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ASSIGNMENT WITH C++ Visual Studio
Please read the instructions carefully and follow them then start coding
in C++ with comment for each steps . the code s ...
ASSIGNMENT WITH C++ Visual Studio
Please read the instructions carefully and follow them then start coding
in C++ with comment for each steps . the code should be written in
Visual Studio otherwise the code will not work in my side we have a
limitation that can not exceed. All should be by your code and should be
original and no one has used it before.. DO NOT COPY ANY FUNCTION FROM
THE INTERNET , because the code will be run under the OSBEL (that has
database of Visual studio and Java ) so don't cheat yourself and cheat
me. Good work will be reviewed with a strong recommendation. Please Do
not waste my time.
The Question is related to OPerational Excellence
A hands-on assessment project to explore the world of data analytics in operational excellence. Transform dirty data into ...
The Question is related to OPerational Excellence
A hands-on assessment project to explore the world of data analytics in operational excellence. Transform dirty data into meaningful information.
Overview:
Tableau desktop (Business analytics anyone can use) - Tableau Desktop is based on breakthrough technology from Stanford University that lets you drag & drop to analyze data. You can connect to data in a few clicks, then visualize and create interactive dashboards with a few more. Tableau is a system that supports people’s natural ability to think visually. Shift fluidly between views, following your natural train of thought. You’re not stuck in wizards or bogged down writing scripts. You just create beautiful, rich data visualizations.
Assignment Type: Individual Project
Transform dirty data into meaningful information
Each student should go to the landing page to download Tableau Desktop and Prep, then enter the product key noted below to activate each application. This key will allow you to activate Tableau Desktop and Prep for the duration of the course.
Please use these instructions:
Download Tableau Desktop and Tableau Prep here
Select each product download link to get started. When prompted, enter your school
email address for Business E-mail and enter the name of your school for Organization.
Activate with your product key: Request for student license or use the 14 days free trial
After installation, open the Tableau in your Desktop. Do a screen capture of the landing page using “Snipping Tool” from your laptop. Copy and paste the Tableau landing page image to a Word doc and name as Appendix A at the end of the paper.
Review the Data Analytics for University Students guide and write an overview about lessons learned about 3-4 pages (title page and Appendix A included).
Use auto-generated Table of Content on the title page to separate the topic themes
Syracuse University R Programming Discussion
Review Discussion: Storytelling and Success Stories then focus on Moritz’s work on the OECD Better life index download t ...
Syracuse University R Programming Discussion
Review Discussion: Storytelling and Success Stories then focus on Moritz’s work on the OECD Better life index download the executive summary and the index data.In a one-page summary, note a few particular places around the world and compare and contrast the statistics. Also, note briefly how this graphic tells the story.
Grossmont College Cloud Storage Essay
Hard drives are great for storing data on your computer, but there are many reasons why you might want to have access to y ...
Grossmont College Cloud Storage Essay
Hard drives are great for storing data on your computer, but there are many reasons why you might want to have access to your data wherever you go. Research various types of available cloud storage (such as Google Drive, OneDrive and Dropbox). Cloud storage is also known as mobile cloud storage that applies to storing an individual's data in the cloud and providing the individual with access to the data from anywhere. Using an internet search engine (Google, Bing, Yahoo, etc), find at least three Websites that discuss cloud storage. Answer the following questions as you are doing you research. You may add other questions that come to mind.
Which service do you think is most appropriate for you and why?
Are there different types of Cloud storage or are they all the same?
What service would you recommend to a small business that has multiple locations and over 50 employees?
What are any advantages to using Cloud drive technology instead of using your local hard drive as storage?
What are any disadvantages to using Cloud services instead of your local hard drive?
suggested websites
https://www.webopedia.com/TERM/C/cloud_storage.html
https://computer.howstuffworks.com/cloud-computing/cloud-storage.htm
https://www.businessnewsdaily.com/6294-benefits-of-online-data-storage.html
https://www.youtube.com/watch?v=QYzJl0Zrc4M&feature=youtu.be
https://study.com/academy/lesson/what-is-cloud-storage-definition-lesson-quiz.html
https://www.youtube.com/watch?v=HdX2VetFc6M&feature=youtu.be
University of South Florida Week 10 Using SVM on an Air Quality Dataset Code
Week 10: Lab - Using SVM on an Air Quality Dataset[Name][Date]InstructionsConduct predictive analytics on the Air Quality ...
University of South Florida Week 10 Using SVM on an Air Quality Dataset Code
Week 10: Lab - Using SVM on an Air Quality Dataset[Name][Date]InstructionsConduct predictive analytics on the Air Quality dataset to predict changes in ozone values.You will split the Air Quality dataset into a “training set” and a “test set”. Use various techniques, such as Kernal-Based Support Vector Machines (KSVM), Support Vector Machines (SVM), Linear Modelling (LM), and Naive Bayes (NB). Determine which technique is best for the dataset.Add all of your libraries that you use for this homework here.# Add your library below.
# library(tidyverse)Step 1: Load the data (0.5 point)Let’s go back and analyze the air quality dataset (we used that dataset previously in the visualization lab). Remember to think about how to deal with the NAs in the data. Replace NAs with the mean value of the column.# Write your code below.Step 2: Create train and test data sets (0.5 point)Using techniques discussed in class (or in the video), create two datasets – one for training and one for testing.# Write your code below.Step 3: Build a model using KSVM and visualize the results (2 points)Step 3.1 - Build a modelUsing ksvm(), create a model to try to predict changes in the ozone values. You can use all the possible attributes, or select the attributes that you think would be the most helpful. Of course, use the training dataset.# Write your code below.Step 3.2 - Test the model and find the RMSETest the model using the test dataset and find the Root Mean Squared Error (RMSE). Root Mean Squared Error formula here:* http://statweb.stanford.edu/~susan/courses/s60/split/node60.html# Write your code below.Step 3.3 - Plot the results.Use a scatter plot. Have the x-axis represent Temp, the y-axis represent Wind, the point size and color represent the error (as defined by the actual ozone level minus the predicted ozone level). It should look similar to this:Step 3.3 Graph - Air Quality# Write your code below.Step 3.4 - Compute models and plot the results for svm() and lm()Use svm() from in the e1071 package and lm() from Base R to computer two new predictive models. Generate similar charts for each model.Step 3.4.1 - Compute model for svm()# Write your code below.Step 3.4.2 - Compute model for lm()# Write your code below.Step 3.5 - Plot all three model results togetherShow the results for the KSVM, SVM, and LM models in one window. Use the grid.arrange() function to do this. All three models should be scatterplots.# Write your code below.Step 4: Create a “goodOzone” variable (1 point)This variable should be either 0 or 1. It should be 0 if the ozone is below the average for all the data observations, and 1 if it is equal to or above the average ozone observed.# Write your code below.Step 5: Predict “good” and “bad” ozone days. (3 points)Let’s see if we can do a better job predicting “good” and “bad” days.Step 5.1 - Build a modelUsing ksvm(), create a model to try to predict goodOzone. You can use all the possible attributes, or select the attributes that you think would be the most helpful. Of course, use the training dataset.# Write your code below.Step 5.2 - Test the model and find the percent of goodOzoneTest the model on the test dataset, and compute the percent of “goodOzone” that was correctly predicted.# Write your code below.Step 5.3 - Plot the results# determine the prediction is "correct" or "wrong" for each case,
# create a new dataframe contains correct, tempreture and wind, and goodZone
# change column names
colnames(Plot_ksvm) <- c("correct","Temp","Wind","goodOzone","Predict")
# plot result using ggplotUse a scatter plot. Have the x-axis represent Temp, the y-axis represent Wind, the shape representing what was predicted (good or bad day), the color representing the actual value of goodOzone (i.e. if the actual ozone level was good) and the size represent if the prediction was correct (larger symbols should be the observations the model got wrong). The plot should look similar to this:Step 5.3 Graph - Good Ozone# Write your code below.Step 5.4 - Compute models and plot the results for svm() and lm()Use svm() from in the e1071 package and lm() from Base R to computer two new predictive models. Generate similar charts for each model.Step 5.4.1 - Compute model for svm()# Write your code below.Step 5.4.2 - Compute model for naiveBayes()# Write your code below.Step 5.5 - Plot all three model results togetherShow the results for the KSVM, SVM, and LM models in one window. Use the grid.arrange() function to do this. All three models should be scatterplots.# Write your code below.Step 6: Which are the best Models for this data? (2 points)Review what you have done and state which is the best and why.[ Write your answer here. ]Step 7: Upload your compiled file. (1 point)Your compiled file should contain the answers to the questions.
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Most Popular Content
ASSIGNMENT WITH C++ Visual Studio
Please read the instructions carefully and follow them then start coding
in C++ with comment for each steps . the code s ...
ASSIGNMENT WITH C++ Visual Studio
Please read the instructions carefully and follow them then start coding
in C++ with comment for each steps . the code should be written in
Visual Studio otherwise the code will not work in my side we have a
limitation that can not exceed. All should be by your code and should be
original and no one has used it before.. DO NOT COPY ANY FUNCTION FROM
THE INTERNET , because the code will be run under the OSBEL (that has
database of Visual studio and Java ) so don't cheat yourself and cheat
me. Good work will be reviewed with a strong recommendation. Please Do
not waste my time.
The Question is related to OPerational Excellence
A hands-on assessment project to explore the world of data analytics in operational excellence. Transform dirty data into ...
The Question is related to OPerational Excellence
A hands-on assessment project to explore the world of data analytics in operational excellence. Transform dirty data into meaningful information.
Overview:
Tableau desktop (Business analytics anyone can use) - Tableau Desktop is based on breakthrough technology from Stanford University that lets you drag & drop to analyze data. You can connect to data in a few clicks, then visualize and create interactive dashboards with a few more. Tableau is a system that supports people’s natural ability to think visually. Shift fluidly between views, following your natural train of thought. You’re not stuck in wizards or bogged down writing scripts. You just create beautiful, rich data visualizations.
Assignment Type: Individual Project
Transform dirty data into meaningful information
Each student should go to the landing page to download Tableau Desktop and Prep, then enter the product key noted below to activate each application. This key will allow you to activate Tableau Desktop and Prep for the duration of the course.
Please use these instructions:
Download Tableau Desktop and Tableau Prep here
Select each product download link to get started. When prompted, enter your school
email address for Business E-mail and enter the name of your school for Organization.
Activate with your product key: Request for student license or use the 14 days free trial
After installation, open the Tableau in your Desktop. Do a screen capture of the landing page using “Snipping Tool” from your laptop. Copy and paste the Tableau landing page image to a Word doc and name as Appendix A at the end of the paper.
Review the Data Analytics for University Students guide and write an overview about lessons learned about 3-4 pages (title page and Appendix A included).
Use auto-generated Table of Content on the title page to separate the topic themes
Syracuse University R Programming Discussion
Review Discussion: Storytelling and Success Stories then focus on Moritz’s work on the OECD Better life index download t ...
Syracuse University R Programming Discussion
Review Discussion: Storytelling and Success Stories then focus on Moritz’s work on the OECD Better life index download the executive summary and the index data.In a one-page summary, note a few particular places around the world and compare and contrast the statistics. Also, note briefly how this graphic tells the story.
Grossmont College Cloud Storage Essay
Hard drives are great for storing data on your computer, but there are many reasons why you might want to have access to y ...
Grossmont College Cloud Storage Essay
Hard drives are great for storing data on your computer, but there are many reasons why you might want to have access to your data wherever you go. Research various types of available cloud storage (such as Google Drive, OneDrive and Dropbox). Cloud storage is also known as mobile cloud storage that applies to storing an individual's data in the cloud and providing the individual with access to the data from anywhere. Using an internet search engine (Google, Bing, Yahoo, etc), find at least three Websites that discuss cloud storage. Answer the following questions as you are doing you research. You may add other questions that come to mind.
Which service do you think is most appropriate for you and why?
Are there different types of Cloud storage or are they all the same?
What service would you recommend to a small business that has multiple locations and over 50 employees?
What are any advantages to using Cloud drive technology instead of using your local hard drive as storage?
What are any disadvantages to using Cloud services instead of your local hard drive?
suggested websites
https://www.webopedia.com/TERM/C/cloud_storage.html
https://computer.howstuffworks.com/cloud-computing/cloud-storage.htm
https://www.businessnewsdaily.com/6294-benefits-of-online-data-storage.html
https://www.youtube.com/watch?v=QYzJl0Zrc4M&feature=youtu.be
https://study.com/academy/lesson/what-is-cloud-storage-definition-lesson-quiz.html
https://www.youtube.com/watch?v=HdX2VetFc6M&feature=youtu.be
University of South Florida Week 10 Using SVM on an Air Quality Dataset Code
Week 10: Lab - Using SVM on an Air Quality Dataset[Name][Date]InstructionsConduct predictive analytics on the Air Quality ...
University of South Florida Week 10 Using SVM on an Air Quality Dataset Code
Week 10: Lab - Using SVM on an Air Quality Dataset[Name][Date]InstructionsConduct predictive analytics on the Air Quality dataset to predict changes in ozone values.You will split the Air Quality dataset into a “training set” and a “test set”. Use various techniques, such as Kernal-Based Support Vector Machines (KSVM), Support Vector Machines (SVM), Linear Modelling (LM), and Naive Bayes (NB). Determine which technique is best for the dataset.Add all of your libraries that you use for this homework here.# Add your library below.
# library(tidyverse)Step 1: Load the data (0.5 point)Let’s go back and analyze the air quality dataset (we used that dataset previously in the visualization lab). Remember to think about how to deal with the NAs in the data. Replace NAs with the mean value of the column.# Write your code below.Step 2: Create train and test data sets (0.5 point)Using techniques discussed in class (or in the video), create two datasets – one for training and one for testing.# Write your code below.Step 3: Build a model using KSVM and visualize the results (2 points)Step 3.1 - Build a modelUsing ksvm(), create a model to try to predict changes in the ozone values. You can use all the possible attributes, or select the attributes that you think would be the most helpful. Of course, use the training dataset.# Write your code below.Step 3.2 - Test the model and find the RMSETest the model using the test dataset and find the Root Mean Squared Error (RMSE). Root Mean Squared Error formula here:* http://statweb.stanford.edu/~susan/courses/s60/split/node60.html# Write your code below.Step 3.3 - Plot the results.Use a scatter plot. Have the x-axis represent Temp, the y-axis represent Wind, the point size and color represent the error (as defined by the actual ozone level minus the predicted ozone level). It should look similar to this:Step 3.3 Graph - Air Quality# Write your code below.Step 3.4 - Compute models and plot the results for svm() and lm()Use svm() from in the e1071 package and lm() from Base R to computer two new predictive models. Generate similar charts for each model.Step 3.4.1 - Compute model for svm()# Write your code below.Step 3.4.2 - Compute model for lm()# Write your code below.Step 3.5 - Plot all three model results togetherShow the results for the KSVM, SVM, and LM models in one window. Use the grid.arrange() function to do this. All three models should be scatterplots.# Write your code below.Step 4: Create a “goodOzone” variable (1 point)This variable should be either 0 or 1. It should be 0 if the ozone is below the average for all the data observations, and 1 if it is equal to or above the average ozone observed.# Write your code below.Step 5: Predict “good” and “bad” ozone days. (3 points)Let’s see if we can do a better job predicting “good” and “bad” days.Step 5.1 - Build a modelUsing ksvm(), create a model to try to predict goodOzone. You can use all the possible attributes, or select the attributes that you think would be the most helpful. Of course, use the training dataset.# Write your code below.Step 5.2 - Test the model and find the percent of goodOzoneTest the model on the test dataset, and compute the percent of “goodOzone” that was correctly predicted.# Write your code below.Step 5.3 - Plot the results# determine the prediction is "correct" or "wrong" for each case,
# create a new dataframe contains correct, tempreture and wind, and goodZone
# change column names
colnames(Plot_ksvm) <- c("correct","Temp","Wind","goodOzone","Predict")
# plot result using ggplotUse a scatter plot. Have the x-axis represent Temp, the y-axis represent Wind, the shape representing what was predicted (good or bad day), the color representing the actual value of goodOzone (i.e. if the actual ozone level was good) and the size represent if the prediction was correct (larger symbols should be the observations the model got wrong). The plot should look similar to this:Step 5.3 Graph - Good Ozone# Write your code below.Step 5.4 - Compute models and plot the results for svm() and lm()Use svm() from in the e1071 package and lm() from Base R to computer two new predictive models. Generate similar charts for each model.Step 5.4.1 - Compute model for svm()# Write your code below.Step 5.4.2 - Compute model for naiveBayes()# Write your code below.Step 5.5 - Plot all three model results togetherShow the results for the KSVM, SVM, and LM models in one window. Use the grid.arrange() function to do this. All three models should be scatterplots.# Write your code below.Step 6: Which are the best Models for this data? (2 points)Review what you have done and state which is the best and why.[ Write your answer here. ]Step 7: Upload your compiled file. (1 point)Your compiled file should contain the answers to the questions.
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