuracy-scoresclassification-reportconfusion-matriximbalanced-learningimblearnlogistic-regressionnumpypandaspythonrandom-over-samplingscikit-learnsklearntrain-test-split
  Language:Jupyter Notebook