[freeCodeCamp] Book Recommendation K-Nearest Neighbor ML Jupyter Notebook
-
Updated
Oct 4, 2021 - Jupyter Notebook
[freeCodeCamp] Book Recommendation K-Nearest Neighbor ML Jupyter Notebook
This project involves detecting iris species using the k-nearest neighbors (KNN) algorithm in Jupyter Notebook. The iris species detection task is a classic problem in machine learning, where the goal is to classify iris flowers into different species based on their measurements.
This project focuses on predicting heart disease using the K-Nearest Neighbors (KNN) classification algorithm implemented in a Jupyter Notebook. It aims to provide a tool that can assist in early detection and diagnosis of heart disease based on given input features.
📘 This repository offers a complete K-Nearest Neighbors (KNN) tutorial, guiding you from core theory to hands-on practice. Learn to implement KNN from scratch with NumPy, apply it using scikit-learn, and explore visualizations, datasets, and Jupyter notebooks to fully understand, test, and optimize the algorithm.
This repository contains the Lab practices of Machine Learning performed in Jupyter Notebook using python language. This repo consists of KNN and SVM Classification models to perform classification on the iris dataset.
Add a description, image, and links to the k-nearest-neighbor-classifier topic page so that developers can more easily learn about it.
To associate your repository with the k-nearest-neighbor-classifier topic, visit your repo's landing page and select "manage topics."