Fill in the worksheet with your responses (per the instructions in the write up for the deliverable).

Transcription

1 ANALYTICSLM Worksheet 1 Fill in the worksheet with your responses (per the instructions in the write up for the deliverable). Word Clouds 1. Briefly describe your two writing samples and why you have chosen them. Both articles are about how you can become a better runner. I chose them because I read a lot of articles about this and I thought it would be interesting to compare the key words from each article and what they really emphasize when they talk about what makes you a better runner Give the URL of the website you used to create your word clouds Paste (do not attach separate files) your two word clouds here (you can reduce their size so they fit the width of the page).

2 2 ANALYTICSLM Worksheet 4. Critique how well the two word clouds summarize their content and how well they help compare / contrast the two writing samples. The first word cloud summarizes the article pretty well because it mostly focuses on hydrating, eating right, and training correctly. It really focuses on how you are treating your body as a runner. The second article was more about training hard to get results during races and long runs. You can definitely see the difference because the large words really tell you what the article focuses more on. Obviously both are about running and training, but the larger words in the first article s wordle are much different than the ones in the second article. I definitely can see a lot of similarities between the two articles in the words, but I can definitely tell by looking at the word clouds what the article focuses on the most.

3 ANALYTICSLM Worksheet 3 Sentiment Analysis 1. What are the keywords you used for your sentiment analysis? Explain why you chose it. Obamacare. I knew that it was a hot topic at least on my Twitter feed and I was interested to see the opinions of other people. Most on my Twitter feed were negative as well so I was curious to see elsewhere how people felt about it. 2. What observations do you have about misclassified posts do you see any patterns that explain why the sentiment analysis tool is likely misclassifying. It seemed like a lot of the posts with pictures were taken as neutral or positive when they were actually negative. There were actually posts that were negative that were taken as positive, most of which were saying to defund Obamacare. A lot of tweets were also hashtagged with #tcot which generally means they are conservative and mostly don t agree with Obamacare. It really doesn t seem like many people were positive about it at all. 3. Paste into this document (do not attach separate files) the Sentiment Analytics resulting from the first and second days you did your sentiment search.

4 4 ANALYTICSLM Worksheet 4. Offer your explanation of why the two sentiment analytics report are similar / different. It seems like the second day I viewed the sentiment analytics, there were a lot more positive reactions on Twitter. However, when I looked through the tweets, it didn t seem like there were many tweets that were actually positive. I find it hard to understand the algorithm that decides whether a tweet or post is positive or negative. Romney said today that he thinks he lost the election because poor people and minorities like Obamacare, and that has generated a lot of different responses in the social media world that are mostly negative. I don t know if the software knows how to interpret these tweets as either positive or negative in this sense (since most of them are listed as positive). One Facebook post that said Obamacare isn t turning out to be so great is it? was listed as positive I believe because the word great is in it. It s interesting how the software is right about some things but completely incorrect about others.

5 ANALYTICSLM Worksheet 5 Exploration with Statwing 1. What percentage of institutions primarily grant associate s degrees? 51.5% 2. The net price cost per year to attend Davis College is $17,510. Is that below or above the median net price for the data? Right below. The median is $17, Look at the graph resulting from relating median borrowing and median payment. Explain why it looks the way it does. Borrowing is positively correlated with the payment. It s directly proportional because what you borrow affects how much you are paying. 4. Which of the factors (institution group, undergraduate enrollment, net price, graduation rate, or median borrowing) may help to explain the default rate of an institution. Using your critical thinking, explain what may be the connection of factor(s) to default rate. It seems like the higher the median borrowing, the lower the default rate. The only one that shows statistical resemblance is the institution group and it seems like the default rate is lower for the bachelor s degree institutions. It would seem that the institution group affects it the most and the median borrowing is next. Default rate was weakly negatively correlated with graduate rate as well.

6 6 ANALYTICSLM Worksheet Create a Predictive Model with BigML 1. Create a predictive model for loan approval from the data we provided. For each of the following applicants, indicate what loan approval decision the model recommends, along with the confidence level. Home owner Credit Score Yrs of Credit History Revolving Balance Revolving Utilization Recommend? Confidence Level Yes $21,000 15% Yes 86% No $4,000 90% No 87% Yes $1,000 70% No 34% Yes $2,000 7% No 87% Yes $4,000 60% Yes 86%