cluster-centroids

There are 0 repository under cluster-centroids topic.

timeamagyar / kdd-cup-99-python
Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn
kdd99 python sklearn matplotlib smote cluster-centroids resampling-methods principal-component-analysis linear-separability convex-hull one-hot-encode standardization normalization
Language:Jupyter Notebook 21
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.
python pandas modeling logistic-regression random-forest ensemble-machine-learning accuracy-score confusion-matrix classification-report resampling smote cluster-centroids smoteenn
Language:Jupyter Notebook 10
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
areeba0 / Image-Segmentation-with-Lazy-Snapping-and-K-Means-Clustering
This Jupyter notebook demonstrates image segmentation using Lazy Snapping and K-Means Clustering. It showcases how these algorithms can partition an image into segments based on pixel intensity and user-defined masks.
background-segmentation cluster-centroids euclidean-distance foreground-extraction image-segmentation jupyter-notebook k-means-clustering lazy-snapping matplotlib numpy pandas python likelihood-computation seed-pixels
Language:Jupyter Notebook 1
devinaa1604 / Credit_Risk_Analysis_Using_Classification
This project aims to predict credit risk using various ensemble machine learning techniques. I have also tried to handle imbalance by using various sampling methods.
logistic-regression imbalanced-learning balanced-accuracy-scores imbalance-classification smote-sampling cluster-centroids smoteenn ensemble-learning
Language:Jupyter Notebook 1
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.
classification-report credit-risk tp-fp balanced-accuracy-scores smote-sampling randomoversampler cluster-centroids random-forest
Language:Jupyter Notebook 1
vaitybharati / P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-
Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)
python numpy sklearn pandas scatter-plot normalize clustering-algorithm kmeans-clustering wcss sum-of-squares elbow-plot matplotlib-pyplot cluster-centroids standard-scaler kmeans-algorithm
Language:Jupyter Notebook 1
BenMejedAmen / Default-Credit-Card-Payment-Project
boxplot cluster-centroids imblearn isolation-forest local-outlier-factor machine-learning matplotlib-pyplot numpy pandas pca scipy-stats seaborn sklearn kmeans-smote adaboostclassifier gridsearchcv kneighborsclassifier random-forest-classifier xgboost-classifier f-score
Language:Jupyter Notebook 0
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
smoteenn random-forest machine-learning-algorithms oversampling undersampling cluster-centroids
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
enj657 / Credit_Risk_Analysis
Built and evaluated 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
minut9 / Credit_Risk_Analysis
Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk
machine-learning-algorithms machine-learning logistic-regression smote oversampling undersampling clustering cluster-centroids randomoversampler smoteenn jupyter-notebook python
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.
balanced-random-forest cluster-centroids ensemble-model logistic-regression oversampling randomoversampler smote-sampling smoteenn supervised-machine-learning undersampling
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
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.
adaboost cluster-centroids credit-risk ensemble-learning imblearn oversampling python random-forest resampling sklearn smote smoteenn
Language:Jupyter Notebook
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.
machine-learning ensemble-learning random-forest-classifier easy-ensemble-classifier smote cluster-centroids smoteenn credit-risk
Language:Jupyter Notebook
kaustubholpadkar / K-means_Clustering-Simulation
Simulation of K-means Clustering algorithm using P5.JS
kmeans-clustering-simulation machine-learning clustering-algorithm simulation cluster-centroids unsupervised-machine-learning javascript-library p5js p5-sketches interactive-visualizations
Language:JavaScript
vmieres / Machine-Learning
This repo is about Machine Learning and Classification
artificial-intelligence classification-report cluster-centroids credit-risk ensemble-classifier ensemble-machine-learning evaluating-models imbalance-classification logistic-regression machine-learning naive-random-oversampler oversampling resampling sklearn-classify sklearn-library smoteenn undersampling
Language:Jupyter Notebook

cluster-centroids

timeamagyar / kdd-cup-99-python

fischlerben / Machine-Learning-Credit-Risk

cedoula / Credit_Risk_Analysis

areeba0 / Image-Segmentation-with-Lazy-Snapping-and-K-Means-Clustering

devinaa1604 / Credit_Risk_Analysis_Using_Classification

shaunwang1350 / CreditLoans_MachineLearning

vaitybharati / P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-

BenMejedAmen / Default-Credit-Card-Payment-Project

carolinacraus / Credit_Risk_Analysis

DSupps / Credit_Risk_Analysis

enj657 / Credit_Risk_Analysis

minut9 / Credit_Risk_Analysis

NedaAJ / Credit_Risk_Analysis

nhafer88 / Credit_Risk_Analysis

sarahm44 / credit-risk-predictor

shayanafzal / Credit_Risk_Analysis

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

DL777 / Risky_Business

kaustubholpadkar / K-means_Clustering-Simulation

vmieres / Machine-Learning