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balanced-random-forest

Here are 37 public repositories matching this topic...

sharmaroshan / Fraud-Detection-in-Insurace-Claims

This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

python sklearn jupyter-notebook data-visualization imbalanced-data imbalanced-learning target-encoding imblearn balanced-random-forest

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👁 Insurance-Claim-Fraud-Detection

nirab25 / Insurance-Claim-Fraud-Detection

Insurance claim fraud detection using machine learning algorithms.

machine-learning neural-network random-forest naive-bayes-classifier xgboost linear-discriminant-analysis knn-classification mlp-classifier knn-classifier insurance-claims descision-tree balanced-random-forest

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cedoula / Credit_Risk_Analysis

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

machine-learning machine-learning-algorithms logistic-regression ensemble-model smote oversampling undersampling cluster-centroids randomoversampler balanced-random-forest smoteenn

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Deving789 / Credit_Risk_Analysis

Evaluate the performance of multiple machine learning models using sampling and ensemble techniques and making a recommendation on whether they should be used to predict credit risk.

python machine-learning random-forest scikit-learn supervised-learning ensemble-learning smote oversampling imbalanced-learning undersampling credit-risk balanced-random-forest

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daiphuongngo / Banking-Dataset-Imbalanced-Learn-Comparison

Banking-Dataset-Marketing-Targets

machine-learning deep-neural-networks deep-learning random-forest tensorflow scikit-learn keras xgboost classification decision-tree balancedbaggingclassifier balanced-random-forest rusboost bagging-ensemble easyensembleclassifier

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BellexFire / Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

machine-learning scikit-learn imbalanced-learning balanced-random-forest smoteenn random-over-sampling smote-algorithm easy-ensemble-classifier cluster-centroids-algorithm

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Kenner82 / Credit_Risk_Analysis

Testing 6 different machine learning models to determine which is best at predicting credit risk.

python pandas ensemble-learning supervised-machine-learning ada-boost-classifier balanced-random-forest smote-oversampler naive-random-oversampler cluster-centroid-undersampling smoteenn-combination oversampling-algorithms undersampling-algorithms combination-sampling-algorithms

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jharvey09 / solana_sentinment_analysis

For this analysis, we used computational linguistics and biometrics to systematically identify the trend using various news articles and closing prices using the "CoinGecko CSV & Crypto News API"!

natural-language-processing sentiment-analysis stochastic-gradient-descent balanced-random-forest easy-ensemble-classifier

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BaileeRice / Credit_Risk_Analysis

using machine learning to assess credit risk

machine-learning undersampling balanced-random-forest smote-oversampler naive-random-oversampler easy-ensemble-classifier

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ed12rivera / Credit_Risk_Analysis

Extract data provided by lending club, and transform it to be useable by predictive models.

logistic-regression accuracy-score balanced-random-forest smoteenn smote-oversampler cluster-centroids-undersampling easy-ensemble-classifier

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👁 credit-risk-predictor

sarahm44 / credit-risk-predictor

Uses several machine learning models to predict credit risk.

machine-learning random-forest fintech ensemble-learning logistic-regression resampling smote credit-risk cluster-centroids balanced-random-forest smote-oversampler naive-random-oversampler cluster-centroids-undersampling easy-ensemble-classifier smoteenn-combination

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Akotovets1 / Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

machine-learning confusion-matrix adaboost scikitlearn-machine-learning supervised-machine-learning imbalanced-learning balanced-random-forest random-over-sampling smote-algorithm easy-ensemble-classifier cluster-centroids-algorithm

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Mishkanian / Credit_Risk_Analysis

Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.

python machine-learning scikit-learn smote unbalanced-data balanced-random-forest smoteenn imbalanced-learn easy-ensemble-classifier

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cbrito3 / Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

machine-learning scikit-learn machine-learning-algorithms scikitlearn-machine-learning ada-boost-classifier precision-recall imbalance-learning under-sampling over-sampling balanced-random-forest smote-oversampler naive-random-oversampler cluster-centroids-undersampling easy-ensemble-classifier

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Nusratnimme / Credit_Risk_Analysis

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

machine-learning smote undersampling randomoversampler balanced-random-forest smoteenn easyensembleclassifier

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jbalooshie / Credit_Risk_Analysis

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

smote credit-risk randomoversampler balanced-random-forest smoteenn clustercentroids easyensembleclassifier

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inregards2pluto / credit-risk-analysis

Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.

machine-learning ensemble-model balanced-random-forest cluster-centroid-undersampling adaboost-classifier naive-random-oversampling smoteenn-combination-sampling smote-oversampling

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cmmgw / Credit_Risk_Analysis

python numpy excel scikit-learn jupyter-notebook pandas collections supervised-machine-learning pathlib balanced-random-forest smote-oversampler imbalanced-learn naive-random-oversampler cluster-centroids-undersampling easy-ensemble-classifier smoteenn-combination

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LJD0 / Credit_Risk_Analysis

Analysis of different machine learning models' performance on predicting credit default

python machine-learning numpy pandas smote balanced-random-forest smoteenn random-over-sampling cluster-centroid-undersampling easy-ensemble-classifier

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Tracari / Credit_Risk_Analysis

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

python machine-learning python-library machine-learning-algorithms scikitlearn-machine-learning precision-recall-curve precision-recall imbalance-learning balanced-random-forest balanced-accuracy-scores

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