#Knn-classification
Showing 11 of 11 repositories tagged #knn-classification, ranked by stars
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
:art: Color recognition & classification & detection on webcam stream / on video / on single image using K-Nearest Neighbors (KNN) is trained with color histogram features by OpenCV.
Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.
Source for the book Computer Science from Scratch
A simple, modern and scalable facial recognition based attendance system built with Python back-end & Angular front-end.
Machine Learning Project on Resume Screening using Python
Web application for engineering students to predict appropriate job roles using Machine learning and other guidance material like job descriptions, links to courses, etc.
The Smart Agriculture Advisory System is an application designed to provide farmers with personalized advice on crop management, pest control, and irrigation scheduling. By leveraging machine learning models, the system analyzes various environmental and soil parameters to recommend the most suitable crops for cultivation.
Pornhub Data Analysis and Porn Recommendation Algorithm
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.