Machine Learning Data Visualization

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Data Visualization

Data visualization refers to techniques used to communicate insights from data through visual representation. Its main goal is to distill large datasets into visual graphics to allow for an easy understanding of complex relationships within the data. It is often used interchangeably with terms such as information graphics, statistical graphics, and information visualization. Following are the most commonly used graphs for the purpose of data visualization:


1. Histogram
2. Bar Plot
3. Scatter Plot
4. Pie Plot
5. Area Plot

1. Histograms
Histogram refers to a graphical representation, that displays data by way of bars to show the frequency of numerical data. It helps to visualize the distribution of non-discrete variables. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2. Barplot

Bar graph is a pictorial representation of data that uses bars to compare different categories of data. Bar graphs are used for doing a comparison of discrete variables/categorical variables.

 

 

 

 

 

 

 

 

 

 

 

 

 

3. Scatter Plot
Scatter plots are used to plot data points on a horizontal and vertical axis in the attempt to show how much one variable is affected by another. Each row in the data table is represented by a marker the position depends on its values in the columns set on the X and Y axes. A third variable can be set to correspond to the color or size of the markers, thus adding yet another dimension to the plot.

4. Pie Plot
A Pie Chart can only display one series of data. Pie charts show the size of items (called wedge) in one data series, proportional to the sum of the items. The data points in a pie chart are shown as a percentage of the whole pie.

 

 

 

 

5. Area Plot
Area charts can be used to plot change over time (years, months and days) or categories and draw attention to the total value across a trend. By showing the sum of the plotted values, an area chart also shows the relationship of parts to a whole. An example plot is given below

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