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cluster-centroids

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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 th…

  • Updated Jan 24, 2021
  • Jupyter Notebook

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 c…

  • Updated Jun 27, 2021
  • Jupyter Notebook

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.

  • Updated Jun 17, 2024
  • Jupyter Notebook

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…

  • Updated Nov 19, 2020
  • Jupyter Notebook

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.

  • Updated Dec 7, 2020
  • Jupyter Notebook

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