Model buat TA Sentimen and Topik Berita Indonesia

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

Diabetes Prediction Using SVM Algorithm

BoW model in image classification

Here we have fully implemented a number of algorithms related to machine learning

The notebook contains Python code for various machine learning tasks and models. Here is an overview of its content:

Credit Score Classification in R using various algorithms

Support Vector Machine (SVM) is a supervised machine learning algorithm commonly used for classification and regression tasks. It works by finding the hyperplane that best separates the data into different classes.

This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.

Bu projede Breast Cancer data veri seti kullanılarak KNN (K Nearest Neighbor) SVM (Support Vector Machine) Naive Bayes algoritmalarıyla eğitim yapılmıştır. Eğitimde Gridsearch kullanılarak en optimum parametreler bulunmuştur.

In this work, an automatic and reproducible methodology is proposed using computer vision techniques for sorting oranges by size and defects. Master thesis written in Spanish.

This project aims to clarify the role of meta data in music genre classification and how helpful or hurtful it can be to music recommendation systems. Much experimentation was done with multiple different machine learning models and results were analysed and collated into a single academic paper

Using Machine Learning in predicting customer churn from bank credit card services

Data Mining | Machine Learning

Educational notebooks reviewing machine learning models and concepts.

Skin Cancer Detection: Leveraging Hybrid Deep Learning Models and Traditional Machine Learning Classifiers

Comparing logistic regression, decision tree, random forest, k-nearest neighbors, and SVMs in regard to binary prediction performance metrics.

This code performs email spam classification using three machine learning models: Naive Bayes, Support Vector Machines (SVM), and Random Forest Classifier. It evaluates their performance using accuracy scores and classification reports, ultimately identifying Random Forest Classifier as the best performer among the three.