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Text_Analytics_Week13_NEC_Solved
Create time series charts for each tweeter showing how word usage has changed over time. Show for three words. You may have to manipulate a parameter to show Comment your code, line by line. Show a graph for each tweeter revealing the ten words with the highest number of retweets. Comment your code, line by line.
- Book & Paket-Deal
- Examen
- • 19 páginas •
Create time series charts for each tweeter showing how word usage has changed over time. Show for three words. You may have to manipulate a parameter to show Comment your code, line by line. Show a graph for each tweeter revealing the ten words with the highest number of retweets. Comment your code, line by line.
Text_Analytics_Week12_NEC_Solved
Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters. Make a dataframe of word frequency for each of Dave and Julia. Plot the frequencies against each other. Include a dividing line in red showing words nearby that are similar in frequency and words more distant which are shared less frequently. Create a stacked chart compa...
- Book & Paket-Deal
- Examen
- • 13 páginas •
Using the attached files of around 3200 tweets per person, show a histogram (frequency distribution) of the tweets of both Dave and Julia. Use `UTC` to create the time stamp. Remember that the case of column headers matters. Make a dataframe of word frequency for each of Dave and Julia. Plot the frequencies against each other. Include a dividing line in red showing words nearby that are similar in frequency and words more distant which are shared less frequently. Create a stacked chart compa...
Text_Analytics_Week11_NEC_Solved
1. Show stacked bar charts of the most common terms within each of 2 topics from the Associated Press articles in the topicmodels package. Color the charts by topic. Comment your code line by line. 2. Show a stacked bar chart showing the words that have a Beta greater than 1/1000 in at least one topic with the greatest difference in Beta between topic 1 and topic 2. comment each line of your code.
- Book & Paket-Deal
- Examen
- • 7 páginas •
1. Show stacked bar charts of the most common terms within each of 2 topics from the Associated Press articles in the topicmodels package. Color the charts by topic. Comment your code line by line. 2. Show a stacked bar chart showing the words that have a Beta greater than 1/1000 in at least one topic with the greatest difference in Beta between topic 1 and topic 2. comment each line of your code.
Text_Analytics_Week10_NEC_Solved
Create a chart showing the words with the greatest contribution to positive or negative sentiment in the AP articles. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line. Create charts showing the terms with the highest tf-idf from each of four selected inaugural addresses. Eliminate the ? term. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line. Create charts showing over time...
- Book & Paket-Deal
- Examen
- • 8 páginas •
Create a chart showing the words with the greatest contribution to positive or negative sentiment in the AP articles. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line. Create charts showing the terms with the highest tf-idf from each of four selected inaugural addresses. Eliminate the ? term. Show all the code from the necessary packages untll you can produce the chart. Comment your code line by line. Create charts showing over time...
Text_Analytics_Week8_NEC_Solved
Create network graph of bigrams in a Wells' novels. Do not include stop words. Make the links darker the more common the bigram is. Use arrows at the end of the line toward the second word. Colorize the central node. Show a chart and your code with line by line comments. Create a count_bigrams function to reuse for counting bigrams in other texts. Comment your code line by line. Create a visualize_bigrams function to reuse for visualizing network graphs of other texts. Comment your code li...
- Book & Paket-Deal
- Examen
- • 9 páginas •
Create network graph of bigrams in a Wells' novels. Do not include stop words. Make the links darker the more common the bigram is. Use arrows at the end of the line toward the second word. Colorize the central node. Show a chart and your code with line by line comments. Create a count_bigrams function to reuse for counting bigrams in other texts. Comment your code line by line. Create a visualize_bigrams function to reuse for visualizing network graphs of other texts. Comment your code li...
Text_Analytics_Week7_NEC_Solved
Find the most common bigrams in Wells' novels. Show a chart and your code with comments. Show tf-idf frequency in faceted graphs of Wells' novels. Determine how often the word 'not' precedes another word in Wells' novels. Show a table.
- Book & Paket-Deal
- Examen
- • 10 páginas •
Find the most common bigrams in Wells' novels. Show a chart and your code with comments. Show tf-idf frequency in faceted graphs of Wells' novels. Determine how often the word 'not' precedes another word in Wells' novels. Show a table.
Text_Analytics_Week6_NEC_Solved
• Using the Jane Austen novels, show a term frequency distribution with a separate graph for each book. Comment your code line by line to show what it is doing. • Examine Zipf's law for Jane Austen's novels. Create a single graph of rank v. term frequency using logarythmic scales. Comment your code line by line to show what it is doing. • Compare Austen's novels to H.G. Wells to see if they similarly use a percentage of the most common words. Produce one graph for both authors. Use c...
- Book & Paket-Deal
- Examen
- • 9 páginas •
• Using the Jane Austen novels, show a term frequency distribution with a separate graph for each book. Comment your code line by line to show what it is doing. • Examine Zipf's law for Jane Austen's novels. Create a single graph of rank v. term frequency using logarythmic scales. Comment your code line by line to show what it is doing. • Compare Austen's novels to H.G. Wells to see if they similarly use a percentage of the most common words. Produce one graph for both authors. Use c...
Text_Analytics_Week5_NEC_Solved
• Using the gutenbergr package, (if the default mirror doesn't work use: hgwells <- gutenberg_download(c(35,36,5230,159), mirror = " • Create bar charts of the top ten words that contribute to the positive and negative sentiment in one of the books. • Produce a Word Cloud of the 100 most common words in the same book.
- Book & Paket-Deal
- Examen
- • 53 páginas •
• Using the gutenbergr package, (if the default mirror doesn't work use: hgwells <- gutenberg_download(c(35,36,5230,159), mirror = " • Create bar charts of the top ten words that contribute to the positive and negative sentiment in one of the books. • Produce a Word Cloud of the 100 most common words in the same book.
Text_Analytics_Week4_NEC_Solved
Find another spam text file (UCI, Kaggle, etc) and compare word frequency using both bar charts scatterplots side by side. Order the bar charts from high frequency to low. Create another visualization to show the ten highest frequency words that appear in both files. Show screenshots of your work. Include comments that explain what is happening line by line.
- Book & Paket-Deal
- Examen
- • 12 páginas •
Find another spam text file (UCI, Kaggle, etc) and compare word frequency using both bar charts scatterplots side by side. Order the bar charts from high frequency to low. Create another visualization to show the ten highest frequency words that appear in both files. Show screenshots of your work. Include comments that explain what is happening line by line.
TextAnalytics_Week3_NEC_solved
Using the dataset from week 2, eliminate the stop words and create a visualization of the highest frequency words in both a bar chart and a pie chart. Label your graphs and your axes. Comment your code showing your understanding of what the code is doing line by line.
- Book & Paket-Deal
- Examen
- • 5 páginas •
Using the dataset from week 2, eliminate the stop words and create a visualization of the highest frequency words in both a bar chart and a pie chart. Label your graphs and your axes. Comment your code showing your understanding of what the code is doing line by line.
Nutrition_Case_Study_ML_Week8_NEC
Fundamentals_of_general_additive_models_ML_Week7_NEC
Santander_Bank_Case_Study_ML_Week6_NEC
Fundamentals_of_ensemble_modeling_Week5_NEC
Fundamentals_of_ensemble_modeling_Week5_NEC