Data visualization is one of the most fascinating fields in Data Science. Sometimes, using a good plot or graphical representation can make us better understand the information hidden inside data. How can we do it with more than 2 dimensions?
As long as we work with two-dimensional datasets, a simple scatterplot can be quite useful to visualize patterns and events. If we work with three-dimensional data there’s still some chance to visualize something using 3d plots.
But what happens if we want to visualize higher-dimensional datasets? Things can become more difficult. Think about clustering problems. It would be very wonderful if we could visualize data in many dimensions in order to check whether there are some patterns or not.
Of course, we don’t have a multidimensional vision, so we must transform multidimensional data into 2d data. An algorithm able to do it is MDS.
#data-visualization #python #artificial-intelligence #data-science #machine-learning
According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.
Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.
To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.
The dataset is currently available on GitHub.
#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets
Visual Analytics is the scientific visualization to emerge an idea to present data in such a way so that it could be easily determined by anyone.
It gives an idea to the human mind to directly interact with interactive visuals which could help in making decisions easy and fast.
Visual Analytics basically breaks the complex data in a simple way.
The human brain is fast and is built to process things faster. So Data visualization provides its way to make things easy for students, researchers, mathematicians, scientists e
#blogs #data visualization #business analytics #data visualization techniques #visual analytics #visualizing ml models
Visual analytics is the process of collecting, examining complex and large data sets (structured or unstructured) to get useful information to draw conclusions about the datasets and visualize the data or information in the form of interactive visual interfaces and graphical manner.
Data analytics is usually accomplished by extracting or collecting data from different data sources in the form of numbers, statistics and overall activity of any organization, with different deep learning and analytics tools, which is then processed using data visualization software and presented in the form of graphical charts, figures, and bars.
In today technology world, data are reproduced in incredible rate and amount. Visual Analytics helps the world to make the vast and complex amount of data useful and readable. Visual Analytics is the process to collect and store the data at a faster rate than analyze the data and make it helpful.
As human brain process visual content better than it processes plain text. So using advanced visual interfaces, humans may directly interact with the data analysis capabilities of today’s computers and allow them to make well-informed decisions in complex situations.
It allows you to create beautiful, interactive dashboards or reports that are immediately available on the web or a mobile device. The tool has a Data Explorer that makes it easy for the novice analyst to create forecasts, decision trees, or other fancy statistical methods.
#blogs #data visualization #data visualization tools #visual analytics #visualizing ml models
The goal of palmerpenguins is to provide a great dataset for data exploration & visualization, as an alternative to iris.
You can install the development version from
# install.packages("remotes") remotes::install_github("allisonhorst/palmerpenguins")
Data were collected and made available by Dr. Kristen
and the Palmer Station, Antarctica LTER, a
member of the Long Term Ecological Research
The palmerpenguins package contains two datasets.
library(palmerpenguins) data(package = 'palmerpenguins')
One is called
penguins, and is a simplified version of the raw data;
?penguins for more info:
head(penguins) #> # A tibble: 6 x 8 #> species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex #> <fct> <fct> <dbl> <dbl> <int> <int> <fct> #> 1 Adelie Torge… 39.1 18.7 181 3750 male #> 2 Adelie Torge… 39.5 17.4 186 3800 fema… #> 3 Adelie Torge… 40.3 18 195 3250 fema… #> 4 Adelie Torge… NA NA NA NA <NA> #> 5 Adelie Torge… 36.7 19.3 193 3450 fema… #> 6 Adelie Torge… 39.3 20.6 190 3650 male #> # … with 1 more variable: year <int>
The second dataset is
penguins_raw, and contains all the variables and
original names as downloaded; see
?penguins_raw for more info.
head(penguins_raw) #> # A tibble: 6 x 17 #> studyName `Sample Number` Species Region Island Stage `Individual ID` #> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> #> 1 PAL0708 1 Adelie… Anvers Torge… Adul… N1A1 #> 2 PAL0708 2 Adelie… Anvers Torge… Adul… N1A2 #> 3 PAL0708 3 Adelie… Anvers Torge… Adul… N2A1 #> 4 PAL0708 4 Adelie… Anvers Torge… Adul… N2A2 #> 5 PAL0708 5 Adelie… Anvers Torge… Adul… N3A1 #> 6 PAL0708 6 Adelie… Anvers Torge… Adul… N3A2 #> # … with 10 more variables: `Clutch Completion` <chr>, `Date Egg` <date>, #> # `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>, `Flipper Length #> # (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>, `Delta 15 N (o/oo)` <dbl>, #> # `Delta 13 C (o/oo)` <dbl>, Comments <chr>
Both datasets contain data for 344 penguins. There are 3 different
species of penguins in this dataset, collected from 3 islands in the
Palmer Archipelago, Antarctica.
str(penguins) #> tibble [344 × 8] (S3: tbl_df/tbl/data.frame) #> $ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ... #> $ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ... #> $ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ... #> $ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ... #> $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ... #> $ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ... #> $ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ... #> $ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
Thank you to Dr. Gorman, Palmer Station LTER and the LTER Network!
Special thanks to Marty Downs (Director, LTER Network Office) for help
regarding the data license & use.
You can find these and more code examples for exploring palmerpenguins
Penguins are fun to summarize! For example:
library(tidyverse) penguins %>% count(species) #> # A tibble: 3 x 2 #> species n #> <fct> <int> #> 1 Adelie 152 #> 2 Chinstrap 68 #> 3 Gentoo 124 penguins %>% group_by(species) %>% summarize(across(where(is.numeric), mean, na.rm = TRUE)) #> # A tibble: 3 x 6 #> species bill_length_mm bill_depth_mm flipper_length_mm body_mass_g year #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Adelie 38.8 18.3 190\. 3701\. 2008. #> 2 Chinstrap 48.8 18.4 196\. 3733\. 2008. #> 3 Gentoo 47.5 15.0 217\. 5076\. 2008.
Penguins are fun to visualize! For example:
#data visualization #visual studio code #visual studio
Why do we visualize data?
It helps us to comprehend _huge _amounts of data by compressing it into a simple, easy to understand visualization. It helps us to find hidden patterns or see underlying problems in the data itself which might not have been obvious without a good chart.
Our brain is specialized to perceive the physical world around us as efficiently as possible. Evidence also suggests that we all develop the same visual systems, regardless of our environment or culture. This suggests that the development of the visual system isn’t solely based on our environment but is the result of millions of years of evolution. Which would contradict the tabula rasa theory (Ware 2021 ). Sorry John Locke. Our visual system splits tasks and thus has specialized regions that are responsible for segmentation (early rapid-processing), edge orientation detection, or color and light perception. We are able to extract features and find patterns with ease.
It is interesting that on a higher level of visual perception (visual cognition), our brains are able to highlight colors and shapes to focus on certain aspects. If we search for red-colored highways on a road map, we can use our visual cognition to highlight the red roads and put the other colors in the background. (Ware 2021)
#data-visualization #gestalt-principles #visualization #data-science #visual-variables