balanced-random-forest

There are 1 repository under balanced-random-forest 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.
jupyter-notebook python imbalanced-data imbalanced-learning data-visualization target-encoding imblearn sklearn balanced-random-forest
Language:Jupyter Notebook 10
Insurance-Claim-Fraud-Detection
nirab25 / Insurance-Claim-Fraud-Detection
Insurance claim fraud detection using machine learning algorithms.
insurance-claims machine-learning naive-bayes-classifier mlp-classifier descision-tree knn-classifier linear-discriminant-analysis knn-classification random-forest balanced-random-forest xgboost neural-network
Language:Jupyter Notebook 8
cedoula / Credit_Risk_Analysis
Build and evaluate several machine learning algorithms to predict credit risk.
machine-learning oversampling undersampling randomoversampler smote machine-learning-algorithms cluster-centroids smoteenn balanced-random-forest ensemble-model logistic-regression
Language:Jupyter Notebook 6
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 imbalanced-learning scikit-learn smote random-forest balanced-random-forest credit-risk ensemble-learning oversampling undersampling machine-learning supervised-learning
Language:Jupyter Notebook 4
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
Baylex / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
machine-learning imbalanced-learning scikit-learn random-over-sampling smote-algorithm cluster-centroids-algorithm smoteenn balanced-random-forest easy-ensemble-classifier
Language:Jupyter Notebook 1
Kenner82 / Credit_Risk_Analysis
Testing 6 different machine learning models to determine which is best at predicting credit risk.
ada-boost-classifier balanced-random-forest cluster-centroid-undersampling ensemble-learning naive-random-oversampler oversampling-algorithms smote-oversampler smoteenn-combination combination-sampling-algorithms undersampling-algorithms pandas python supervised-machine-learning
Language:Jupyter Notebook 1
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
Akotovets1 / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
adaboost balanced-random-forest cluster-centroids-algorithm confusion-matrix easy-ensemble-classifier imbalanced-learning machine-learning random-over-sampling scikitlearn-machine-learning smote-algorithm supervised-machine-learning
Language:Jupyter Notebook 0
BaileeRice / Credit_Risk_Analysis
using machine learning to assess credit risk
machine-learning balanced-random-forest easy-ensemble-classifier naive-random-oversampler smote-oversampler undersampling
Language:Jupyter Notebook 0
BiancaTaisePommerening / Credit_Risk_Analysis-
Supervised Machine Learning
balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier imbalanced-learning machine-learning random-over-sampling scikit-learn smote smoteenn-combination pandas-dataframe python3
Language:Jupyter Notebook 0
cbrito3 / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
ada-boost-classifier balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier imbalance-learning machine-learning machine-learning-algorithms naive-random-oversampler over-sampling precision-recall scikit-learn scikitlearn-machine-learning smote-oversampler under-sampling
Language:Jupyter Notebook 0
cmmgw / Credit_Risk_Analysis
python numpy pandas pathlib collections scikit-learn imbalanced-learn supervised-machine-learning naive-random-oversampler smote-oversampler cluster-centroids-undersampling smoteenn-combination balanced-random-forest easy-ensemble-classifier excel jupyter-notebook
Language:Jupyter Notebook 0
DSupps / 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
Language:Jupyter Notebook 0
dw251414 / 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 clustercentroids randomoversampler balanced-random-forest smoteenn
Language:Jupyter Notebook 0
enj657 / Credit_Risk_Analysis
Built and evaluated several machine learning algorithms to predict credit risk.
balanced-random-forest cluster-centroids ensemble-model logistic-regression machine-learning machine-learning-algorithms oversampling randomoversampler smote smoteenn undersampling
Language:Jupyter Notebook 0
eric-blankinshp / Credit_Risk_Analysis_Supervised_ML
About 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. Topics
balanced-random-forest imbalanced-learning machine-learning python scikit-learn smote smoteen unbalanced-data easy-ensemble-classifier
Language:Jupyter Notebook 0
jbalooshie / Credit_Risk_Analysis
Testing various supervised machine learning models to predict a loan applicant's credit risk.
balanced-random-forest clustercentroids credit-risk easyensembleclassifier randomoversampler smote smoteenn
Language:Jupyter Notebook 0
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 balanced-random-forest easy-ensemble-classifier stochastic-gradient-descent
Language:Jupyter Notebook 0
Jimmygjr10 / Credit_Risk_Analysis
anaconda balanced-random-forest classification confusion-matrix elbow-curves jupyter-notebooks kmeans-clustering machine-learning-algorithms python3 smote smoteenn
Language:Jupyter Notebook 0
LJD0 / Credit_Risk_Analysis
Analysis of different machine learning models' performance on predicting credit default
balanced-random-forest cluster-centroid-undersampling easy-ensemble-classifier machine-learning numpy pandas python random-over-sampling smote smoteenn
Language:Jupyter Notebook 0
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.
machine-learning scikit-learn imbalanced-learn unbalanced-data python easy-ensemble-classifier balanced-random-forest smote smoteenn
Language:Jupyter Notebook 0
NedaAJ / Credit_Risk_Analysis
Build and evaluate several machine learning algorithms to predict credit risk
balanced-random-forest cluster-centroids logistic-regression machine-learning machine-learning-algorithms oversampling smote smoteenn undersampling
Language:Jupyter Notebook 0
nhafer88 / Credit_Risk_Analysis
Built and evaluated variety of supervised machine learning algorithms to predict credit risk.
supervised-machine-learning logistic-regression ensemble-model smote-sampling oversampling undersampling cluster-centroids randomoversampler balanced-random-forest smoteenn
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
Peteresis / Credit_Risk_Analysis
Using Supervised Machine Learning algorithms to identify credit risks
machine-learning machine-learning-algorithms resampling-methods balanced-random-forest centroids ensemble-classifier ensemble-learning python smote smoteen jupyter-notebook pandas
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
Language:Jupyter Notebook 0
credit-risk-predictor
sarahm44 / credit-risk-predictor
Uses several machine learning models to predict credit risk.
balanced-random-forest cluster-centroids cluster-centroids-undersampling credit-risk easy-ensemble-classifier ensemble-learning fintech logistic-regression machine-learning naive-random-oversampler random-forest resampling smote smote-oversampler smoteenn-combination
Language:Jupyter Notebook 0
Credit_Risk_Analysis
shayanafzal / Credit_Risk_Analysis
Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.
cluster-centroids random-over-sampling smote balancedrandomforestclassifier balanced-random-forest easy-ensemble-classifier
Language:Jupyter Notebook 0
showkatewang / Credit_Risk_Analysis
Predicts credit risk of individuals based on information within their application utilizing supervised machine learning models
balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier machine-learning naive-random-oversampler python synthetic-minority-over-sampling
Language:Jupyter Notebook 0
Tracari / Credit_Risk_Analysis
Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk
python balanced-accuracy-scores balanced-random-forest imbalance-learning machine-learning machine-learning-algorithms python-library scikitlearn-machine-learning precision-recall precision-recall-curve
Language:Jupyter Notebook 0
ZeroDarkHardy / Credit_Risk_Analysis
Train and test multiple Machine Learning models to predict risk based on consumer credit profiles.
machine-learning machine-learning-algorithms smote smoteenn logistic-regression ensemble-model oversampling undersampling balanced-random-forest
Language:Jupyter Notebook 0
annette-blackburn / Credit_Risk_Analysis
Supervised Machine Learning Project
balanced-random-forest easy-ensemble-classifier imbalanced-learning machine-learning naive-random-oversampler scikit-learn smote-oversampler undersampling combination-under-over-sampling
Language:Jupyter Notebook
inregards2pluto / credit-risk-analysis
Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.
cluster-centroid-undersampling ensemble-model machine-learning naive-random-oversampling smote-oversampling smoteenn-combination-sampling adaboost-classifier balanced-random-forest
Language:Jupyter Notebook
jamesmoonusa / Credit_Risk_Analysis
Predicting Credit Risk by Using Several Machine Learning algorithms
machine-learning logistic-regression balanced-random-forest
Language:Jupyter Notebook
lawrencegoodwyn / LendingClub-Risk-Analysis
This repo contains code that looks into LendingClub's membership data and employs ML to see if the model can predict a user's "credit risk" based on lending.
machine-learning smote-sampling balanced-random-forest adaboost-algorithm python
Language:Jupyter Notebook

balanced-random-forest

sharmaroshan / Fraud-Detection-in-Insurace-Claims

nirab25 / Insurance-Claim-Fraud-Detection

cedoula / Credit_Risk_Analysis

Deving789 / Credit_Risk_Analysis

daiphuongngo / Banking-Dataset-Imbalanced-Learn-Comparison

Baylex / Credit_Risk_Analysis

Kenner82 / Credit_Risk_Analysis

abidor13 / Credit_Risk_Analysis

Akotovets1 / Credit_Risk_Analysis

BaileeRice / Credit_Risk_Analysis

BiancaTaisePommerening / Credit_Risk_Analysis-

cbrito3 / Credit_Risk_Analysis

cmmgw / Credit_Risk_Analysis

DSupps / Credit_Risk_Analysis

dw251414 / Credit_Risk_Analysis

enj657 / Credit_Risk_Analysis

eric-blankinshp / Credit_Risk_Analysis_Supervised_ML

jbalooshie / Credit_Risk_Analysis

jharvey09 / solana_sentinment_analysis

Jimmygjr10 / Credit_Risk_Analysis

LJD0 / Credit_Risk_Analysis

Mishkanian / Credit_Risk_Analysis

NedaAJ / Credit_Risk_Analysis

nhafer88 / Credit_Risk_Analysis

Nusratnimme / Credit_Risk_Analysis

Peteresis / Credit_Risk_Analysis

ramya-ramamur / Credit_Risk_Analysis

sarahm44 / credit-risk-predictor

shayanafzal / Credit_Risk_Analysis

showkatewang / Credit_Risk_Analysis

Tracari / Credit_Risk_Analysis

ZeroDarkHardy / Credit_Risk_Analysis

annette-blackburn / Credit_Risk_Analysis

inregards2pluto / credit-risk-analysis

jamesmoonusa / Credit_Risk_Analysis

lawrencegoodwyn / LendingClub-Risk-Analysis