3 Data Mining Language: R Data discretization using equal-width binning is susceptible to outliers in the data. TRUE or FALSE?
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3
Data Mining
Language: R
Data discretization using equal-width binning is susceptible to outliers in the data.
TRUE or FALSE?
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- 1. Load the tidyverse package first and then explore the diamonds dataset. What’s the average price of premium cut diamonds in the diamonds dataset? 2. Which is true about mutate() and transmute()? Select all the correct answers. Group of answer choices: A. They both create new columns that are based upon existing columns. B. mutate(flights, gain = dep_delay - arr_delay, speed = distance / air_time * 60) and transmute(flights, gain = dep_delay - arr_delay, hours = air_time / 60, gain_per_hour = gain / hours) returns the same output. C. If you only want to keep the new variables that you have created, use transmute() instead of mutate(). No hand written and fast answer with explanationSubject RStudio Explain how you would handle missing or bad data in your dataset? Since we don't have an actual dataset, you need to provide some generic guidelines for how you would approach this problem. (IE: negative age, missing logout time from server, missing closing price for stock, etc).#Question 4 use sku.csv and WarehouseLocations.csv##############################################################def warehouse_stats(sku):"""Question 4- Read sku.csv with CSV and create a dictionary of the New SKU Statistics.- The New Sku should be the key, with the corresponding value being an innerdictionary containing the following statistics:- 350 Loc: True if not 0- Warehouse Qty- Forcasted Qty- Items/Day: can be calculated using CuFt/Day divided by Item Cube.This result should be an float rounded to5 decimals places.- In your warehouse dictionary, add an inner dictionary with key Totals whichcontains:- Total Qty in Warehouse as key "Qty": Do Not add to Totals if '350 Loc' is not a valid location.- Number of Valid 350 Loc as key "Valid"Data Cleaning Steps:- In some variates of New Sku #, the Item Cube & CuFt/Day are faulty.Fix the manufacturers mistake. If either is **less than or equal to 0**,Item Cube can be assumed to be 5.0 and CuFt/Day is 10% of theForcasted Qty of the New…
- Acording to the table in the image Database Using MySQL commands, answer the following questions ; (i) Display matrixnum, name from table students and total_marks, grade from table assessments. (ii) Display matrixnum, name from table students, subject_code, sub_name from table subjects and total_marks, grade from table assessments. (iii) Update name Afif to Syed Afif (iv) Insert the following data to table students Matrixnum: S0003 Name: Raudah (v) Delete the data entered in question (iv)in oracle mysql commend line client . i want to ccreate a data like this create database test;Create table SPORTSCARS{ -> S_id int not null, -> S_YEAR NUMBER(4,0), -> S_Make VARCHAR 2(20), -> S_Model VARCHAR 2 (20), -> S_PRICE DECIMAL (10,0), -> Primary key (S_id)INSERT into SPORTSCARS values(1, 2015 , 'Audi' , 'R8' , 112996); INSERT into SPORTSCARS values(2, 2013 , 'Ford' , 'Mustang' , 22995); INSERT into SPORTCARS values(3, 2020 , 'Honda' , 'RDX' , 34899); INSERT into SPORTSCARS values(4, 2009 , 'Porsche' , '911' , 90800);INSERT into SPORTSCARS (I'd, Year, Make, Model, Price) values(1, 2015 , 'Audi' , 'R8' , 112996); INSERT into SPORTSCARS(I'd, Year, Make, Model, Price) values(2, 2013 , 'Ford' , 'Mustang' , 22995); INSERT into SPORTCARS(I'd, Year, Make, Model, Price) values(3, 2020 , 'Honda' , 'RDX' , 34899); INSERT into SPORTSCARS(I'd, Year, Make, Model, Price) values(4, 2009 , 'Porsche' , '911' , 90800); ------------- it shows error like this…DO NOT COPY FROM OTHER WEBSITES Q. How to create dynamic circular list from this txt file data? txt file: Alyce,Female,33 Autumn,Female,26 Brad,Male,45 Jon,Male,60 Zach,Male,22
- CREATE DATABASE COUNTRIES; USE COUNTRIES; DROP TABLE IF EXISTS `City`; CREATE TABLE `City` ( `ID` int(11) NOT NULL AUTO_INCREMENT, `Name` char(35) NOT NULL DEFAULT '', `CountryCode` char(3) NOT NULL DEFAULT '', `District` char(20) NOT NULL DEFAULT '', `Population` int(11) NOT NULL DEFAULT '0', PRIMARY KEY (`ID`) ) ENGINE=MyISAM AUTO_INCREMENT=4080 DEFAULT CHARSET=latin1; -- -- Dumping data for table `City` -- -- ORDER BY: `ID` INSERT INTO `City` VALUES (1,'Kabul','AFG','Kabol',1780000); INSERT INTO `City` VALUES (2,'Qandahar','AFG','Qandahar',237500); INSERT INTO `City` VALUES (3,'Herat','AFG','Herat',186800); INSERT INTO `City` VALUES (4,'Mazar-e-Sharif','AFG','Balkh',127800); INSERT INTO `City` VALUES (5,'Amsterdam','NLD','Noord-Holland',731200); INSERT INTO `City` VALUES (6,'Rotterdam','NLD','Zuid-Holland',593321); INSERT INTO `City` VALUES (7,'Haag','NLD','Zuid-Holland',440900); INSERT INTO `City` VALUES (3068,'Berlin','DEU','Berliini',3386667); INSERT INTO `City` VALUES…please solve all parts I am in need ( in pycharm) Question II: A data repository is maintained by Johns Hopkins University CSSE research center (https://github.com/CSSEGISandData/COVID-19/) about corona virus incidents. The site https://www.w3resource.com/python-exercises/project/covid-19/index.php includes some exercises on COVID-19 data set. You can look at these exercises before solving the following questions. First, get the latest covid data from github as follows: # Import data import pandas as pd covid_data= pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID19/master/csse_covid_19_data/csse_covid_19_daily_reports/05-10-2022.csv') covid_series= pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID- 19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv') Display first 5 rows from COVID-19 daily summary (covid_data) and time series (covid_series) datasets. Write a Python program to get the latest…using R please 1) Initial data overview a. Load the faithful dataset in R b. What are the column headers for this data set? c. How many rows of data are in the data set? 2) Summary stats for the full data set a. Compute all of the following for the duration of the eruptions and the waiting time between the eruptions i. mean ii. population variance iii. population standard deviation iv. population coefficient of variation 3)Sampling a. Create a new data frame that contains 100 samples of size 10 from the eruption duration column of the faithful data set i. You can use the sample() function to create your samples of size 10 ii. You can use the replicate() function to repeat the sampling 100 times iii. You can cast the result as a data frame using data.frame() 4) Analyze the Samples a. Create 3 new empty vectors – these will store the sample mean, sample variance, and sample standard deviation of each of your 100 samples b. For each sample i. compute the sample mean…
- 6.1. Import your datasets and save them as Exam1 and Exam1_predict. 6.2. Check the structure of your datasets using str(). 6.3. Select records and deal with missing values. # filter CURRENCY attribute to limit records of US dollars only Exam1_2 <- Exam1[Exam1$Currency!= 'GBP',] # verify if GBP currency records are removed and missing data issue resolved summary(Exam1_2) str(Exam1_2) # remove the attribute, currency, with one level Exam1New <- subset(Exam1_2, select = -Currency) # Check if the attribute is removed str(Exam1New) # replace the missing data in OpenPrice attribute using the minimal ofOpenPrice Exam1New$OpenPrice[which(is.na(Exam1New$OpenPrice))] <- min(Exam1New$OpenPrice, na.rm=T) #check if missing values are replaced summary(Exam1New) Take a screenshot of your R codes with date and time to show how you import and prepare the data for modeling and prediction, that is, Steps 6.1-6.3, with date and time (Screenshot 3). 6.4. Decision Tree model 6.4.1. Build a decision…Hive is a data warehouse application built on top of Apache Hadoop that allows users to query and analyze data. HDFS stores massive data comparable to hive tables. Is it possible for Hive to store metadata in HDFS? If not, where and how does the metadata information get stored?Db&__Course: Database *(SQL)* Please excute the given SQL script (https://drive.google.com/file/d/1zxe_aOhERjVCL54_zbgSLkFTRHYQhOPW/view?usp=sharing) for accessing the data. The data is described in the following relation schemas: Airport (airportID, name, city) Passenger (ticketNo, name, nationality, flightNo, seatNo)FK: flightNo references Flight (flightNo)FK: seatNo references Seat (seatNo) Flight (flightNo, flightCompany, departAirport, arrivalAirport)FK: departAirport references Airport (airportID)FK: arrivalAirport references Airport (airportID) Seat (seatNo, flightNo, class)FK: flightNo references Flight (flightNo) #Construct the SQL statements based on following transactions:1. Retrieve all rows in Airport table for all the airports in London city.2. Retrieve all British and German passengers.3. Retrieve all names of all the passengers...