Python KNN---KMean Resources

Product-based-recommendation-system A product based recommendation system which

Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them.

Built various Machine Learning algorithms (Logistic Regression, Random Forest, KNN, Gradient Boosting and XGBoost. etc). Structured a custom ensemble model and a neural network. Found a outperformed model for heart failure prediction accuracy of 88 percent.

GPU implementation of kNN and SNN GPU implementation of $k$-Nearest Neighbors and Shared-Nearest Neighbors Supported by numba cuda and faiss library E

This project has Classification and Clustering done Via kNN and K-Means respectfully. It later tests its efficiency via F1/accuracy/recall/precision for kNN and Davies-Bouldin Index for Clustering. The Data is also visually represented.

K Nearest Neighbors (KNN) algorithm In this Machine Learning world, there are various algorithms designed for classification problems such as Logistic

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

FPT_data_centric_competition - Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

Make Sure the dataset file "spamData.mat" is in the folder spam\src Environment: Python --version = 3.7 Third Party: numpy, matplotlib, math, scipy

The value of international students to the United States. Probability of getting a non-immigrant visa. Project timeline: Jan 2021 - April 2021 Project

KNN With Spark Implementation of KNN using PySpark. The KNN was used on two separate datasets (https://archive.ics.uci.edu/ml/datasets/iris and https:

kNN_From_Scratch I implemented the k nearest neighbors (kNN) classification algorithm on python. This algorithm is used to predict the classes of new

Machine Learning Algorithms Machine Learning algorithms implementation. What can I find here? ML Algorithms KNN K-Means-Clustering SVM (MultiClass) Pe

PyNNDescent PyNNDescent is a Python nearest neighbor descent for approximate nearest neighbors. It provides a python implementation of Nearest Neighbo

Non-Metric Space Library (NMSLIB) Important Notes NMSLIB is generic but fast, see the results of ANN benchmarks. A standalone implementation of our fa

crab-knn implementation of the KNN algorithm in Python applied to biometrics data of purple rock crabs (leptograpsus variegatus) to classify the sex o

kNN-vs-RFR My project contrasts K-Nearest Neighbors and Random Forrest Regressors on Real World data In many areas, rental bikes have been launched to

Auto-Baby-Cry-Detection-with-Music-Player A simple voice detection system which can be applied practically for designing a device with capability to d

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

Industrial KNN-based Anomaly Detection ⭐ Now has streamlit support! ⭐ Run $ streamlit run streamlit_app.py This repo aims to reproduce the results of

Fast k-NN graph construction for slow metrics

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

MSF Official code for "Mean Shift for Self-Supervised Learning" Requirements Python = 3.7.6 PyTorch = 1.4 torchvision = 0.5.0 faiss-gpu = 1.6.1 In

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install

README: python-timbl Authors: Sander Canisius, Maarten van Gompel Contact: [email protected] Web site: https://github.com/proycon/python-timbl/ pytho