randomoversampler

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.

cedoula/Credit_Risk_Analysis

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

abdoghareeb46/NTI-Final-Assignment

NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

ChaitanyaC22/Fraud_Analytics_Credit_Card_Fraud_Detection

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

Robertfnicholson/Credit_Risk_Analysis

Developed Machine Learning Models to Predict Credit Risk

Sanushi-Salgado/Tumor-Teller-Prediction-Module

Prediction module for Tumor Teller - primary tumor prediction system

abhijha3011/Techniques-To-Handle-Imbalanced-Data

Different Techniques to Handle Imbalanced Data Set

Malvi1497/Credit_Risk_Analysis

To evaluate the performance of supervised machine learning models to make a written recommendation on whether they should be used to predict credit risk.

Priya2216/Insurance_Claim_Prediction-Neural-Network

rutujahingankar/Health-insurance-cross-sell-prediction

Predict Health Insurance Owners who will be interested in Vehicle Insurance

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.

AlexGeiger1/Credit_Risk_Analysis

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

ashwinihegde28/Credit_Risk_Analysis

Credit_Risk_Analysis using Machine Learning

blleshi/Credit_Risk_Classification

Credit Risk Classification

caseygomez/Credit_Risk_Analysis

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

DangCoop/Credit_Risk_Analysis

diercz/Credit_Risk_Analysis

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

echoqshen/Credit_Risk_Analysis

Use different techniques to train and evaluate different machine learning models to predict credit risk with unbalanced classes

Gsilvera24/Credit-Risk-Analysis

Credit Worthyness Analysis using Linear Regression

helenaschatz/credit-risk-classification

Using various techniques to train and evaluate a model based on loan risk. Also, using a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.

kaylah176/Credit-Risk-Classification

Objective: Address the classification problem behind predicting credit risk

lingumd/Credit_Risk_Analysis

Machine learning models for predicting credit risk in LendingClub dataset.

minut9/Credit_Risk_Analysis

Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk

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.

NeonOstrich/Autism-Diagnosis-with-Linear-Regression-and-Neural-Networks-using-Random-Oversampling

We used a dataset that included birth and personal data as well as Autism Spectrum Quotient test scores to train machine learning algorithms to predict autism. We used Logistic Regression, Neural Network Models and Keras Tuner with Random Oversampling to train one with 90% accuracy.

Nusratnimme/Credit_Risk_Analysis

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

Priya2216/DataScience-MachineLearning-Project

Data Science Major Project Completed in IT Vedant Institute using Machine learning algorithms

PriyankaSett/Identifying_gamma_hadron_magic_dataset

Identifying rare event.

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.

twigikit/regression-modelling

Logistic regression model with train_test_split data

bhaskrr/amazon-books-4-class-rating-prediction

This repository holds the dataset and notebooks for the Amazon Books dataset 4 class Rating prediction

bhaskrr/restaurant-reviews-5-class-rating-prediction-

This repo contains the dataset and notebook for the kaggle restaurant reviews five class rating prediction

harmanveer-2546/Rainfall-Prediction-using-Machine-Learning

Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department’s prediction fails sometimes. In this project, I learn how to build a machine learning model which can predict whether there will be rainfall today or not based on some atmospheric factors.

Harmanveer2546/Rainfall-Prediction-using-Machine-Learning-

Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department’s prediction fails sometimes. In this project, I learn how to build a machine learning model which can predict whether there will be rainfall today or not based on some atmospheric factors.

saraparveen26/SupervisedLearning---Credit-Risk-Classification

This project trains and avaluates machine learning model to identify creditworthiness of borrowers and classify credit risk predictions for a peer-to-peer lending services company.