D3.JS and Data Visualization Assignment Help
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Data Visualization Using D3.js
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Features Of D3.js Visualization
Uses Web Standards: D3 is an extremely powerful visualization tool to create interactive data visualizations. It exploits the modern web standards: SVG, HTML and CSS to create data visualization.
Data Driven: D3 is data driven. It can use static data or fetch it from the remote server in different formats such as Arrays, Objects, CSV, JSON, XML etc. to create different types of charts.
DOM Manipulation: D3 allows you to manipulate the Document Object Model (DOM) based on your data.
Data Driven Elements: It empowers your data to dynamically generate elements and apply styles to the elements, be it a table, a graph or any other HTML element and/or group of elements.
Dynamic Properties: D3 gives the flexibility to provide dynamic properties to most of its functions. Properties can be specified as functions of data. That means your data can drive your styles and attributes.
Types of visualization: With D3, there are no standard visualization formats. But it enables you to create anything from an HTML table to a Pie chart, from graphs and bar charts to geospatial maps.
Custom Visualizations: Since D3 works with web standards, it gives you complete control over your visualization features.
Transitions: D3 provides the transition() function. This is quite powerful because internally, D3 works out the logic to interpolate between your values and find the intermittent states.
Interaction and animation: D3 provides great support for animation with functions like duration(), delay() and ease(). Animations from one state to another are fast and responsive to user interactions.
D3.js Visualization Real Life Applications
Stoppage made by Police in January 2012 in New York
Visual Introduction to Machine Learning
Race Track leads to Victory
Connections between Oscar Contenders
Get Help To Create Graph In D3.js Visualization
D3.js is used to create a static SVG chart:
Bubble Chart, etc.
Exploratory Data Analysis (EDA) Help
1. The initial process in any machine learning implementation
2. The purpose is to understand the data, interpret the hidden information, visualizing and engineering the feature to be used by the machine learning
3. A few things to consider:
– What questions do you want to answer or prove true/wrong?
– What kind of data do you have? Numeric, Categorical, Text, Image? How are you going to treat them.
– Do you have any missing values, wrong format, etc.
– How the data is spread? Do you have any outliers? How are you going to deal with them?
– Which features are important?
– Can we add or remove features to get more from the data?
4. Data Wrangling
– Understand the data
– Getting basic summary statistics
– Handling missing values
– Handling outliers
– Typecasting and transformation
5. Data Visualization
– Univariate Analysis: histogram, distribution (distplot, boxplot, violin)
– Multivariate Analysis: scatter plot, pair plot, etc
Get Help In Feature Engineering
1. Why Feature Engineering?
– Better representation of data
– Better performing models
– Essential for model building and evaluation
– More flexibility on data types
– Emphasis on the business and domain
2. Types of data for feature engineering ranges from numerical, categorical, text, temporal, and image
– Numerical Data
– Categorical Data
Data Analysis Using Package Commands
Here below some important commands that useful to analyze the data:
csvstat: provide a broad understanding of data by generating summary statistics for all the data in a CSV file
csvcut –c 2,3,5 filename.csv | csvstat Will give general summary stats on the chosen columns Stats include: data type, has empty cells, # of unique values, max length, 5 top frequent values
csvgrep: search for matches across columns csvcut –c 2,3,5 filename.csv | csvgrep –c 2 –m testme | csvlook
csvsort: sort records based on column(s) csvcut –c 2,3,5 filename.csv | csvgrep –c 2 –m testme | csvsort –c 3 –r | csvlook
csvstack: stack two files on each other
csvformat: format files with different delimiters and add quotations
csvformat -D \| filename.csv ## change , to |
csvformat -T data.csv ## change , to tab
csvformat -U 1 data.csv ## quote cells
csvformat -D \& -Q \$ -U 2 -M \* data.csv ## Ampersand- delimited, dollar-signs for quotes, quote all strings, and asterisk for line endings:
csvclean: reports rows that have a different number of columns than the header row and attempts to correct the CSV by joining short rows into a single row