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Project Objective Employers are required to report any serious work-related injuries and death to the authority. This information helps employers, workers and the authority to evaluate the safety of a workplace, understand industry hazards, and implement worker protections to reduce and eliminate hazards. In this mini-project, assume you are engaged by a client to perform text mining on the accident reports to help find answers to the following questions: 1. What are the major types of accidents reflected in the reports? No labels, supervised or non-supervised? Clustering or Topic modelling? All data or partial data? 2. Which type of accidents are more common? Frequency of doc wrt topic 3. Can we find out the more risky occupations in such accidents? Information Extraction, how to identify “occupations” words? 4. Which part of the body is injured most? (Optional) Information Extraction, how to identify “body” words? The dataset is in file “osha.txt“. Data understanding and cleaning Load the data file into R. – read.delim(), header=FALSE e.g. textdata <- read.delim("osha.txt", header=FALSE, sep="\t", quote = "", stringsAsFactors = FALSE) Explore your data. - How many records do you have? How many variables? - Examine the first few records in the datasets. - What information does the dataset contain? - Which fields are useful for your study? - How long are the reports generally? - How’s the data quality? - What are the contents of the reports roughly? [ Create a word cloud for the dataset ] Vectorsource, corpus, DTM Term frequency summary Wordcloud contact us you can contact us directly at here to get any machine learning project related assignment help.

Overview The practical requires demonstrations of practical skills. You will be required to develop a web site that consists of a single, validated HTML page and one image. Basic Web Page · Create an .HTML file. · All styles created must be in an external style sheet. · If CSS fails to display properly, it usually means a missing a colon, semi-colon, or curly-brace somewhere. It could also mean the CSS file hasn’t been linked to the HTML file. · Follow the example screenshot of this practical’s finished version as a guide. · Download and extract the contents: marble_canyon.txt marble_canyon.jpg marble_canyon_panorama.jpg · Create an external style sheet named styles.css and an index.html REQUIREMENTS · Add code to index.html that links to the styles.css file. · Write the necessary HTML markup and CSS so that the page meets the following requirements: · The title for the page is: Your Name’s Midterm Practical. (for example: Jane Doe’s Midterm Practical) · Input the contents of marble_canyon.txt inside the & tags and give the body a Lavender (#E6E6FA) background a color. · Place the text "Marble Canyon " in a block. · Use H1 for the header block and CSS to center. · Use H3 for the Wikipedia line of text and CSS to display in Blue. · Include the five paragraphs (ending in 39.2.) in five separate blocks. In the last paragraph, enclose United States Bureau of Reclamation inside a container that uses an ID or Class to a CSS rule that gives these four words a CornflowerBlue (#6495ED) background color. · Add the image marble_canyon.jpg using the appropriate markup. Set the attributes of width to 500 pixels and height to 378 pixels. If, for some reason, the image does not load, display marble_canyon in its place. · Use CSS to float the image to the right of the page. · Add the image marble_canyon_panorama.jpg and set the width to 1000 pixels and the height to 280 pixels. Failure to load should display panorama. · Using appropriate tags and CSS, center this panorama image · Using the appropriate CSS, clear any text from flowing around either side of the panorama image. · Add a block to display at the bottom of the page · In the footer block, use an ordered list to display the three references. · Make the first reference a hyperlink using: http://www.gcrg.org/bqr/10-2/mcd.html · Add a paragraph to the bottom of the page that contains a copyright symbol followed by a date and your name. · Style the copyright paragraph with an id selector named copyright that will cause the text to be center-aligned, red, and 80% of the normal font-size. · Add a CSS rollover effect to all hyperlinks. All links, excluding background, should turn white when the mouse is over them. Add this rule to the external style sheet · Add a caption tag under the panorama image that includes the text: "A panoramic view of Marble Canyon from Navajo Bridge. Looking into Marble Canyon, Shinimo Alter in the distance." Note that the caption text must fit inside whatever container used to incorporate the image.

In machine learning, the ability of a model to predict continuous or real values based on a training dataset is called Regression. With a small dataset and some great python libraries, we can solve such a problem with ease. In this blog post, we will learn how to solve a supervised regression problem using the famous Boston housing price dataset. Other than location and square footage, a house value is determined by various other factors. Let’s analyze this problem in detail and using machine learning model to predict a housing price. Dependencies pandas - To work with solid data-structures, n-dimensional matrices and perform exploratory data analysis. matplotlib - To visualize data using 2D plots. seaborn - To make 2D plots look pretty and readable. scikit-learn - To create machine learning models easily and make predictions. Boston Housing Prices Dataset In this dataset, each row describes a boston town. There are 506 rows and 13 attributes (features) with a target column (price). The problem that we are going to solve here is that given a set of features that describe a house in Boston, our machine learning model must predict the house price. To train our machine learning model with boston housing data, we will be using scikit- learn’s boston dataset. We will use pandas and scikit-learn to load and explore the dataset. The dataset can easily be loaded from scikit-learn datasets module using load_boston function import pandas as pd from sklearn import datasets boston = datasets.load_boston() There are four keys in this dataset using which we can access more information about the dataset .["data ", "target", "feature_name" and "DESCR"] are the four keys which could be accessed using keys() on the dataset variable. To know the description of each column name in this dataset, we can use DESCR to display the description of this dataset . Exploratory Data Analysis histogram plot We can easily convert the dataset into a pandas dataframe to perform exploratory data analysis. Simply pass in the boston.data as an argument to pd.DataFrame(). We can view the first 5 rows in the dataset using head() function. bos = pd.DataFrame(boston.data, columns = boston.feature_names) bos['PRICE'] = boston.target bos.head() Exploratory Data Analysis is a very important step before training the model. Here, we will use visualizations to understand the relationship of the target variable with other features. Let’s first plot the distribution of the target variable. We will use the histogram plot function from the matplotlib library.

Description: Card memory is a game where you have to click on a card to see what image is underneath it and try to find the matching image underneath the other cards. User Stories User can see a grid with n x n cards (n is an integer). All the cards are faced down initially (hidden state) User can click a button to start the game. When this button is clicked, a timer will start User can click on any card to unveil the image that is underneath it (change it to visible state). The image will be displayed until the user clicks on a 2nd card When the User clicks on the 2nd card: If there is a match, the 2 cards will be eliminated from the game (either hide/remove them or leave them in the visible state) If there isn’t a match, the 2 cards will flip back to their original state (hiddenstate) When all the matches have been found, the User can see a dialog box showing a Congratulations message with a counter displaying the time it took to finish the game Bonus features Use can choose between multiple levels of difficulty (Easy, Medium, Hard). Increased difficulty means: decreasing the time available to complete and/or increasing the number of cards User can see the game statistics (nr. of times he won / he lost, the best time for each level)

What is selenium? Selenium is a framework for the automated testing of web applications. Using Selenium, you can basically automate every task in your browser as if a real person were to execute the task. The interface used to send commands to the different browsers is called Selenium WebDriver. Installation Here we install selenium using pip command > pip install selenium Import web driver class and a key class > from selenium import webdriver > from selenium.webdriver.common.keys import Keys webdriver - class will connect you to a browser’s instance Keys - class lets you emulate the stroke of keyboard keys, including special keys like “Shift” and “Return” Take a path where the web driver is installed Here we take the path of chrome driver, where driver .exe exists. > driver = webdriver.Chrome('./chromedriver') Use the get to take the web

IN this blog we will be doing R studio assignment help Requirements: in this assignment, we have 3 databases on excel that have in them the top ten production countries of 3 crops, with the production per ton in each country each crop is in one excel file that is attached here ( three excel files, three crops) and we need 3 bubble maps depicting the production of each country on it, we want you if you can to have the bubbles on the maps be in different colors for each map. As well we need a mContact Codersarts for any such project/assignment helpap of Italy with points for four cities: Udine, Sicily, Pisa, and San Piero a Grado Contact Codersarts for any such project/assignment help Codersarts is a top-rated website for students which is looking for online Programming Assignment Help, Homework Help, Coursework Help in C, C++, Java, Python, Database, Data structure, Algorithms, Final year project, Android, Web, C sharp, ASP NET to students at all levels whether it is school, college and university level Coursework Help or Real-time project. Hire us and Get your projects done by a computer science expert.

Here One database schema is given, you need to do queries as per below database, if you need any help then please comments in below comments section and get instant help with an affordable prices. Customer Table CREATE TABLE CUSTOMER ( cID int PRIMARY KEY, fNAME varchar (30) not null, lName varchar (30), Address varchar (50) not null, Postcode int, Email varchar (50) not null, DOB date, ); Car Table: CREATE TABLE CAR ( CarID int PRIMARY KEY, Make varchar (20) not null, Model varchar (20) not null, Type varchar (20) not null, Year int not null, Price MONEY, ); Employee Table: CREATE TABLE EMPLOYEE ( Eid int PRIMARY KEY, fNAME varchar (30) not null, lName varchar (30) not null, Job varchar (30) not null, Manager int, Hired date, ); Transaction Table: CREATE TABLE Transactions ( tID int PRIMARY KEY, cID int FOREIGN KEY REFERENCES CUSTOMER (cID), carID int FOREIGN KEY REFERENCES CAR (carID), eID int FOREIGN KEY REFERENCES EMPLOYEE (eID), tDate date not null, PickupDate date not null, ReturnDate date not null, ); Shift Table: CREATE TABLE [SHIFT]( [sID] int PRIMARY KEY IDENTITY (1, 1), [Day] varchar (20) not null, startTime int not null, endTime int not null, ); Inserting Data Into Table Inserting Into Customer: INSERT INTO CUSTOMER VALUES (1001, 'Ken', 'Adams', '5 Shipton Creek', '2064', 'abc@email.com', '1988-04-12'); INSERT INTO CUSTOMER (cID, fNAME, Adress, Postcode, Email, DOB) VALUES (1002, 'Ryu', '1 Oakwood', '3004', 'def@email.com', '1990-01-18'); INSERT INTO CUSTOMER VALUES (1003, 'Dhal', 'Sim', '8 Ivory lane' ,'5000' ,'ghi@email.com' ,'1991-03-07'); INSERT INTO CUSTOMER VALUES (1004, 'Elvis', 'Honda', '2 Bathhouse', '2000', 'jkl@email.com', '1993-04-17'); INSERT INTO CUSTOMER VALUES (1005, 'Mike', 'Balrog', '9 Club road', '3174' ,'mno@email.com' ,'1996-06-21'); INSERT INTO CUSTOMER VALUES (1006, 'Martin', 'Bison' ,'12 Show Ground' ,'4000' ,'pqr@email.com', '1990-07-12'); INSERT INTO CUSTOMER VALUES (1007, 'Chun', 'Li' ,'6 Market street' ,'2000' ,'stu@email.com' ,'1984-09-18'); INSERT INTO CUSTOMER VALUES (1008, 'Zan' ,'Gif', '7 Mill View' ,'3010' ,'vwx@email.com' ,'1998-02-02'); INSERT INTO CUSTOMER (cID, fNAME, Adress, Postcode, Email, DOB) VALUES (1009, 'Sagar', '11 Temple wood', '3064', 'yz@email.com', '1986-03-01'); INSERT INTO CUSTOMER VALUES (1010,'Warren' ,'Gates' ,'3 Wind Crescent', '2000', '123@email.com', '1984-09-18'); INSERT INTO CUSTOMER VALUES (1011, 'Jeff', 'Buffet', '9 Chariot point' ,'3010' ,'456@email.com' ,'1997-02-02'); INSERT INTO CUSTOMER VALUES (1012,'Steve', 'Bezos' ,'11 Riverview', '3063', '789@email.com' ,'1986-03-01'); INSERT INTO CUSTOMER VALUES (1013, 'Bill' ,'Jobs' ,'15 Apple court' ,'2000' ,'10@emailcom' ,'1996-06-21'); INSERTINTO CUSTOMER VALUES(1014,'Mansa','Musa','13 Timbuktu','3172','A1email.com','1999-07-12'); Inserting Into Transaction: INSERT INTO Transactions VALUES (1001, 1005 ,1001, 105 ,'20180412', '20180422' ,'20180426'); INSERT INTO Transactions VALUES (1002 ,1006 ,1004, 104 ,'20180418', '20180428' ,'20180429'); INSERT INTO Transactions VALUES (1003 ,1003 ,1005, 104 ,'2018-04-12', '2018-04-21' ,'2018-04-22'); INSERT INTO Transactions VALUES (1004 ,1004 ,1001, 105 ,'2018-04-17', '2018-04-21' ,'2018-04-27'); INSERT INTO Transactions VALUES (1005 ,1008 ,1006, 106 ,'2018-04-21', '2018-04-23' ,'2018-04-25'); INSERT INTO Transactions VALUES (1006 ,1001 ,1001, 106 ,'2018-04-21', '2018-04-29', '2018-05-21'); Inserting Into Car: INSERT INTO CAR VALUES ('1001', 'Toyota', 'Camry', 'Sedan' ,'2018' ,'$150'); INSERT INTO CAR VALUES ('1002', 'VW', 'Passat' ,'Sedan' ,'2016' ,'$160'); INSERT INTO CAR VALUES ('1003' ,'Mazda' ,'CX5' ,'C-SUV', '2017' ,'$190'); INSERT INTO CAR VALUES ('1004', 'Ford' ,'Focus' ,'Hatch' ,'2019' ,'$140'); INSERT INTO CAR VALUES ('1005' ,'Toyota' ,'HiLux', 'Ute' ,'2018' ,'$250'); INSERT INTO CAR VALUES ('1006' ,'Kia' ,'Carnival' ,'Minivan' ,'2017' ,'$200'); Inserting Into Employee: INSERT INTO EMPLOYEE VALUES ('101', 'Adam' ,'Smith' ,'Manager', '102', '1998-04-12'); INSERT INTO EMPLOYEE (Eid, fNAME, lName, Job, Hired) VALUES ('102', 'Marry' ,'Jane' ,'CEO' ,'1997-01-18'); INSERT INTO EMPLOYEE VALUES ('103', 'Eliza' ,'Rynd', 'Manager' ,'102' ,'1999-03-07'); INSERT INTO EMPLOYEE VALUES ('104', 'Imran', 'Khan' ,'Sales' ,'101' ,'2003-04-17'); INSERT INTO EMPLOYEE VALUES ('105', 'Wally' ,'Ham' ,'Sales' ,'101' ,'2006-06-21'); INSERT INTO EMPLOYEE VALUES ('106', 'Jack Hobbs' ,'Sales' ,'101', '2000-07-12'); INSERT INTO EMPLOYEE VALUES ('107', 'Don', 'Bradman', 'Advertng', '101', '2014-09-18'); INSERT INTO EMPLOYEE VALUES ('108', 'Nawab', 'Pat' ,'Accounts', '103' ,'2008-02-02'); INSERT INTO EMPLOYEE VALUES ('109' ,'Jack', 'Odumbe', 'Accounts', '103', '2016-03-01'); Inserting Into Shift: SET IDENTITY_INSERT [shift] ON INSERT INTO [shift] ( [sID] ,[Day], [startTime] ,[endTime] ) VALUES (1, 'Monday', 0900, 1700), (2, 'Tuesday', 0900 ,1700), (3 ,'Wednesday', 0900 ,1700), (4 ,'Thursday' ,0900, 1700), (5, 'Friday', 0900 ,1700), (6 ,'Friday' ,1200 ,2000), (7 ,'Saturday' ,0900 ,1700), (8, 'Saturday', 1200, 2000), (9, 'Sunday', 1000, 1600); SETIDENTITY_INSERT [shift] OFF Answer the below queries Q1 Create a view called ‘Young_Customers’, that displays all information for Customers who are less than 25 years of age Q2 Write a stored procedure called ‘Tax_Due’. The procedure should require a transaction id as input and display the tax for that transaction. Tax is calculated as 10% of the transaction value (transaction value = car price * number of days). Execute the procedure for transaction 1002. Q3 Write a trigger called ‘TransactionInfo’. The trigger should run whenever a new row is added or an existing row is updated in the Transaction table. It should display the transaction id, car make, car model, pickup and return dates. A query should be written to test the trigger. Q4 Write a query to display the number of transactions for each type of car. The output should have a separate row for each type of car such as sedan, hatch, ute etc. Q5 Write a query to display the day on which the CEO joined the company. Hint: There are functions that can be used to display day of week for dates. Q6 Write a query to display all information about the manager who manages the greatest number of employees. Hint: The manager column in the employee table contains eID (employee ID) for Managers. Q7 Write a query to display each employees’ fName, lName, and commission. Commission is calculated as 2.5% of the transaction value. Hint: Have a look at the transaction table and you will find the employee who was responsible for each transaction. Q8 Write a query to display the employees who will be working today. You should use a built-in function to identify which day of the week it is today. Use this day to extract information form the timetable Contact Us to get Instant help with an affordable price at contact@codersarts.com, or if you need solution of above query then comments so we can get response soon.

E-R Design. You are provided two application descriptions at the end of this document. Design an Entity- Relationship model representing the conceptual design of the database, for one of these two applications –your choice which one. At the minimum, include in your E-R diagram: - All the entity sets mentioned in the application description, - A primary key for each entity set and at least those attributes mentioned in the application description, - All appropriate relationship sets, - Cardinality and participation constraints of all relationship sets. As a general rule, your design should have 5 or more entity sets, and a similar number of relationship sets. Include in your design notes all the assumptions that you make. IMPORTANT: You must use the E-R notations exactly how they were introduced in this class. Other notations will not be accepted. What and where to turn in: - You will turn in a document with your E-R diagram – if hand-written/-drawn, it must be very neat; you will need to submit a scan or picture on Canvas; and design notes. - Create a Word document HW10_YourLastName.docx (substitute your last name), that includes your E-R diagram and design notes, and submit it using the link Assignment 10 in the module for Chapter 6 (E-R Design). Application 1. Real-Estate Company Application Description. The application consists of the operations of a real-estate company. The company needs to keep track of its offices, agents, buyers, sellers, properties on the market, and recently sold properties. This company focuses on homes rather than business real-estate. (To learn more about the application domain, look at some real-estate web sites.). Offices. The real-estate company has multiple offices, and address and contact information should be recorded for each office. Persons. For each person, either a buyer, seller, or agent, some basic information needs to be recorded (such as name, address, personal and work phone, etc.) Agents. For each agent, the company needs to keep track of their license number, commission rate, the date when they became affiliated with the company, office they are associated with. Buyers and sellers. You may choose to record other information (besides the basic person information) for buyers and sellers, such as required move date (buyers) or proposed vacancy date (sellers). Record who are the agents representing the buyers and sellers. Properties. Record the address, type, number of rooms, square footage, lot size, number of bedrooms, number of bathrooms, etc. for each property. Keep track of the price the properties are listed for, at different times. Purchases. Keep track of transaction information, such as the purchase date, the parts involved in the transaction. Application 2. Automobile Sales Application Description. The application consists of (some of) the operations of an automobile company, such as General Motors, Ford, Toyota, or Volkswagen. The company needs to keep track of brands, models, vehicles and their options, dealers and customers. Focus on the following aspects of corporate operations: Brands. The company may have several brands (for example, GM has Chevrolet, Pontiac, Buick, Cadillac, GMC, Saturn, Hummer, Saab, Daewoo, Holden, Vauxhall, and Opel and Volkswagen has Volkswagen, Audi, Lamborghini, Bentley, Bugatti, Skoda, and SEAT). Each brand has at least a name and a logo (text). Models. Each brand offers several models (for example, Buick’s models are the Enclave, LaCrosse, and Lucerne, and Mercury’s models are the Mariner, Milan, Sable, and Grand Marquis). Each model may come in a variety of body styles (4-door, wagon, etc.) Vehicles. Each vehicle has a vehicle identification number (VIN). Each vehicle has options: we’ll stick to color, engine and transmission. Also record the production date. Dealers and customers. Dealers buy vehicles from the manufacturer and sell them to customers. We’ll keep track of sales by date, brand, model, and color; and also by dealer. Dealers keep some cars in inventory. Some, of course, are already sold, but the dealer still keeps track of that fact. You must record the tag price and the price for which a vehicle is sold. Customers. In reality, lots of demographic data are gathered. We’ll stick to name, address, phone, gender, and annual income for individual buyers. In reality, the customer may also be a company (e.g. Hertz, Avis), but this office focuses on individual clients rather than companies. If you need complete solution of this problem or other database related help then please contact us at contact@codersarts.com and get an instant help with an affordable prices.

The overall page has a background image of background.png. Text on the page uses an 8pt font. The preferred fonts for text are Verdana, Tahoma, or any sans-serif font available on the system. The overall page body has no margin or padding, so its contents stretch to the very edge of the browser window. The top of the page contains an image banner. The center of this banner is the image banner.png. Behind this banner the image bannerbackground.png is repeated horizontally across the entire length of the page. Each of these images is 50px tall. (Hint: To make the banner stretch all the way to the edges of the page, use the bannerbackground.png as the background image of a block element that is behind the banner.png.) Underneath the image banner is a centered heading containing the movie name and year in a 24pt bold font. The preferred fonts for this heading are Tahoma, Verdana, or any sans-serif font available on the system. Below the main heading is the page's overall content area, containing an overall 32% rating for the film, several critics' reviews, and an overview of the film at right. Taken together this content occupies 800px in width and is centered horizontally within the page. If the page resizes horizontally, this 800px section should move itself dynamically so that it remains centered horizontally on the page. This overall section has a 4px gray solid border and should be sized large enough to contain all of its contents. (See textbook section 4.3.3 on making contents fit into a container.) Within the overall content area, there is a 550px-wide left-central section that contains the overall "rotten" rating of 32% and the critics' reviews of the movie. The section is topped by a smaller section containing a large "rotten" image (rottenbig.png). Behind this the image rottenbackground.png repeats horizontally across the entire length of the section. Each of these images is 83px tall. This is followed by the 32% overall rating for the film, which is shown in a 48pt red bold font, and a message that there are 88 reviews total, which is shown in the standard 8pt font in white. (Hint: It can be tricky to get the large "rotten" image and the 32% rating text to line up vertically properly. Try adjusting vertical alignment and/or making some content float.) Below the 32% overall rating, there are two columns of reviews. The columns each occupy 47% of the width of the overall left-center section of the page. There is a horizontal spacing of 2% between the columns and neighboring content. (See class notes or just goggle on creating multi-column layouts.) Each individual review consists of a box in which the reviewer gives a quote about the movie, in bold 8pt font. The quote box has a background color of #E1D697. The box has a gray border, 2px thick. 8px separate the quote box's content from its border. Each quote box also contains an icon showing whether the reviewer liked (fresh.gif) or disliked (rotten.gif) the movie. These images are on the left side of the quote box, with 5px separating them from the text to their right. Text wraps around these images as needed. The reviewer's personal information follows underneath the quote box, including the reviewer's name and publication in italic. A reviewer icon (critic.gif) is also shown, to the left of the name/publication text, with 5px of horizontal space separating it from the text. There is 3em of vertical space between reviews. (Hint: Paragraphs that are part of movie reviews should be sized large enough to contain all of their content, including any floating content. See textbook section 4.3.3 on making contents fit into a container.) To the right of the critics' reviews is a General Overview section of the page with a list of information about the movie. This section is 250px wide, and there is 10px of space between the edge of the section and the text of the list. The overview's content uses a background color of #A2B964. Its text appears in an 8pt font of Arial or any sans-serif font available on the system. The section includes a definition list (using dl, dt, and dd elements) about the movie such as its stars and director. Each term is bolded and has 1em of vertical separation between it and the element that precedes it. The bottom of this section contains a link to the movie review web site, https://savedelete.com/internet-tips/movie-reviewwebsites/36302/. Below the set of reviews is a bar with centered text explaining that the page shows reviews 1-8 of 88. This bar has a background color of #A2B964 and is placed directly up against its surrounding content. 5px separate the edge of its text and the element's own outer edge. The bottom right corner of the page has a section containing two links to the W3C validators. These are the same images and links as used in the previous assignment, with no borders. The W3C images are always present at the page's very bottom-right corner, both when the page first appears and after any scrolling. All other style elements on the page are subject to the preference of the web browser. The screenshots in this document were taken on an older browser of Firefox, which may differ from your system. The web page's title text should be DGMR - DA Gucci Movie Review . The page should have a "favorites icon" or "favicon" of rotten.gif from the image directory shown previously. Built With HTML CSS Output Contact us for this Web assignment Solutions by Codersarts Specialist who can help you mentor and guide for such Web assignments. If you have project or assignment files, You can send at contact@codersarts.com directly

Hierarchical Clustering: The dataset on American College and University Rankings contains information on 1302 American colleges and universities offering an undergraduate program. For each university, there are 17 continuous measurements (such as tuition and graduation rate) and 2 categorical measurements (such as location by state and whether it is a private or public school). Note that many records are missing some measurements. Our first goal is to estimate these missing values from “similar” records. This will be done by clustering the complete records and then finding the closest cluster for each of the partial records. The missing values will be imputed from the information in that cluster. a. Remove all records with missing measurements from the dataset. b. For all the continuous measurements, run hierarchical clustering using complete linkage and Euclidean distance. Make sure to normalize the measurements. From the dendrogram, how many clusters seem reasonable for describing these data? c. Compare the summary statistics for each cluster and describe each cluster in this context (e.g., “Universities with high tuition, low acceptance rate...”). d. Use the categorical measurements that were not used in the analysis (State and Private/Public) to characterize the different clusters. Is there any relationship between the clusters and the categorical information? e. What other external information can explain the contents of some or all of these clusters? f. Consider Tufts University, which is missing some information. Compute the Euclidean distance of this record from each of the clusters that you found above (using only the measurements that you have). Which cluster is it closest to? Impute the missing values for Tufts by taking the average of the cluster on those measurements. Principal Component Analysis: The file ToyotaCorolla.csv contains data on used cars (Toyota Corollas) on sale during the late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications. The goal will be to predict the price of a used Toyota Corolla based on its specifications. a. Identify the categorical variables. b. Explain the relationship between a categorical variable and the series of binary dummy variables derived from it. c. How many dummy binary variables are required to capture the information in a categorical variable with N categories? d. Use R to convert the categorical variables in this dataset into dummy variables, and explain in words, for one record, the values in the derived binary dummies. e. Use R to produce a correlation matrix and matrix plot. Comment on the relationships among variables. Test your concepts: Consider the 3-means algorithm on a set S consisting of the following 6 points in the plane: a= (0,0), b = (8,0), c=(16,0), d=(0,6), e=(8,6), f=(16,6). The algorithm uses the Euclidean distance metric to assign each point to its nearest centroid; ties are broken in favor of the centroid to the left/down. A starting configuration is a subset of 3 starting points from S that form the initial centroids. A 3-partition is a partition of S into 3 subsets; thus {a,b,e}, {c,d}, {f} is a 3-partition; clearly any 3-partition induces a set of three centroids in a natural manner. A 3-partition is stable if repetition of the 3-means iteration with the induced centroids leaves it unchanged. a. How many starting configurations are there? b. What are the stable 3-partitions? c. What is the number of starting configurations leading to each of the stable 3-partitions in (b)above? d. What is the maximum number of iterations from any starting configuration to its stable 3-partition? Codersarts is a top-rated website for students which is looking for online Programming Assignment Help, Homework Help, Coursework Help in C, C++, Java, Python, Database, Data structure, Algorithms, Final year project, Android, Web, C sharp, ASP NET to students at all levels whether it is school, college and university level Coursework Help or Real-time project. Hire us and Get your projects done by a computer science expert. Contact Codersarts for any such project/assignment help

The task is to complete the method bodies of the TextProcessor class the TextProcessor class will be used as part of an application to gather information about word use in documents. The purpose of an instance of the class is to keep a count of how many times each word in a document is used. It must also be able to respond to questions about the words. Some examples are given below, after the descriptions of the methods. Your implementation of TextProcessor will also be tested for correctness by a test class developed by the marker so it is worth considering creating your own test class as part of the development of the TextProcessor class. While that is partly evaluated by correctness demonstrated through testing, the implementation should also be of good quality. So, you will be assessed on the appropriateness of your solution as well as its correctness. Nevertheless, correctness is, in general, to be valued over efficiency. So, don’t be satisfied with a highly efficient but slightly incorrect solution. The TextProcessor class The class provided in the starting project consists of a constructor and four methods. There is no useful implementation in any of those elements and you must add further code to complete them. You must not modify any of the names, parameters, or return types of those elements, although you may add further non-public methods if you wish and you have complete freedom over the fields you define, although all instance fields must be kept private. The class must be implemented to provide the following functionality: • addWord: The parameter represents a case-sensitive word that has been found in a document. Because an instance must keep track of how many times each word has been added, the count of occurrences for that word must be increased by 1. You may assume that all strings passed to this method will consist only of one or more alphabetic characters and there is no need to check that this is the case. In other words, all parameters will be valid words. • howMany: The parameter represents a case-sensitive word that has been found in a document. The number of times that the exact word has been passed to the add word method must be returned. • suffixCount: This method takes a single parameter and it must return the number of distinct words added so far to the processor that the parameter is a suffix of. It counts as a suffix if a word in the processor ends with the parameter but does not also exactly match the parameter. For instance: parameter "thing" matches all of: "Something", "something", "nothing", and "thing" but only the first three qualify as distinct suffix matches, so the result would be 3. The count must represent distinct words, so each added word that contains the suffix must only be counted once. For instance, if "something" has been added 3 times, that only counts as 1 match for “something”. • anagramCount: [challenging] This method takes a single parameter and it must return the number of distinct case-insensitive anagrams of the given word that have been added to the processor. An anagram consists of exactly the same number of letters as the given word with each character occurring the same number of times. However, identical words are not considered to be anagrams. For instance: "and" is an anagram of "dan" but "nan" is neither an anagram of "naan" nor of “nan”. Note that because the match must be case-insensitive, “Dan” is also an anagram of “and”. However, because the match is case-insensitive, all of “Dan”, “dan”, and “DAN” only count as a single match for any of “and”, “AND” and “And”. Also, each anagram is only counted once, regardless of how many times it has been added to the processor via addWord. The TextProcessMain class This has been provided to help you check your implementation. You may change it as much as you wish and you will not be assessed on its contents. It currently provides a small amount of literal text as a sample document and makes a few calls on the TextProcessor’s method to check their functionality. It is not a complete test class. You can pass any text document to the program as input bypassing its file name to the main method as a parameter when you run the program. If a file is provided, then the text in that will be analyzed rather than the literal text. Example data Consider the following text found in a document that is being analyzed using an instance of TextProcessor

This assignment is about Convolutional Neural Networks. You will do object recognition using CNN. The implementation is to be done with PyTorch and the dataset is CIFAR10. The easiest way to set up your environment is to use Colab. Data set: You will use the CIFAR-10 dataset. It consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. Questions Q1 Run the CNN for 10 epochs (# of passes through the training set) and record the accuracy of the test set per epoch. Then, drop the conv layers and modify the code to get a simple neural network. Change the number of hidden layers from 0 to 4 and record the model's accuracy per epoch. Compare the accuracy of the CNN with the simple neural networks with 0,1,2,3,4 hidden layers. Each hidden layer has 120 nodes with Relu as non-linearity. Plot the test accuracy (computed on the test set) versus epochs. 10 epochs may not be enough to reach the convergence, but it is enough to see the trend. Create a single figure. Your figure must have 6 curves, one for CNN and five for the simple neural network with 0,1,2,3,4 hidden layers. Explain why some models perform better than others? Q2 Create a new model by replacing the Relu unites with Sigmoid unites in the CNN. Plot the test accuracy versus epoch number for 10 epochs. Create a single figure. Your figure should contain two curves. One for the model with Relu units and one for the model with Sigmoid units. Explain the results. Q3 The provided CNN network uses 5x5 filters with valid convolution. Design three new models by changing the filter size to 3x3 and/or the convolution type to the same convolution. Run the new models for 10 epochs. Plot the accuracy on the test set versus epoch number for the four models. Create a single figure. Your figure should contain four curves corresponding to 1- (5x5 filters, valid convolution), 2-(3x3 filters, valid convolution), (5x5 filters, same convolution) and 4-(3x3 filters, same convolution). Explain why some models perform better than others? Use print(Net()) to generate a summary of the structure of your model. Repeat this for the four models and attach the output generated by print(Net()). Codersarts is a top-rated website for students which is looking for online Programming Assignment Help, Homework Help, Coursework Help in C, C++, Java, Python, Database, Data structure, Algorithms, Final year project, Android, Web, C sharp, ASP NET to students at all levels whether it is school, college and university level Coursework Help or Real-time project. Hire us and Get your projects done by a computer science expert. Contact Codersarts for any such project/assignment help