Octree/Quadtree/N-dimensional linear tree

Knn implementation without K parameter

An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.

C++ implementation for machine learning algorithm K-NN

A simple implementation of K Nearest Neighbors classifier model in python.

A semantic search engine to find questions semantically similar to given query using Elastic Search, Tensorflow, Universal Sentence Encoder, AWS Lambda, API Gateway

Machine learning and Deep learning project

Predicting the champion of the 2023 Cricket World Cup through the implementation of the Random Forest algorithm.

AU331机器学习与知识发现课程项目——拍照矩阵计算器开发

Building a ML model that can predicts the species of the flower from the measurements of the petal and the sepal

Recommendation System Using K-Nearest Neighbors .

Here we are making a predictive system to measure the sentiment of each review or tweet, whether it is 1 (Positive Sentiment) or 0 (Negative Sentiment). In this work, LGBM Classifier, XGBooost Classifier, CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, K-Nearest Neighbors, and Logistic Regression are used.

Image classification in the gastrointestinal tract with KNN and CNN

A swift implementation of a KNearestNeighbour Classifier in swift.

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.

Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees

Project 1 for Introduction to Machine Learning course.

Self Work Coding Files related to Data Science

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.

n this project I used different regression algorithms to predict flight delay. I used Kaggles free GPUs and Datasets from Zindi in this project. Those different algorithms include random forrest, decision tree, xgboost and so on. Initially I used feature engineering and used data visualization techniques to get my data into the best shape.