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

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…

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

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…

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…

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.

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.

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.

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

Build and evaluate several machine learning algorithms to predict credit risk

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

Columbia FinTech Boot Camp Homework - Programs to utilize resampling and ensemble machine learning models to predict credit risk for retail loans.

Uses several machine learning models to predict credit risk.

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

Simulation of K-means Clustering algorithm using P5.JS

The purpose of this script is to predict credit risk by employing different techniques to train and evaluate models with unbalanced classes

Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.

Built and evaluated variety of supervised machine learning algorithms to predict credit risk.

This repo is about Machine Learning and Classification