Popular GitHub Repositories for Knn Classification

Web application for engineering students to predict appropriate job roles using Machine learning and other guidance material like job descriptions, links to courses, etc.

AUTOMATED TYPE CLASSIFICATION OF GLAUCOMA DETECTION USING DEEP LEARNING

Face recognition using dlib and kNN classification (ROS compatible)

Contains 5th Semester AIML Lab programs

The project is a simple sentiment analysis using NLP. The project in written in python with Jupyter notebook. It shows how to do text preprocessing (removing of bad words, stop words, lemmatization, tokenization). It further shows how to save a trained model, and use the model in a real life suitation. The machine learning model used here is k-Nearest Neighbor which is used to build the model. Various performance evaluation techniques are used, and they include confusion matrix, and Scikit-learn libraries classification report which give the accuracy, precision, recall and f1- score preformance of the model. The target values been classified are positive and negative review.

Insurance claim fraud detection using machine learning algorithms.

My exercises in the machine learning course

Recommendation Model using Milvus vector db

simple implementation of machine learning algorithm in pyhton

Python implementation of basic machine learning algorithms

Using machine learning for the study of music.

Implementation of Google Quick Draw doodle recognition game in PyTorch and comparing other classifiers and features.

NK-VECTOR, a powerful Natural Language Processing (NLP) library.

Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests for classification of Malaria Cells

Here you will find a Notebook with examples of various Machine Learning algorithms (ML), more specifically, Supervised and Unsupervised Learning examples. All of the code is followed by explanations and everything is easy to use and to understand thanks to the documentation.

A credit card fraud detection kernel

The dataset consists of data collected from heavy Scania trucks in everyday usage. The system in focus is the Air Pressure system (APS) which generates pressurised air that is utilized in various functions in a truck, such as braking and gear changes. The dataset’s positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS. So, I created a model where It can able to detect whether the APS is going to fail or Not.

CIND820 Capstone Project: Classification of Water Quality samples using ML

The Breast Cancer Wisconsin (Diagnostic) Dataset GitHub repository contains code and data for a machine learning model that predicts whether a breast mass is malignant or benign based on several features extracted from digitized images of a fine needle aspirate (FNA) of a breast mass.

NLP with LSTM for Sentiment Analysis of English texts