tomartushar/Credit-Card-Fraud-Detection

An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

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AnveshaM/Predicting_Customer_Churn_Neural_Network

A Comparative Study in Customer Churn Prediction through Multilayer Perceptrons and Support Vector Machines

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MiracleOny/Credit-Risk-Analysis

Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.

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jakeryu37/Stroke-Prediction-ML-Model

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sagartv/diabetes_risk_predictor

A FLASK-based web application that predicts the risk of diabetes based on the answers to a questionnaire. The app currently uses an XGBoost (Extreme Gradient Boosted Model) model trained on a Kaggle Dataset of CDC Data.

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fischlerben/Machine-Learning-Credit-Risk

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample the data. Evaluation metrics like the accuracy score, classification report and confusion matrix are generated to compare models and determine which suits this particular set of data best.

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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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weihaolun/Credit_Risk_Analysis

Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.

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Angienoelhaverly/Credit_Risk_Analysis

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

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domingosdeeulariadumba/MarketingCampaignPrediction2

Continuing with telemarketing model to predict campaign subscriptions in a portuguese bank institution. For this project I have evaluated the performance of four resampling techniques and selected the best one to implement the logistic model.

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jbalooshie/Credit_Risk_Analysis

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

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LJD0/Credit_Risk_Analysis

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

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trevor-leach803/Heart_Transplant_Model

Machine learning model to predict heart transplant failure and success using XGBoost algorithm and SMOTE/ENN to balance the dataset.

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MoinDalvs/Assignment_SVM_Forest_Fire_Prediction

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MoinDalvs/Assignment_Decision_Tree_2

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lingumd/Credit_Risk_Analysis

Machine learning models for predicting credit risk in LendingClub dataset.

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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.

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abidor13/linear_regression_salary

There are a number of classification algorithms that can be used to determine loan elgibility. Some algorithms run better than others. We built a loan approver using different Supervised Machine Learning algorithms and compared their accuracies and performances

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DangCoop/Credit_Risk_Analysis

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lilyhanhub/Credit_Risk_Analysis

Supervised Machine Learning project to predict credit risk

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tanmelissa/Credit_Risk_Analysis

Creating various machine learning models to create the most accurate model to predict credit risk

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annakthrnlee/Credit_Risk_Analysis

Using my skills in data preparation, statistical reasoning, and machine learning I employed different techniques to train and evaluate models with unbalanced classes.

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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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AlexGeiger1/Credit_Risk_Analysis

Python and sklearn are used to build and evaluate multiple machine learning models to predict credit risk.

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diercz/Credit_Risk_Analysis

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

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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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JennyJohnson78/Credit_Risk_Analysis

Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.

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shumph10/Credit_Risk_Analysis

Established a supervised machine learning model trained and tested on credit risk data through a variety of methods to establish credit risk based on a number of factor

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AJMnd/Credit_Risk_Analysis

An analysis on credit risk

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mdbinger/Credit_Risk_Analysis

Built, trained and evaluated multiple supervised machine learning algorithms to predict credit risk for loan applicants. Algorithms ran include Random Oversampler, SMOTE, Cluster Centroids, SMOTEENN, Balanced Random Forest Classifier, and Easy Ensemble Classifier.

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sarahrosegallagher/Credit_Risk_Analysis

Machine Learning models compared to find the strongest predictor for credit risk

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JayDS22/Churn-Prediction

Build an End-to-End Data Science Project to predict customer churn for the Telecom industry and provide prescriptive countermeasures.

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cedoula/Credit_Risk_Analysis

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

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enj657/Credit_Risk_Analysis

Built and evaluated several machine learning algorithms to predict credit risk.

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YonathanTE/Machine-Learning

Compared the effectiveness of the EasyEnsembleClassifier and LogisticRegression libraries. This was to assess the model with the best scores for balanced accuracy, recall, and geometric mean.

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existentialplantperson/Week_16

Week 16 - Decision Trees

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ZeroDarkHardy/Credit_Risk_Analysis

Train and test multiple Machine Learning models to predict risk based on consumer credit profiles.

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jose-perth/Credit_Risk_Analysis

Employ different techniques to train and evaluate models with unbalanced classes. Evaluate the performance of these models and make recommendations on their suitability to predict credit risk.

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AbdullahBera/Credit_Risk_Analysis

Built several supervised machine learning models to predict the credit risk of candidates seeking loans.

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Baylex/Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

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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

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carolinacraus/Credit_Risk_Analysis

The purpose of this script is to predict credit risk by employing different techniques to train and evaluate models with unbalanced classes

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arzuisiktopbas/Risky_Business

Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning

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DL777/Risky_Business

Using machine learning (ML) models to predict credit risk using data typically analysed by peer-to-peer lending services. Resampling data with SMOTE, Cluster Centroids, SMOTEENN and applying ensemble learning classifiers: Balanced Random Forest Classifier and Easy Ensemble Classifier.

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AaronGalloway/classification-homework

Using Resampling and Ensemble Learning to look at data and predict default rates on loans.

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vmieres/Machine-Learning

This repo is about Machine Learning and Classification

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bwacker1/Machine-Learning-Homework-Columbia-FinTech-Boot-Camp

Columbia FinTech Boot Camp Homework - Programs to utilize resampling and ensemble machine learning models to predict credit risk for retail loans.

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