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support-vector-machines

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This repository contains a detailed analysis of the Spambase Dataset using different classification algorithms, including Logistic Regression, Logistic Regression with Backward Feature Elimination (BFE), Support Vector Machine (SVM), SVM with Normalized Data, Decision Trees, Random Forest, K-Nearest Neighbors (K-NN), and K-NN with Normalized Data.

  • Updated May 28, 2024
  • HTML

This project aims to build a model to predict the truth of an article, hoax or non-hoax. Apart from that, this project also wants to identify the percentage of hoax and non-hoax articles.

  • Updated May 26, 2024
  • Jupyter Notebook

Predicting Credit Card Defaults This repository provides a step-by-step tutorial on predicting credit card defaults using machine learning algorithms in Python with scikit-learn. Learn to implement SVM, Random Forest, Decision Trees, k-Nearest Neighbors, and Artificial Neural Networks to forecast default payments for credit card clients.

  • Updated May 25, 2024
  • Jupyter Notebook

Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

  • Updated May 18, 2024
  • Python

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