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ER diagram of Library Management System

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ER Diagram is known as Entity-Relationship Diagram, it is used to analyze to the structure of the Database. It shows relationships between entities and their attributes. An ER Model provides a means of communication. The Library Management System database keeps track of readers with the following considerations – The system keeps track of the staff with a single point authentication system comprising login Id and password. Staff maintains the book catalog with its ISBN, Book title, price(in INR), category(novel, general, story), edition, author Number and details. A publisher has publisher Id, Year when the book was published, and name of the book. Readers are registered with their user_id, email, name (first name, last name), Phone no (multiple entries allowed), communication address. The staff keeps track of readers. Readers can return/reserve books that stamps with issue date and return date. If not returned within the prescribed time period, it may have a due date too. Staff also g

Develop k-Nearest Neighbors in Python From Scratch (Implementation of KNN)

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  In this tutorial you are going to learn about the   k-Nearest Neighbors algorithm  including how it works and how to implement it from scratch in Python ( without libraries ). A simple but powerful approach for making predictions is to use the most similar historical examples to the new data. This is the principle behind the k-Nearest Neighbors algorithm. After completing this tutorial you will know: How to code the k-Nearest Neighbors algorithm step-by-step. How to evaluate k-Nearest Neighbors on a real dataset. How to use k-Nearest Neighbors to make a prediction for new data. Kick-start your project  with my new book  Machine Learning Algorithms From Scratch , including  step-by-step tutorials  and the  Python source code  files for all example Develop k-Nearest Neighbors in Python From Scratch Image taken from  Wikipedia , some rights reserved. Tutorial Overview This section will provide a brief background on the k-Nearest Neighbors algorithm that we will implement in this tutoria