Python - call functions without parentheses
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Arizona State University Python File Script Coding Sample Run
InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labAS ...
Arizona State University Python File Script Coding Sample Run
InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labASURITE is your user nameExample: part1-cjingers.py, part2-cjingers.pySubmit all .py files as part of your submissionDon't forget the interpreter line!The output of your script must match the same format shown in the sample run for each part.Part 1 Write a Python script that will prompt the user for monetary value (such as 11.26). Then, calculate the number of dollars, quarters, dimes, nickels, and pennies. Your solution should use the maximum possible number of dollars, then quarters and so on. For example; converting 11.26 to 1126 pennies is not acceptable.Sample runEnter an ammount of money: 11.56Your amount of $11.56 consists of; 11 dollars 2 quarters 0 dimes 1 nickels 1 penniesPart 2Given two points, the formula for computing distance is;(x2−x1)2+(y2−y1)2−−−−−−−−−−−−−−−−−−√(x2−x1)2+(y2−y1)2Write a Python script that prompts the user for two points and computes the distance between them.A point is comprised of two floating point numbers; representing the x and y coordinates.Sample runEnter x1 and y1 for Point 1: 1.5, -3.4Enter x2 and y2 for Point 2: 4, 5The distance between (1.5, -3.4) and (4, 5) is 8.8

Harvard University Database Management and Modeling MySQL Task
R HomeworkDatabase Management and ModelingGeneral exercisesCreate a text vector called Months with names of the 12 months ...
Harvard University Database Management and Modeling MySQL Task
R HomeworkDatabase Management and ModelingGeneral exercisesCreate a text vector called Months with names of the 12 months of the year. Create a numeric vector Summer, with Calendar month index positions for the summer months (inclusive, with 4 elements in all). Use vector indexing to extract the text values of Months, indexed by Summer. Multiply Summer by 3. What are the values of Months, when indexed by Summer multiplied by 3? Why do you get that answer? What is the mean (average) summer month, as an integer value? Which value of Months corresponds to it? Why do you get that answer? Use the floor() and ceiling() functions to return the upper and lower limits of Months for the average Summer month. (Hint: to find out how a function works, use R help if needed.) E-commerce Data for Exercises The data set comprises responses to intercept surveys asked when users visited the site, along with data about each user’s site activity such as number of pages visited and whether a sale was completed. Identifying details for the site and customers have been removed but the observations otherwise are actual data. We will load the data set first, and then explain a few of its observations. To load the data from CSV format, use the following command (or load ecommerce-data.csv from a local location if you have downloaded it, as noted in Section 1.6.3). ecomm.df <- read.csv("https://goo.gl/hzRyFd") summary(ecomm.df) How many observations and variables are in the e-commerce data set? Compute a frequency table for the country of origin for site visits. After the United States, which country had the most visitors? Compute a two-way frequency table for the intent to purchase (intentWasPlanningToBuy), broken out by user profile. What are the proportions of parents who intended to purchase? the proportions of teachers who did? For each one, omit observations for whom the intent is unknown (blank). Among US states (recorded in the variable region), which state had the most visitors and how many? Solve the previous problem for the state with the most visitors, using the which.max() function (or repeat the same answer, if you already used it). Draw a histogram for the number of visits to the site (behavNumVisits). Adjust it for more detail in the lower values. Color the bars and add a density line. Draw a horizontal boxplot for the number of site visits. Which chart from the previous two exercises, a histogram or a boxplot, is more useful to you, and why? Draw a boxplot for site visits broken out with a unique row for each profile type. (Note: if the chart margins make it unreadable, try the following command before plotting: par(mar=c(3, 12, 2, 2)). After plotting, you can use the command par(mar=c(5, 4, 4, 2) + 0.1) to reset the chart margins.) *Write a function called MeanMedDiff that returns the absolute difference between the mean and the median of a vector. *What is the mean-median difference for number of site visits? *What is the mean-median difference for site visits, after excluding the person who had the most visits?
3 pages

Software Development Life Cycle
System/Software Development Life Cycle (SDLC) model and methodology System/Software Development Life Cycle (SDLC) model ...
Software Development Life Cycle
System/Software Development Life Cycle (SDLC) model and methodology System/Software Development Life Cycle (SDLC) model and methodology When ...

Shell scripting question
Write a shell script to concatenate lists together, and output the resulting list Do not include any argument that is a ...
Shell scripting question
Write a shell script to concatenate lists together, and output the resulting list Do not include any argument that is a sub-list of the entire list. (The script will clean the list of any redundant items.) You must preserve the original order of the list. Remember to account for the following situations: a : at the beginning of the list is the same as a . at the beginning of the "list" o :/bin is the same as .:/bin a :: anywhere in the list is the same as :.: o /bin::/etc is the same as /bin:.:/etc a : at the end of the list is the same a :. o /bin: is the same as /bin:

Northern Virginia Community Predictive Modeling and Summary of Results Project
We will be working with the loans_df data frame in this project. This data set contains information on over 4,000 individu ...
Northern Virginia Community Predictive Modeling and Summary of Results Project
We will be working with the loans_df data frame in this project. This data set contains information on over 4,000 individuals who secured a personal loan in 2017 from a national bank. The description of this data and the variables contained in it are provided below.The objective of this project is to explore the factors that lead to loan default and develop a machine learning algorithm that will predict the likelihood of an applicant defaulting on their loan in the future.The project consists of three parts: Data Analysis, Predictive Modeling and Summary of ResultsLoan Data and Project ObjectivesThe loans_df data frame contains information on 3 and 5-year loans that were originated in 2017 by a national bank for customers residing in the Middle Atlantic and Northeast regions of the United States.The company is looking to see if it can determine the factors that lead to loan default and whether it can predict if a customer will eventually default on their loan.The bank has experienced record levels of customers defaulting on their loans in the past couple of years and this is leading to large financial losses.The goal is to become better at identifying customers at risk of defaulting on their loans to minimize financial losses. What are the factors that are associated with customers defaulting on their loans? Is it possible to predict whether a customer will default on their loan? If so, how accurate are the predictions? • How many costly errors is the model expected to produce (customers classified as not defaulting, but eventually do)? Are there any actions or policies the bank can implement to reduce the risk of loan default?The data set contains a mixture of applicant financial information (income, debt ratios, etc..), and applicant behavior (number of open accounts, historical engagement with the bank’s products, number of missed payments, etc. . . )The response variable in this data is loan default. This variable records whether an applicant eventually defaulted on their loan and indicates a financial loss to the bank.Please find attached two files for the project - - Final Project Instructions- Loan Data File that you'll analyze.
3 pages

CS192 Grantham Week 4 Programming Essentials Quiz Help
Multiple Attempts Not allowed. This test can only be taken once. Force Completion This test can be saved and resumed later ...
CS192 Grantham Week 4 Programming Essentials Quiz Help
Multiple Attempts Not allowed. This test can only be taken once. Force Completion This test can be saved and resumed later.
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Arizona State University Python File Script Coding Sample Run
InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labAS ...
Arizona State University Python File Script Coding Sample Run
InstructionsFor each part of the lab; create a python file with the name; part#-ASURITE.pywhere # is the part of the labASURITE is your user nameExample: part1-cjingers.py, part2-cjingers.pySubmit all .py files as part of your submissionDon't forget the interpreter line!The output of your script must match the same format shown in the sample run for each part.Part 1 Write a Python script that will prompt the user for monetary value (such as 11.26). Then, calculate the number of dollars, quarters, dimes, nickels, and pennies. Your solution should use the maximum possible number of dollars, then quarters and so on. For example; converting 11.26 to 1126 pennies is not acceptable.Sample runEnter an ammount of money: 11.56Your amount of $11.56 consists of; 11 dollars 2 quarters 0 dimes 1 nickels 1 penniesPart 2Given two points, the formula for computing distance is;(x2−x1)2+(y2−y1)2−−−−−−−−−−−−−−−−−−√(x2−x1)2+(y2−y1)2Write a Python script that prompts the user for two points and computes the distance between them.A point is comprised of two floating point numbers; representing the x and y coordinates.Sample runEnter x1 and y1 for Point 1: 1.5, -3.4Enter x2 and y2 for Point 2: 4, 5The distance between (1.5, -3.4) and (4, 5) is 8.8

Harvard University Database Management and Modeling MySQL Task
R HomeworkDatabase Management and ModelingGeneral exercisesCreate a text vector called Months with names of the 12 months ...
Harvard University Database Management and Modeling MySQL Task
R HomeworkDatabase Management and ModelingGeneral exercisesCreate a text vector called Months with names of the 12 months of the year. Create a numeric vector Summer, with Calendar month index positions for the summer months (inclusive, with 4 elements in all). Use vector indexing to extract the text values of Months, indexed by Summer. Multiply Summer by 3. What are the values of Months, when indexed by Summer multiplied by 3? Why do you get that answer? What is the mean (average) summer month, as an integer value? Which value of Months corresponds to it? Why do you get that answer? Use the floor() and ceiling() functions to return the upper and lower limits of Months for the average Summer month. (Hint: to find out how a function works, use R help if needed.) E-commerce Data for Exercises The data set comprises responses to intercept surveys asked when users visited the site, along with data about each user’s site activity such as number of pages visited and whether a sale was completed. Identifying details for the site and customers have been removed but the observations otherwise are actual data. We will load the data set first, and then explain a few of its observations. To load the data from CSV format, use the following command (or load ecommerce-data.csv from a local location if you have downloaded it, as noted in Section 1.6.3). ecomm.df <- read.csv("https://goo.gl/hzRyFd") summary(ecomm.df) How many observations and variables are in the e-commerce data set? Compute a frequency table for the country of origin for site visits. After the United States, which country had the most visitors? Compute a two-way frequency table for the intent to purchase (intentWasPlanningToBuy), broken out by user profile. What are the proportions of parents who intended to purchase? the proportions of teachers who did? For each one, omit observations for whom the intent is unknown (blank). Among US states (recorded in the variable region), which state had the most visitors and how many? Solve the previous problem for the state with the most visitors, using the which.max() function (or repeat the same answer, if you already used it). Draw a histogram for the number of visits to the site (behavNumVisits). Adjust it for more detail in the lower values. Color the bars and add a density line. Draw a horizontal boxplot for the number of site visits. Which chart from the previous two exercises, a histogram or a boxplot, is more useful to you, and why? Draw a boxplot for site visits broken out with a unique row for each profile type. (Note: if the chart margins make it unreadable, try the following command before plotting: par(mar=c(3, 12, 2, 2)). After plotting, you can use the command par(mar=c(5, 4, 4, 2) + 0.1) to reset the chart margins.) *Write a function called MeanMedDiff that returns the absolute difference between the mean and the median of a vector. *What is the mean-median difference for number of site visits? *What is the mean-median difference for site visits, after excluding the person who had the most visits?
3 pages

Software Development Life Cycle
System/Software Development Life Cycle (SDLC) model and methodology System/Software Development Life Cycle (SDLC) model ...
Software Development Life Cycle
System/Software Development Life Cycle (SDLC) model and methodology System/Software Development Life Cycle (SDLC) model and methodology When ...

Shell scripting question
Write a shell script to concatenate lists together, and output the resulting list Do not include any argument that is a ...
Shell scripting question
Write a shell script to concatenate lists together, and output the resulting list Do not include any argument that is a sub-list of the entire list. (The script will clean the list of any redundant items.) You must preserve the original order of the list. Remember to account for the following situations: a : at the beginning of the list is the same as a . at the beginning of the "list" o :/bin is the same as .:/bin a :: anywhere in the list is the same as :.: o /bin::/etc is the same as /bin:.:/etc a : at the end of the list is the same a :. o /bin: is the same as /bin:

Northern Virginia Community Predictive Modeling and Summary of Results Project
We will be working with the loans_df data frame in this project. This data set contains information on over 4,000 individu ...
Northern Virginia Community Predictive Modeling and Summary of Results Project
We will be working with the loans_df data frame in this project. This data set contains information on over 4,000 individuals who secured a personal loan in 2017 from a national bank. The description of this data and the variables contained in it are provided below.The objective of this project is to explore the factors that lead to loan default and develop a machine learning algorithm that will predict the likelihood of an applicant defaulting on their loan in the future.The project consists of three parts: Data Analysis, Predictive Modeling and Summary of ResultsLoan Data and Project ObjectivesThe loans_df data frame contains information on 3 and 5-year loans that were originated in 2017 by a national bank for customers residing in the Middle Atlantic and Northeast regions of the United States.The company is looking to see if it can determine the factors that lead to loan default and whether it can predict if a customer will eventually default on their loan.The bank has experienced record levels of customers defaulting on their loans in the past couple of years and this is leading to large financial losses.The goal is to become better at identifying customers at risk of defaulting on their loans to minimize financial losses. What are the factors that are associated with customers defaulting on their loans? Is it possible to predict whether a customer will default on their loan? If so, how accurate are the predictions? • How many costly errors is the model expected to produce (customers classified as not defaulting, but eventually do)? Are there any actions or policies the bank can implement to reduce the risk of loan default?The data set contains a mixture of applicant financial information (income, debt ratios, etc..), and applicant behavior (number of open accounts, historical engagement with the bank’s products, number of missed payments, etc. . . )The response variable in this data is loan default. This variable records whether an applicant eventually defaulted on their loan and indicates a financial loss to the bank.Please find attached two files for the project - - Final Project Instructions- Loan Data File that you'll analyze.
3 pages

CS192 Grantham Week 4 Programming Essentials Quiz Help
Multiple Attempts Not allowed. This test can only be taken once. Force Completion This test can be saved and resumed later ...
CS192 Grantham Week 4 Programming Essentials Quiz Help
Multiple Attempts Not allowed. This test can only be taken once. Force Completion This test can be saved and resumed later.
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