Practical - 3
Practical 3
Aim: Different Visualization of Data using Orange Tool
Theory: Orange is an open-source data visualization and data analysis tool for data mining through visual programming or Python scripting. The tool has components for almost all well-known machine learning algorithms, add-ons for bioinformatics and text mining as well as features for data analytics also.
Theory: Orange is an open-source data visualization and data analysis tool for data mining through visual programming or Python scripting. The tool has components for almost all well-known machine learning algorithms, add-ons for bioinformatics and text mining as well as features for data analytics also.
Advantages:
- It is an open-source data mining package build on Python, NumPy, wrapped C, C++, and Qt.
- Works both as a script and with an ETL workflow GUI.
- The shortest script for doing training, cross-validation, algorithms comparison, and prediction.
- Orange the easiest tool to learn.
- Cross-platform GUI.
- Orange is written in python hence is easier for most programmers to learn.
- Has a better debugger. Scripting data mining categorization problems are simpler in Orange.
- Orange does not give optimum performance for association rules.
- It is an open-source data mining package build on Python, NumPy, wrapped C, C++, and Qt.
- Works both as a script and with an ETL workflow GUI.
- The shortest script for doing training, cross-validation, algorithms comparison, and prediction.
- Orange the easiest tool to learn.
- Cross-platform GUI.
- Orange is written in python hence is easier for most programmers to learn.
- Has a better debugger. Scripting data mining categorization problems are simpler in Orange.
- Orange does not give optimum performance for association rules.
Limitations:
- Not super polished.
- The install is big since you need to install QT.
- A limited list of machine learning algorithms.
- Machine learning is not handled uniformly between the different libraries.
- Orange is weak in classical statistics; although it can compute basic statistical properties of the data, it provides no widgets for statistical testing.
- Reporting capabilities are limited to exporting visual representations of data models.
Import the data:
1.Add file widget to the canvas and add the dataset file.
2.Add different visualization widgets and connect them with file.
3.Analize your output
Scatter plot
Box plot
Line Plot
Distribution plot
Linear Projection
- Not super polished.
- The install is big since you need to install QT.
- A limited list of machine learning algorithms.
- Machine learning is not handled uniformly between the different libraries.
- Orange is weak in classical statistics; although it can compute basic statistical properties of the data, it provides no widgets for statistical testing.
- Reporting capabilities are limited to exporting visual representations of data models.
Import the data:
1.Add file widget to the canvas and add the dataset file.
2.Add different visualization widgets and connect them with file.
3.Analize your output
Scatter plot
Box plot
Line Plot
Distribution plot
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