you can download the Dataset from this link - https://drive.google.com/file/d/1wlepxNIAc_6EQgv9M6WmYtSRJ7eQoty/view?usp=share_link Draw a bar chart to view of the distribution of spam and non-spam email samples in the dataset. How many emails are in the dataset? How many of the emails are spam? (Please answer this question based on implemeted code. You can use any programming language)
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you can download the Dataset from this link - https://drive.google.com/file/d/1wlepxNIAc_6EQgv9M6WmYtSRJ7eQoty/view?usp=share_link
Draw a bar chart to view of the distribution of spam and non-spam email samples in the dataset. How
many emails are in the dataset? How many of the emails are spam? (Please answer this question based on implemeted code. You can use any
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- Please Implement your changes into the code I have provided in the link below [This is the link to what I have so far] https://onlinegdb.com/4P-DKN2qhGet anime characters and display them as cards When the page loads, get the anime characters from https://bit.ly/3HPlkBl and display them as cards as shown in the image below. You only need to display the [Title, Rating , Image[Large] of the each anime character with a delete button. Caching Anime Data in IndexedDB Improve your application performance by caching the anime data in IndexedDB. Meaning, when you get the anime data save it to IndexedDB. Subsequent visits should read the data from IndexedDB. Only fetch data from the remote API if the anime data is not available in the IndexedDB. Display Anime Details When the user clicks a specific anime card from the list, you should pass the id to a function named displayAnimeDetails and get the anime’s full details from indexedDB. Then display the [Title, Rating, Image and Synopsis] of the anime character. You can simply replace the list by the card shown below. Also add a back link that replaces back the single card by the original…MD5 hash can be applied to which of the following data streams? .ppt file .doc file .dll file all of the above
- Please help me Below here I have written the code for extracting the data using scrapy for the given url for state=uttarpradesh and districts=[SANT RAVIDAS NAGAR, AGRA, VARANASI] But the data present in all pages is not saving in the excel sheet can u provide the code where to add the code and all to save all pages of data in excel sheet import scrapy import pandas as pd class HandicraftSpider(scrapy.Spider): name = "handicrafts" start_urls = [ 'http://www.handicrafts.nic.in/ArtisanData.aspx?MID=SZmOd%2fCrxTo9CHD2XKF+pA%3d%3d' ] def parse(self, response): yield scrapy.FormRequest.from_response( response, formdata={'__EVENTTARGET': '', '__EVENTARGUMENT': '', '__LASTFOCUS': '', '__VIEWSTATE': response.css('input#__VIEWSTATE::attr(value)').get(), '__VIEWSTATEGENERATOR': response.css('input#__VIEWSTATEGENERATOR::attr(value)').get(), '__EVENTVALIDATION': response.css('input#__EVENTVALIDATION::attr(value)').get(), 'ddlstate': 'Uttar Pradesh', 'ddldist': 'Sant Ravidas Nagar, Agra,…import pandas as pd import numpy as np URL = "https://bit.ly/38zhU5T" df=pd.read_csv(URL) note: I think the data should be cleaned before performing the tasks(please include the solution of cleaning the data as well)In R please provide the code and steps for the following Install the dplyr package and load the library. Load the starwars data set Make a new dataframe that only contains the height and species columns from the original data set Remove all of the rows from the new dataframe that have missing values. How many rows of data were lost when the n/a values were removed? It is recommended that you use the na.omit() function for this ex: myData <- mydata %>% na.omit() Create two new dataframes that contain the heights of humans and aliens (not humans and not droids) respectively. Read the help documentation for the par() function – the first example listed is especially relevant. Create 4 images in the same window Top Left : histogram of human heights Top Right: histogram of alien heights Bottom Left: qq plot of human heights Bottom Right: qq plot of alien heights Describe the shape of the distributions and what your observations are about the heights of these two…
- The tasks are relevant to the dataset "College.csv" college.csv file at the path: https://drive.google.com/file/d/1StNKOnI0IDQKgZ35b-VZf1_5a_Dono-N/view?usp=sharing request code python : K nearest neighbor: a. Create a binary attribute Apps01 that contains a 1 if Apps contains a value equal to or above its median, and a 0 if Apps contains a value below its median. Create a single data set d containing both Apps01 and the other College features. Split the data into d.train training set and d.test test set 80:20.b. Perform k-NN with several values of k in order to predict Apps01 using all the features.c. Display the comparison of validation error rate for different values of k.I am a newbie to python. I am working on a CSV file where it has over a million records. In the data, every Location has a unique ID (SiteID). I want to filter for and remove any records where there is no value or mismatch between SiteID and Location in my CSV file. (Note: This script should print the lines number and mismatch field values for each record.)lines = []count = 0 # read linewith open(r"air-quality-data-continuous.csv",'r') as fp: # read an store all lines into list lines = fp.readlines() print(str(len(lines)) + 'lines in input file') with open(r"crop.csv", 'w') as fp: fp.write(lines.pop(0)) #iterate each line for number, line in enumerate(lines): if (line[4] == 'NaN'): print('Empty Site ID found in line:' + str(number)) continue if(str(line[4]) != line[17]): fp.write(line) count +=1 print(str(count + 1) + 'lines written to filter.csv') print(str(count + 1) +…using the data set from the link: https://www.kaggle.com/osmi/mental-health-in-tech-survey?select=survey.csv Write Python code to return the following data:• Select all rows where the Age is less than 1• Select all rows where the Age is greater than 99
- In cell H2, get the unique Area values using UNIQUE(). In column I, use COUNTIF() to get the count of each Area. Pass the data range from A2 to A45 as absolute addresses, and the filter criteria from H2 to H9. In column J, use COUNTIFS() to get the count of each area with more than 20 Swallow-tails. the first two arguments are the same as the previous step, then add a condition for column D to be greater than 20. Area Locality Skipper Swallowtail White-yellow Blue Brush-footed Area Count of Area Count of Area with 20 Swallowtails Indian Subcontinent Indian Subcontinent 307 94 99 458 482 Western Himalaya Western Himalaya 63 31 42 129 152 Western Himalaya Kangra Hills 25 23 37 56 87 Western Himalaya Shimla Hills 41 21 34 88 115 Western Himalaya Dehradun Valley 22 11 19 42 54 Western Himalaya Mussoorie Hills 54 23 32 88 126 Western Himalaya Mussoorie Town 14 10 13 44 65 Western Himalaya Kumaon Hills 52 26 37 109 147…Please find attached, in CSV format, the dataset with filename 2019 EVCP use Q3 and Q4.csv. This is an openly available dataset from the Web link: https://datamillnorth.org/dataset/electric-vehicle-chargepoints-in-council-car-parks- The dataset describes information on the usage data for electric vehicle chargepoints in Council car parks in Leeds, UK, including how often they are used, duration of charge, electricity consumed and type of vehicle. Use Excel, Power BI, RapidMiner, Weka, Python, R, MATLAB, or other software (or software packages) you consider suitable, or their combinations, to perform analysis on the data, and answer the below 3 questions. Question 1. Before performing any analysis, but by (visually) inspecting the data, describe as completely as possible, the data features or attributes, how the data may be acquired, their values, sizes and availability, any general patterns/trends, and the general nature of the data (e.g. the” 5 C’s” for data quality).…Do you have any plans to improve the security of the following URLs (hyperlinks)?
