easyensembleclassifier

There are 0 repository under easyensembleclassifier topic.

daiphuongngo / Banking-Dataset-Imbalanced-Learn-Comparison
Banking-Dataset-Marketing-Targets
machine-learning classification keras deep-learning scikit-learn deep-neural-networks decision-tree random-forest balanced-random-forest bagging-ensemble balancedbaggingclassifier rusboost xgboost easyensembleclassifier tensorflow
Language:Jupyter Notebook 3
Robertfnicholson / Credit_Risk_Analysis
Developed Machine Learning Models to Predict Credit Risk
balancedrandomforestclassifier clustercentroids clustering easyensembleclassifier machine-learning python pythonlibrarires random-forest randomoversampler smote smoteenn
Language:Jupyter Notebook 3
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).
imbalanced-learning machine-learning scikit-learn smote balancedrandomforestclassifier clustercentroids easyensembleclassifier smoteenn
Language:Jupyter Notebook 2
AJMnd / Credit_Risk_Analysis
An analysis on credit risk
balancedrandomforestclassifier clustercentroids easyensembleclassifier get-dummies imbalanced-learning imblearn logistic-regression python random-over-sampling scikit-learn smote smoteenn supervised-machine-learning
Language:Jupyter Notebook 1
daiphuongngo / Porto-Seguro-Safe-Driver-Prediction-Imbalanced-Comparison-Classifiers
machine-learning scikit-learn random-forest classification keras tensorflow deep-learning deep-neural-networks xgboost decision-tree rusboost bagging-ensemble balancedbaggingclassifier balancedrandomforestclassifier easyensembleclassifier
Language:Jupyter Notebook 1
DougUOT / Credit_Risk_Analysis
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.
machine-learning python pandas numpy scikit-learn jupyter-notebook randomoversampler logistic-regression confusion-matrix classification-report smote-oversampler balancedrandomforestclassifier easyensembleclassifier centroids smoteenn
Language:Jupyter Notebook 1
Ekanpat / Credit_Risk_Analysis
Analyzing credit card risk with machine learning models!
balance-accuracy clean-code confusion-matrix credit-card-risk easyensembleclassifier lending-club machinelearning-python peer-to-peer precision recall-scores
Language:Jupyter Notebook 1
Rl16193 / Credit_Risk_Analysis
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,
easyensembleclassifier machine-learning oversampling randomforestclassifier supervised-learning undersampling
Language:Jupyter Notebook 1
AbdullahBera / Credit_Risk_Analysis
Built several supervised machine learning models to predict the credit risk of candidates seeking loans.
machine-learning python jupyter-notebook smote randomove clustercentroids balancedrandomforestclassifier smoteenn easyensembleclassifier
Language:Jupyter Notebook 0
abidor13 / Credit_Risk_Analysis
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.
balanced-random-forest classification clustercentroids easyensembleclassifier machine-learning oversampling resampling sklearn smote smoteenn
Language:Jupyter Notebook 0
ashwinihegde28 / Credit_Risk_Analysis
Credit_Risk_Analysis using Machine Learning
machine-learning python scikit-learn balancedrandomforestclassifier easyensembleclassifier imbalanced-learning randomoversampler smote-algorithms
Language:Jupyter Notebook 0
caseygomez / Credit_Risk_Analysis
Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.
balancedrandomforestclassifier clustercentroids easyensembleclassifier imbalanced-learning python randomoversampler scikit-learn smote smoteenn supervised-machine-learning
Language:Jupyter Notebook 0
diercz / Credit_Risk_Analysis
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
clustercentroids creditrisk-analysis easyensembleclassifier machine-learning random-forest-classifier randomoversampler smote-oversampler smoteenn
Language:Jupyter Notebook 0
DylanSteinhauer / Credit_Risk_Analysis
Train and evaluate models to determine credit card risk using a credit card dataset
machine-learning randomoversampler smote clustercentroids balancedrandomforestclassifier easyensembleclassifier sklearn
Language:Jupyter Notebook 0
jbalooshie / Credit_Risk_Analysis
Testing various supervised machine learning models to predict a loan applicant's credit risk.
credit-risk randomoversampler smote smoteenn easyensembleclassifier clustercentroids balanced-random-forest
Language:Jupyter Notebook 0
JennyJohnson78 / Credit_Risk_Analysis
Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.
machine-learning randomoversampler smote clustercentroids smoteenn balancedrandomforestclassifier easyensembleclassifier
Language:Jupyter Notebook 0
lingumd / Credit_Risk_Analysis
Machine learning models for predicting credit risk in LendingClub dataset.
balancedrandomforestclassifier cluster-centroids-undersampling easyensembleclassifier imbalanced-learn machine-learning randomoversampler smote smoteenn confusion-matrix classification-report matplotlib-pyplot numpy pandas pathlib scikit-learn google-colab get-dummies
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nicoserrano / Credit_Risk_Analysis
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
machine-learning smote-oversampler randomoversampler clustercentroids smoteenn random-forest-classifier easyensembleclassifier creditrisk-analysis
Language:Jupyter Notebook 0
Nusratnimme / Credit_Risk_Analysis
Build and evaluate several machine learning algorithms by resampling models to predict credit risk.
balanced-random-forest easyensembleclassifier machine-learning randomoversampler smote smoteenn undersampling
Language:Jupyter Notebook 0
Credit_Risk_Analysis
ramya-ramamur / Credit_Risk_Analysis
Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn
random-forest ensemble-learning smote-oversampler smoteenn-combination easyensembleclassifier cluster-centroid-undersampling naive-random-oversampler balanced-random-forest python scikit-learn imbalanced-learning
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sjwedlund / Credit_Risk_Analysis
Apply machine learning to solve the challenge of credit risk
machine-learning imbalanced-learning scikit-learn randomoversampler smote clustercentroids smoteen balancedrandomforestclassifier easyensembleclassifier
Language:Jupyter Notebook 0
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.
clustercentroids logistic-regression machine-learning smote smoteenn balancedrandomforestclassifier easyensembleclassifier randomoversampler
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weihaolun / Credit_Risk_Analysis
Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.
supervised-machine-learning imbalanced-learning scikit-learn randomoversampler smote clustercentroids smoteenn balancedrandomforestclassifier easyensembleclassifier
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easyensembleclassifier

daiphuongngo / Banking-Dataset-Imbalanced-Learn-Comparison

Robertfnicholson / Credit_Risk_Analysis

stephperillo / Credit_Risk_Analysis

AJMnd / Credit_Risk_Analysis

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

DougUOT / Credit_Risk_Analysis

Ekanpat / Credit_Risk_Analysis

Rl16193 / Credit_Risk_Analysis

AbdullahBera / Credit_Risk_Analysis

abidor13 / Credit_Risk_Analysis

ashwinihegde28 / Credit_Risk_Analysis

caseygomez / Credit_Risk_Analysis

diercz / Credit_Risk_Analysis

DylanSteinhauer / Credit_Risk_Analysis

jbalooshie / Credit_Risk_Analysis

JennyJohnson78 / Credit_Risk_Analysis

lingumd / Credit_Risk_Analysis

nicoserrano / Credit_Risk_Analysis

Nusratnimme / Credit_Risk_Analysis

ramya-ramamur / Credit_Risk_Analysis

sjwedlund / Credit_Risk_Analysis

SohrabRezaei / Credit-Risk-Analysis

weihaolun / Credit_Risk_Analysis