Streamline Your Projects with a Construction Receipt Template for R&D

Construction receipt template for R&D

hi and welcome to the second section of this course build your first recommendation engine in the previous section we had an introduction to various types of recommendation engines which we will be building in the subsequent sections now that we've got introduced to recommendation engines let's build our first recommendation engine using R the steps to build our basic Commendation engine are as follows loading and formatting data calculating similarity between users predicting the unknown ratings for users now we'll see the first video of section two loading and formatting data in this video we'll learn how to load and format data before we proceed F Implement let's have a brief discussion about the required software in the package for building our first us thisise for this exercise we have used the r 3.2.2 version and R Studio 0.99 ores for inst set up of RNR Studio please refer to the software they list of the book shape two the in order to our package in our studio packages on the right side of work and select packages click on and type the package name that's to be installed already inst I've already installed the data so let's reshape 2 click on this will take few time to the r package so package has been installed package in order to oury go to package and click on packages now import the packages from the packages window which is showing here this will the r-shaped package loading and data set used for this section can be downloaded from on this site the data set chosen for the chapter is a movie rating data containing ratings for six movies given by six users on a scale given by first user six movies second user six movies third user six movies fourth user six movies us fifth user six movies sixth user six movies before we before we Lo the data let me explain a few things about dat the data set chosen is a Comm separated file having movie ratings from 1 to in a step of given by six users movies all critics have all titles intive is to build unknown movies to ERS based on the ratings of is loading the data loading the data from a CSV file in is given by before that we need to import the packages by clicking on the check boxes from the packages tab you've imported the pack check boxes you have now type this the first rad of the data an inbuilt function in R to see the diens of data we use and inbuilt function in R to see the structure of the input data the function as we see that we have a DAT data cons of1 title and also we see critics have rated and the ratings are to see the levels of the attributes levels method in R to build a recommended system we re us contains the ratings given by the US the in a format the step is to arrange the data in a format that is useful to build recomendation current data contains AOW this has to be converted to a matrix format contain critics as tit as colums and rating asell the ACH we use the acast function available in the rshe 2 package the r shape 2 package is an r package popularly used for restructuring data the aast function in the r shape 2 package casts a data frame to Matrix representation the cast function takes the ratings data set as input title as row attribute critic as column attribute and rating as value the transformed data can be viewed as from the formatted data to has rated three movies Lisa Rose and all and Jack Matthews have not has one movie our which is Def at the beginning our OB we recommend to politics movies that they have beginning the SE for examp similar to Toby nice we've successfully learned about loading and formatting data