Image classification in the gastrointestinal tract with KNN and CNN

Demonstrating different Machine Learning Model

Credit Card Fraud Detection: Study and Implementation

Esse pequeno projeto tem como objetivo fazer testes de acurácia com rede neural com apenas um neurônio sem classificadores e com 2 (dois) classificadores, sendo eles KNN e 1R.

This project is an application for classifying the quality of coconuts using the K Nearest Neighbors algorithm. It is built with Streamlit for easy deployment.

Compared the metrics and performance of different classification algorithms on Heart Failure dataset from UCI ML Repository

Using Machine Learning to rank a list of customers most likely to buy a Car Insurance for a cross-sell campaign.

A Python library of 'old school' machine learning methods such as linear regression, logistic regression, naive Bayes, k-nearest neighbors, decision trees, and support vector machines.

A swift implementation of a KNearestNeighbour Classifier in swift.

Data Science - K-Nearest Neighbors (KNN) Work

Project 1 for Introduction to Machine Learning course.

Self Work Coding Files related to Data Science

This repository has a code (function) for K-Nearest Neighbours models. The model is tested on a dataset and compared with the slkearn KNN models. There is runtime analysis and accuracy analysis of the sklearn KNN models for classification and regression.

In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.

Flight_Price Prediction using Machine Learning.(Regression Use Case)

This is a repository to document my progress in learning the basics of common machine learning algorithms.

In this repository I am gonna show the main and most popular non-supervised clustering algorithms with short explanations.

The purpose of this project is to promote understanding -- my own and others' -- of fundamental data science and machine learning concepts and tools. It currently consists of one notebook that classifies fruit types based on weight, volume, and image data.

MNIST Digit Classification using an ANN, k-Nearest Neighbour and hybrid of the two.