What is knn? How is knn done? Why is knn needed?

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

Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department

DataSet builder with scraping and data warehouse analysis

Scripts for machine learning algorithms in MATLAB/Octave and python

Detailed Data Science using Python-Jupyter Notebook ( Data Analysis using Pandas and NumPy, Visualization using plotly express, Exploratory Data Analysis, Supervised ML models: Linear Regression, KNN, Logistic Regression, Support Vector Machine, Decision Trees Ensemble Models: Voting Bootstrap/ Bagging Aggregation, Unsupervised: K-Means

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.

No amount of theory can replace hands-on practice. Textbooks and lessons can lull you into a false belief of mastery because the material is there in front of you. But once you try to apply it, you might find that it’s harder than it looks.👩‍🔬📗📊

Real-Time spontaneous abortion prediction and Health assessment of Pregnant women using Machine Learning and IoT.

To check the data belongs to which class of Iris plant. (Famous data Set: 'Iris.csv')

This Repository contains cognitiveai class's all practice labs.

Machine Learning Models

빅데이터 분석을 이용한 생물 데이터 분석

A K Nearest Neighbors classifier developed from scratch for self-learning purposes. Accuracy is off the charts, since we have full control on the algorithm.

Identifying Image Orientation using Supervised Machine Learning Models of k-Nearest Neighbors, Adaboost and Multi-Layer Feed-Forward Neural Network trained using Back-Propagation Learning Algorithm

Collection of Python scripts

Predicting the results of the given transactions by classification

knn implemented on a dataset from kaggle

K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification. KNN tries to predict the correct class for the test data by calculating the distance between the test data and all the training points. Then select the K number of points which is closet to the test data.