日本欧洲视频一区_国模极品一区二区三区_国产熟女一区二区三区五月婷_亚洲AV成人精品日韩一区18p

COMP 315 代做、代寫 java 語言編程

時(shí)間:2024-03-10  來源:  作者: 我要糾錯(cuò)



1 Introduction
Assignment 1: Javascript
COMP 315: Cloud Computing for E-Commerce March 5, 2024
A common task in cloud computing is data cleaning, which is the process of taking an initial data set that may contain erroneous or incomplete data, and removing or fixing those elements before formatting the data in a suitable manner. In this assignment, you will be tested on your knowledge of JavaScript by implementing a set of functions that perform data cleaning operations on a dataset.
2 Ob jectives
By the end of this assignment, you will:
• Gain proficiency in using JavaScript for data manipulation.
• Be able to implement various data cleaning procedures, and understand the significance of them. • Have developed problem-solving skills through practical application.
3 Problem description
For this task, you have been provided with a raw dataset of user information. You must carry out the following series of operations:
• Set up a Javascript class in the manner described in Section 4.
• Convert the data into the appropriate format, as highlighted in Section 5
• Fix erroneous values where possible e.g. age being a typed value instead of a number, age being a real number instead of an integer, etc; as specified in Section 6.
• Produce functions that carry out the queries specified in Section 7.
 Data name Title
First name
Middle name Surname Date of birth Age
Email
Note
This value may be either: Mr, Mrs, Miss, Ms, Dr, or left blank.
Each individual must have one. The first character is capitalised and the rest are lower case, with the exception of the first character after a hyphen.
This may be left blank.
Each individual must have one.
This must be in the format of DD/MM/YYYY.
All data were collected on 26/02/2024, and the age values should reflect this.
The format should be [first name].[surname]@example.com. If two individuals have the same address then an ID is added to differentiate them eg john.smith1, john.smith2, etc
Table 1: The attributes that should be stored for each user
         1

4 Initial setup
Create a Javascript file called Data Processing.js. Create a class within that file called Data Processing. Write a function within that class called load CSV that takes in the filename of a csv file as an input, eg load CSV (”User Details”). The resulting data should be saved locally within the class as a global variable called raw user data. Write a function called format data, which will have no variables are a parameter. The functionality of this method is described in Section 5. Write a function called clean data, which will also have no parameters. The functionality of this method is similarly described in Section 6.
5 Format data
Within the function format data, the data stored within raw user data should be processed and output to a global variable called formatted user data. The data are initially provided in the CSV format, with the delimiter being the ’,’ character. The first column of the data is the title and full name of the user. The second and third columns are the date of birth, and age of the user, respectively. Finally, the fourth column is the email of the user. Ensure that the dataset is converted into the appropriate format, outlined in Table 1. This data should be saved in the JSON format (you may use any built in JavaScript method for this). The key for each of the values should be names shown in the ’Data name’ column, however converted to lower case with an underscore instead of a space character eg ’first name’.
6 Data cleaning
Within the function clean data, the data cleaning tasks should be carried out, loading the data stored in formatted user data. All of this code may be written within the clean data function, or may be handled by a series of functions that are called within this class. The latter option is generally considered better practice. Examine the data in order to determine which values are in the incorrect format or where values may be missing. If a value is in the incorrect format then it must be converted to be in the correct format. If a value is missing or incorrect, then an attempt should be made to fill in that data given the other values. The cleaned data should be saved into the global variable cleaned user data.
7 Queries
Often, once the data has been processed, we perform a series of data analysis tasks on the cleaned data. Each of these queries are outlined in Table 2. Write a function with the name given in the ’Function name’ column, that carries out the query given in the corresponding ’Query description’. The answer should be returned by the function, and not stored locally or globally.
 Function name
most common surname average age
youngest dr
most common month
Query description
What is the most common surname name?
What is the average age of the users, given the values stored in the ’age’ column? This should be a real number to 3 significant figures.
Return all of the information about the youngest individual in the dataset with the title Dr.
What is the most common month for individuals in the data set?
        percentage titles
 What percentage of the dataset has each of the titles? Return this in the form of an array, following the order specified in the ’Title’ row of Table 1. This should included the blank title, and the percentage should be rounded to the nearest integer using bankers rounding.
  percentage altered
 A number of values have been altered between formatted user data and cleaned user data. What percentage of values have been altered? This should be a real number to 3 significant figures.
  Table 2: The queries that should be carried out on the cleaned data
2

8 Marking
The marking will be carried out automatically using the CodeGrade marking platform. A series of unit tests will be ran, and the mark will correspond with how many of those unit tests were successfully executed. Your work will be submitted to an automatic plagiarism/collusion detection system, and those exceeding a threshold will be reported to the Academic Integrity Officer for investigation regarding adhesion to the university’s policy https://www.liverpool.ac.uk/media/livacuk/tqsd/code-of-practice-on-assessment/appendix L cop assess.pdf.
9 Deadline
The deadline is 23:59 GMT Friday the 22nd of March 2024. Late submissions will have the typical 5% penalty applied for each day late, up to 5 days. Submissions after this time will not be marked. https: //www.liverpool.ac.uk/aqsd/academic-codes-of-practice/code-of-practice-on-assessment/
請(qǐng)加QQ:99515681  郵箱:99515681@qq.com   WX:codehelp 

標(biāo)簽:

掃一掃在手機(jī)打開當(dāng)前頁
  • 上一篇:代寫 CSSE7030 Connect 4
  • 下一篇:代做ACS61012、代寫ACS61012 Machine Vision
  • 無相關(guān)信息
    昆明生活資訊

    昆明圖文信息
    蝴蝶泉(4A)-大理旅游
    蝴蝶泉(4A)-大理旅游
    油炸竹蟲
    油炸竹蟲
    酸筍煮魚(雞)
    酸筍煮魚(雞)
    竹筒飯
    竹筒飯
    香茅草烤魚
    香茅草烤魚
    檸檬烤魚
    檸檬烤魚
    昆明西山國家級(jí)風(fēng)景名勝區(qū)
    昆明西山國家級(jí)風(fēng)景名勝區(qū)
    昆明旅游索道攻略
    昆明旅游索道攻略
  • NBA直播 短信驗(yàn)證碼平臺(tái) 幣安官網(wǎng)下載 歐冠直播 WPS下載

    關(guān)于我們 | 打賞支持 | 廣告服務(wù) | 聯(lián)系我們 | 網(wǎng)站地圖 | 免責(zé)聲明 | 幫助中心 | 友情鏈接 |

    Copyright © 2025 kmw.cc Inc. All Rights Reserved. 昆明網(wǎng) 版權(quán)所有
    ICP備06013414號(hào)-3 公安備 42010502001045

    日本欧洲视频一区_国模极品一区二区三区_国产熟女一区二区三区五月婷_亚洲AV成人精品日韩一区18p

              一区二区三区无毛| 欧美片网站免费| 日韩亚洲精品在线| 国产精品一二| 欧美精品一区二区三区久久久竹菊 | 91久久精品美女| 国产精品天天摸av网| 美乳少妇欧美精品| 亚洲欧美日韩中文视频| 亚洲国产精品成人综合| 国产一区二区久久久| 欧美体内谢she精2性欧美| 蜜桃视频一区| 久久久久国产精品麻豆ai换脸| 一区二区三区精品久久久| 精品成人久久| 国色天香一区二区| 国产欧美在线观看| 欧美日韩国产综合视频在线| 老牛影视一区二区三区| 欧美一区国产一区| 欧美一区二区三区免费看| 亚洲欧美日韩精品久久奇米色影视 | 一区二区动漫| 日韩视频在线你懂得| 亚洲国产精品一区制服丝袜| 激情成人av在线| 国语自产精品视频在线看8查询8| 欧美亚韩一区| 国产精品青草综合久久久久99| 欧美三级电影大全| 国产精品国产三级国产普通话蜜臀| 欧美日韩免费| 国产精品欧美精品| 国产精品永久在线| 国产专区一区| 在线成人亚洲| 亚洲精品乱码久久久久久| 亚洲国产mv| 亚洲乱亚洲高清| 在线视频亚洲| 翔田千里一区二区| 久久国产日本精品| 米奇777超碰欧美日韩亚洲| 老司机免费视频久久 | 国产资源精品在线观看| 狠狠久久亚洲欧美专区| 亚洲电影天堂av| 99国产精品99久久久久久| 亚洲视频电影图片偷拍一区| 亚洲欧美另类久久久精品2019| 欧美一区亚洲二区| 蜜桃av综合| 欧美吻胸吃奶大尺度电影| 国产日韩成人精品| 亚洲精品国产精品国自产观看| 99一区二区| 久久成人在线| 欧美日韩一二三四五区| 国产精品亚发布| 亚洲国产精品传媒在线观看| 一区二区三区四区五区视频 | 国产一区二三区| 亚洲国产日韩美| 亚洲欧美国产一区二区三区| 久久婷婷av| 国产精品乱码一区二三区小蝌蚪| 好看的日韩av电影| 亚洲视频综合在线| 蘑菇福利视频一区播放| 国产美女一区二区| av成人免费在线| 免费不卡视频| 国产一区欧美| 亚洲一区二区三区色| 欧美了一区在线观看| 国产亚洲一区精品| 亚洲永久免费| 欧美日韩在线免费视频| 亚洲风情在线资源站| 欧美一进一出视频| 国产精品嫩草久久久久| 日韩视频一区二区| 欧美a级一区二区| 国产农村妇女精品一区二区| 亚洲色图在线视频| 欧美日韩精选| 99精品国产热久久91蜜凸| 美女精品一区| 在线观看视频一区| 久久亚洲春色中文字幕久久久| 国产精品丝袜久久久久久app| 一区二区三区精品在线 | 亚洲激情欧美激情| 麻豆成人综合网| 精东粉嫩av免费一区二区三区| 午夜精品福利在线| 国产精品资源在线观看| 亚洲制服av| 国产伦精品一区二区| 午夜精品在线视频| 国产日韩欧美综合精品| 久久www成人_看片免费不卡| 国产精品一区二区在线观看| 新67194成人永久网站| 国产精品日韩欧美大师| 亚洲伊人观看| 国产亚洲综合在线| 久久精品官网| 91久久黄色| 欧美日韩综合在线免费观看| 亚洲综合激情| 韩日欧美一区| 欧美va天堂va视频va在线| 亚洲精品一区二区三区婷婷月| 国产乱码精品一区二区三区av| 欧美日韩在线三区| 亚洲免费观看| 欧美日韩在线影院| 亚洲午夜视频在线观看| 国产欧美日韩精品在线| 久热精品视频在线观看| 亚洲免费高清视频| 国产精品一区二区a| 久久精品一区四区| 亚洲人成毛片在线播放女女| 欧美日韩国产首页| 久久精品亚洲一区二区| 亚洲国产福利在线| 国产精品久久久久影院色老大| 久久激情视频久久| 一本色道久久综合亚洲精品按摩| 国产精品夜夜嗨| 麻豆av一区二区三区久久| 亚洲一区二区伦理| 精品91在线| 国产精品久久77777| 久久婷婷久久一区二区三区| 亚洲一区二区高清| 亚洲福利视频网| 国产精品草草| 欧美电影免费| 久久资源av| 欧美一区二区三区四区在线观看地址| 亚洲国产精品v| 国产主播一区| 国产精品一区二区三区久久| 欧美激情精品久久久久| 久久久91精品| 先锋影音国产精品| 99精品欧美一区二区蜜桃免费| 国产主播一区二区三区| 国产精品美女黄网| 欧美伦理在线观看| 久久综合成人精品亚洲另类欧美| 亚洲图片欧美午夜| 亚洲精品无人区| 亚洲成人在线免费| 亚洲国产精品高清久久久| 国产美女在线精品免费观看| 欧美亚州韩日在线看免费版国语版| 欧美成人免费播放| 久久伊人一区二区| 久久综合国产精品| 久久综合久久综合这里只有精品 | 狠狠色丁香婷婷综合影院| 国产精品另类一区| 国产精品久久毛片a| 欧美性猛交视频| 欧美三区在线视频| 欧美体内谢she精2性欧美| 欧美精品日韩www.p站| 欧美日本在线一区| 欧美日韩亚洲一区二区三区四区| 欧美成人官网二区| 欧美剧在线观看| 国产精品v欧美精品v日韩精品| 欧美午夜精品久久久久久人妖| 欧美日韩一级视频| 国产精品二区在线观看| 国产欧美综合在线| 国内免费精品永久在线视频| 精品成人在线视频| 亚洲福利在线看| 日韩视频在线观看| 亚洲欧美综合v| 久久天天躁狠狠躁夜夜av| 欧美成人一二三| 欧美午夜一区| 国产一区自拍视频| 亚洲精品一区中文| 亚洲综合999| 久久美女性网| 久久精品国产清高在天天线| 蜜桃久久av| 欧美体内she精视频| 国产欧美一区二区白浆黑人| 激情丁香综合| 一区二区久久久久久| 欧美在线一区二区|