GitHub topics: smote-sampling

Repositories

LokeshSreenathJ/Bankruptcy-Prediction---Analytics

Built XGBoost Classifier using SMOTE technique and Hyper-Parameter Tuning

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AayushSameerShah/SMOTE

This small repository contains the SMOTE implementation from scratch.

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TzeLun/SMOTE

A minority oversampling method for imbalance data set

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rishawsingh/Credit-Card-Fraud-Detection

System to tell apart the transaction was from the real user who owns the credit card or the transaction was from the stolen credit card.

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BibhuPrasadPanda97/Credit-Card-Default-Risk---AmExpert-CodeLab

Competition conducted by American Express on HackerEarth Platform to Predict Credit Card Defaulters by building Machine Learning Models for the given data.

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chinmaysharmacs10/University_Recommender

A model that recommends University based on details of an applicant.

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gulabpatel/Handle_Imbalance

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Wamuza1/Credit_Risk_Analysis

Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

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yandi-farinango/CreditRiskModel

Training XGBoost ML model to detect credit default risk. Used SMOTE technique for handling unbalanced data. Evaluation of model trained on unbalanced dataset vs SMOTE generated dataset

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GianRomani/ML_course_homework

Code and reports of the two homework for the Machine Learning course (Winter 2020)

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Olatohun/campaign-response

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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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tamannanazmin/Datathon

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sidharth-ds/Credit-Card-Default-prediction

EDA ---> Balancing the Dataset (SMOTE) ---> Feature engineering ---> Modelling with Hyperparameter Tuning

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gaetanoantonicchio/DataMining-2

Repository for "Data Mining - Advanced Topics and Applications" projects exam.

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Toshani/Credit-Card-Fraud-Detector

Mini project repository where we have implemented Credit card fraud detection using encoding, SMOTE-ing and KNN.

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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.

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kalyaniasthana/CS273A_project_diabetes

Course Project for CS273A: Machine Learning at UCI

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Desafio de Classificação do curso de Data Science e Machine Learning da Tera. Em um dataset de mais de 6 milhões de operações bancárias tinhamos um objetivo de realizar a previsão de fraudes. Fazendo uso de um processo de feature engineering que acrescentou 20 features ao modelo, combinado com um resampling feito através do método SMOTE. Para o treinamento criamos três modelos: Regressão Logística, Random Forest e XGBoost. Esses dois últimos performaram com precisão e recall superiores a 99%.

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akibzaman/Soil-nutrient-web

Prediction of basic soil nutrients (phosphorus, potassium, boron, calcium, magnesium and manganese) using reflectance from Hyperspectral Satellite Images (HSI).

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alexberndt/machine-learning-sandbox

Collection of machine learning algorithms ...

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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.

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izzypatrick21/cuisines

classification of asian and indian cuisines. A good example for resampling imbalance dataset for a classification project using interpolation. I have also included deploying machine learning model using Onnyx.

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lucacarniato/predicting-customer-churn-kaggle-competition

A solution to the Kaggle competition "Predicting Churning customers" (https://www.kaggle.com/sakshigoyal7/credit-card-customers)

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86lekwenshiung/Classification-Modelling-Projects

Classification Projects for balanced and imbalanced datasets

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jharvey09/Risky_Business_Peer_To_Peer_Lending

In this project, I will use credit risk models to assess the credit risk using peer-to-peer lending. Algorithms such as SMOTE, Naive Random Sampling, etc.

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ritwikkanodia/Privacy-Preserving-Machine-Learning

Maintaining the privacy of local server data in a federated learning framework using differential privacy by TensorFlow Privacy Library.

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desininja/Employee-Attrition-analysis

To know the main reasons for attrition of employees.

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WilliamSMendes/multiclass_students_classifier

A model for multiclass calssification, label ech student profile in Saint Paul School for predict the future profiles.

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jesussantana/Sampling

Perform Data Sampling with Python

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NataliaVelasquez18/credit-risk

The purpose of this study is to recommend whether PureLending should use machine learning to predict credit risk. Several machine learning models are built employing different techniques, then they are compared and analyzed to provide the recommendation.

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Rizwan-Hasan/Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis Fork of skinan/Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

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Related Keywords

smote-sampling 140 machine-learning 50 random-forest 37 logistic-regression 36 python 31 xgboost 23 smote 17 data-science 15 random-forest-classifier 14 classification 13 hyperparameter-tuning 13 oversampling 13 knn-classification 12 scikit-learn 11 undersampling 11 svm-classifier 10 decision-trees 10 feature-engineering 10 machine-learning-algorithms 8 data-visualization 8 neural-networks 7 eda 7 deep-learning 7 sklearn 7 outlier-detection 7 numpy 7 pandas 7 pca 6 exploratory-data-analysis 6 class-imbalance 6 visualization 5 seaborn 5 decision-tree-classifier 5 python3 5 xgboost-classifier 5 svm 5 imbalanced-data 5 confusion-matrix 5 random-under-sampling 5 gridsearchcv 4 matplotlib 4 neural-network 4 lightgbm 4 classification-model 4 sampling-methods 4 smoteenn 4 data-cleaning 4 supervised-machine-learning 4 tensorflow 4 data-analysis 4 credit-risk 4 optuna 4 keras-tensorflow 4 knn 4 oversampling-technique 4 unbalanced-data 3 fraud-detection 3 smoteen 3 feature-selection 3 multiclass-classification 3 sklearn-metrics 3 cross-validation 3 imbalanced-classification 3 deep-neural-networks 3 decision-tree 3 undersampling-technique 3 imbalanced-learning 3 ensemble-learning 3 adaboost 3 pipeline 3 imblearn 3 naive-bayes-classifier 3 bagging-ensemble 3 linear-regression 3 encoding 3 support-vector-machines 3 stratified-sampling 3 jupyter-notebook 3 gradient-boosting-classifier 3 tree-model 3 random-over-sampling 3 adasyn-sampling 3 matplotlib-pyplot 3 smote-oversampler 3 xgboost-model 3 ml 3 ann 3 classification-algorithm 3 credit-card 3 roc-auc-curve 2 adaboost-classifier 2 randomizedsearchcv 2 polycystic-ovary-syndrome 2 randomoversampler 2 classification-algorithims 2 pcos 2 prediction-model 2 creditcard 2 data-augmentation 2 boosting 2