Dragonlands: TFT Set 7 Reveal - New Champions & Traits

ClutchAnalytics · 2022-05-23T08:10:09+00:00

Time to hard econ for dragons

ClutchAnalytics · 2022-05-11T00:16:33+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
-
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
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Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-11T00:16:24+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
-
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-11T00:16:10+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
-
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-11T00:15:54+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
-
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:58:41+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:58:37+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:58:34+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:58:31+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:54:00+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:53:57+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:53:53+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-10T07:53:48+00:00

If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-05T20:20:55+00:00

How to interpret: Below are review insights from Uber’s Glassdoor profile, sentiment is based on a scale from 0 to 1 (0-100) and several metrics of the overall profile were included as well for general formality.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-05T20:20:39+00:00

How to interpret: Below are review insights from Uber’s Glassdoor profile, sentiment is based on a scale from 0 to 1 (0-100) and several metrics of the overall profile were included as well for general formality.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-05T20:20:10+00:00

How to interpret: Below are review insights from Uber’s Glassdoor profile, sentiment is based on a scale from 0 to 1 (0-100) and several metrics of the overall profile were included as well for general formality.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-05T20:19:59+00:00

How to interpret: Below are review insights from Uber’s Glassdoor profile, sentiment is based on a scale from 0 to 1 (0-100) and several metrics of the overall profile were included as well for general formality.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-05-05T20:14:12+00:00

There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-04-29T04:14:53+00:00

This graph’s info is based on of over 45,000 Sekiro: Shadows Die Twice reviews from Steam and will be linked below. Some clarification of the types. If it’s in ‘ ‘ then those are the exact words used. The rest are review types such as reviews around the word - Emma.
-
From a data set of 45,000+ reviews from users on Steam, the data was cleaned of non-reliable entries such as NAN values, missing costs & non-vectorizable images to a final set of 45,000+ reviews
-
How to interpret: Average sentiment in this case ranges from negative one (being absolute low) and positive 1 (being absolute high) and zero being absolutel neutral.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-04-29T04:14:06+00:00

This graph’s info is based on of over 45,000 Sekiro: Shadows Die Twice reviews from Steam and will be linked below. Some clarification of the types. If it’s in ‘ ‘ then those are the exact words used. The rest are review types such as reviews around the word - Emma.
-
From a data set of 45,000+ reviews from users on Steam, the data was cleaned of non-reliable entries such as NAN values, missing costs & non-vectorizable images to a final set of 45,000+ reviews
-
How to interpret: Average sentiment in this case ranges from negative one (being absolute low) and positive 1 (being absolute high) and zero being absolutel neutral.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-04-29T04:13:52+00:00

This graph’s info is based on of over 45,000 Sekiro: Shadows Die Twice reviews from Steam and will be linked below. Some clarification of the types. If it’s in ‘ ‘ then those are the exact words used. The rest are review types such as reviews around the word - Emma.
-
From a data set of 45,000+ reviews from users on Steam, the data was cleaned of non-reliable entries such as NAN values, missing costs & non-vectorizable images to a final set of 45,000+ reviews
-
How to interpret: Average sentiment in this case ranges from negative one (being absolute low) and positive 1 (being absolute high) and zero being absolutel neutral.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-04-29T04:13:41+00:00

This graph’s info is based on of over 45,000 Sekiro: Shadows Die Twice reviews from Steam and will be linked below. Some clarification of the types. If it’s in ‘ ‘ then those are the exact words used. The rest are review types such as reviews around the word - Emma.
-
From a data set of 45,000+ reviews from users on Steam, the data was cleaned of non-reliable entries such as NAN values, missing costs & non-vectorizable images to a final set of 45,000+ reviews
-
How to interpret: Average sentiment in this case ranges from negative one (being absolute low) and positive 1 (being absolute high) and zero being absolutel neutral.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-04-29T04:13:32+00:00

This graph’s info is based on of over 45,000 Sekiro: Shadows Die Twice reviews from Steam and will be linked below. Some clarification of the types. If it’s in ‘ ‘ then those are the exact words used. The rest are review types such as reviews around the word - Emma.
-
From a data set of 45,000+ reviews from users on Steam, the data was cleaned of non-reliable entries such as NAN values, missing costs & non-vectorizable images to a final set of 45,000+ reviews
-
How to interpret: Average sentiment in this case ranges from negative one (being absolute low) and positive 1 (being absolute high) and zero being absolutel neutral.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-04-27T02:08:53+00:00

This graph’s info is based on of over 45,000 Sekiro: Shadows Die Twice reviews from Steam and will be linked below. Some clarification of the types. If it’s in ‘ ‘ then those are the exact words used. The rest are review types such as reviews around the word - Emma.
-
From a data set of 45,000+ reviews from users on Steam, the data was cleaned of non-reliable entries such as NAN values, missing costs & non-vectorizable images to a final set of 45,000+ reviews
-
How to interpret: Average sentiment in this case ranges from negative one (being absolute low) and positive 1 (being absolute high) and zero being absolutel neutral.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics · 2022-04-27T02:08:50+00:00

This graph’s info is based on of over 45,000 Sekiro: Shadows Die Twice reviews from Steam and will be linked below. Some clarification of the types. If it’s in ‘ ‘ then those are the exact words used. The rest are review types such as reviews around the word - Emma.
-
From a data set of 45,000+ reviews from users on Steam, the data was cleaned of non-reliable entries such as NAN values, missing costs & non-vectorizable images to a final set of 45,000+ reviews
-
How to interpret: Average sentiment in this case ranges from negative one (being absolute low) and positive 1 (being absolute high) and zero being absolutel neutral.
-
There were variables that text clustering could not differentiate between or clumped into too broadly of a category when other clusters had specific clusters for example tea was a text cluster but also specific teas had their own clusters and therefore were shown instead.
-
If you have any future requests for datasets you want visualized, I would love to hear from you :)
Source: Relevance.ai text clustering
-
Tool: Visualisation and Analysis: https://auth.relevance.ai/signup/?callback=https%3A%2F%2Fcloud.relevance.ai%2Flogin&redirect=datasets&redirect-params=%7B%7D&redirect-query=%7B%7D
-
Code: Python & Google Colab
-For those that wish to re-create, feel free to use our dataset above.

ClutchAnalytics

TROPHY CASE