GitHub topics: cluster-centroids

Repositories

KhushiRajurkar/Exoplanet-Habitability-Prediction Fork of SauravSJK/Exoplanet-Habitability-Prediction

Using the PHL Data to predict the habitability of an exoplanet

Language: Python - Size: 15.6 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 8.25 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 18.9 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 9 - Forks: 2

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)

Language: Jupyter Notebook - Size: 72.3 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

sarahm44/credit-risk-predictor

Uses several machine learning models to predict credit risk.

Language: Jupyter Notebook - Size: 9.54 MB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

timeamagyar/kdd-cup-99-python

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

Language: Jupyter Notebook - Size: 852 KB - Last synced at: almost 2 years ago - Pushed at: about 5 years ago - Stars: 21 - Forks: 12

cedoula/Credit_Risk_Analysis

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

Language: Jupyter Notebook - Size: 13.7 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 12

enj657/Credit_Risk_Analysis

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

Language: Jupyter Notebook - Size: 19.5 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 18.4 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 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

Language: Jupyter Notebook - Size: 18.9 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 626 KB - Last synced at: 12 months ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0