hrishikash

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hrishikash

1. The dataset is from the first Harry Potter book which contains around 43000 words. This dataset can be used to get information like word frequency, character relationships. Dataset link - https://github.com/Khushmeet/potter-nlp

2. Created a word cloud depicting the popular words which appear in the book. To do this, I used Python and the NLTK library to get the word frequency of each wordin the dataset. After that, I used this word frequency information to render the word cloud.

3. The second visualisation is complementary to the first one, and depicts the most popular words from the dataset in a bar graph where the x-axis is the words and y-axis is the frequency with which they occur.

4. When you hover over a word in the dataset, it turns red and the corresponding bar in the bar chart also turns red. For a few of the smaller words, we don't observe this because they are not depicted in the bar chart. This also works vice-versa i.e if you hover over a bar in the bar chart, then the corrresponding word i

hrishikash

1. The dataset is from the first Harry Potter book which contains around 43000 words. This dataset can be used to get information like word frequency, character relationships. Dataset link - https://github.com/Khushmeet/potter-nlp

2. Created a word cloud depicting the popular words which appear in the book. To do this, I used Python and the NLTK library to get the word frequency of each wordin the dataset. After that, I used this word frequency information to render the word cloud.

3. The second visualisation is complementary to the first one, and depicts the most popular words from the dataset in a bar graph where the x-axis is the words and y-axis is the frequency with which they occur.

4. When you hover over a word in the dataset, it turns red and the corresponding bar in the bar chart also turns red. For a few of the smaller words, we don't observe this because they are not depicted in the bar chart. This also works vice-versa i.e if you hover over a bar in the bar chart, then the corrresponding word i

hrishikash

1. The dataset is from the first Harry Potter book which contains around 43000 words. This dataset can be used to get information like word frequency, character relationships. Dataset link - https://github.com/Khushmeet/potter-nlp

2. Created a word cloud depicting the popular words which appear in the book. To do this, I used Python and the NLTK library to get the word frequency of each wordin the dataset. After that, I used this word frequency information to render the word cloud.

3. The second visualisation is complementary to the first one, and depicts the most popular words from the dataset in a bar graph where the x-axis is the words and y-axis is the frequency with which they occur.

hrishikash

1. The dataset I'm working with for this visualization is based on Andrew Beveridge’s dataset of “A Storm of Swords”, third book in the series. It contains 1003 edges and 516 nodes. This dataset can be used to infer relationships between the characters and also help us identify clusters amongst characters.

Dataset link - https://networkofthrones.wordpress.com/data/

2. I've created a force-directed graph with the nodes as the characters of the series and the edges are the interactions between them.

3. I've created a complementary second visualisaiton which is a bar chart showing the influence of all the characters vs their id in the x-axis.

hrishikash

1. The dataset I'm working with for this visualization is based on Andrew Beveridge’s dataset of “A Storm of Swords”, third book in the series. It contains 1003 edges and 516 nodes. This dataset can be used to infer relationships between the characters and also help us identify clusters amongst characters.

Dataset link - https://networkofthrones.wordpress.com/data/

2. I've created a force-directed graph with the nodes as the characters of the series and the edges are the interactions between them.

3. I've created a complementary second visualisaiton which is a bar chart showing the influence of all the characters vs their id in the x-axis.

hrishikash

1. The dataset contains stock price information of Microsoft(MSFT) for a 5 year period from 2010 - 2014. Each row has the following attributes: Date, Volume(volume of shares traded on that day), Close)closing price of the stock), average(average price over a specified period). Size of the dataset is: 1243 rows and 4 columns

This dataset can be used for predicting Microsoft stock price in the future and also to gain meaningful insights about the trnds in teh price over the last 5 years.

2,3. I have created both the required visualisations in the same page. The first shows the trend of MSFT stock prices and the moving average taken form the dataset over time from 2010-2014.

The second visualisation uses small rectangles/bars to show the volume of shares traded over time in the same timespan.

hrishikash

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hrishikash

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hrishikash

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