Page 2 out of 15 results
Sort by
-
Week 5
- Summary • 4 pages • 2023
- Available in package deal
-
- $10.49
- + learn more
The text is Mastering Spark with R. 
Create a new connection to your Spark cluster -. sc 
Make sure that you are using the correct version of Java 
Execute the following code: 
>summarize_all(cars, max) 
>summarize_all(cars, min) 
>summarize_all(cars, mean) 
>summarize_all(cars, mean)%>% 
 show_query() 
>cars %>% 
 mutate(transmission = ifelse(am ==0, "automatic", "manual")) %>% 
 group_by(transmission) %>% 
 summarize_all(mean)
-
Week 15
- Summary • 3 pages • 2023
-
- $10.49
- + learn more
Using the code provided in section 4.5.1 AND 4.5.2, create an LDA model of the essays in the okc dataset. Create charts of most common terms per topic. 
ANSWER THE FOLLOWING QUESTIONS: 
1. Explain how the html tags and newline characters were removed from the text. 
2. How were the individual words combined into complete essays? 
3. Explain what an LDA model does. 
4. What are stop words? 
5. In which 2 topics is the word 'want' not in the top ten? 
6. What code snippet causes the topics...
-
Week 7
- Summary • 3 pages • 2023
- Available in package deal
-
- $10.49
- + learn more
The text is Mastering Spark with R. 
Using the sc connection that you have built earlier and making sure that you are still using Java 8, install the corr library and execute the following lines of code: 
>ml_corr(cars) 
>correlate(cars, use = "", method = "pearson" 
>correlate(cars, use = "", method = "pearson" %>% 
 shave() %>% 
 rplot() 
 
Submit a Word doc with screenshots of the results of running the code. Explain what the chart is showing. Expl...
-
Week 6
- Summary • 3 pages • 2023
- Available in package deal
-
- $10.49
- + learn more
The text is Mastering Spark with R. 
Using the sc connection with Java 8, execute the following lines of code: 
>summarize(cars, mpg_percentile = percentile(mpg, 0.25) 
>summarize(cars, mpg_percentile = percentile(mpg, 0.25) %>% 
 show_query() 
>summarize(cars, mpg_percentile = percentile(mpg, array(0.25, 0.5, 0.75) )) 
>summarize(cars, mpg_percentile = percentile(mpg, array(0.25, 0.5, 0.75) )) %>% 
 mutate(mpg_percentile = explode(mpg_percentile))
-
Week 12
- Summary • 4 pages • 2023
- Available in package deal
-
- $10.49
- + learn more
Using the code in section 4.3 to scale the age variable. 
ANSWER THE FOLLOWING QUESTIONS: 
1. Explain what the mutate function is doing in this line of code: mutate(scaled_age = (age - !!scale_values$mean-age) / !!scale_values$sd_age) 
2. What do the two exclamation marks next to each other do? 
Use the code in the book to create a histogram of Scaled Age 
ANSWER THESE QUESTION: 
3. Approximately how many profiles in the training set fall in the 0 bin? 
Using the code in section 4.3, aggregat...