Basic instructuctions

Input a text to receive response from Alfarvis

Enter Bye to close the program

Alfa is data exploration and analysis software that uses natural language

To begin data exploration, you should first load a dataset using the "load command". For example "load tour de france dataset" to load a cycling dataset

To get a list of datasets that can be loaded, call "list files"

Once you have loaded a dataset, you can perform different sets of commands : visualization, statistics, machine learning

You can list commands using "list commands" or "list visualization/statistics... commands" for specific command list

You can also get individual help for each of the commands by calling "help [command name]"

Apart from the above commands, there are also data handling commands that can set row labels, set reference for setting legends etc

You can get a list of data handling commands by calling "list data handling commands"

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Load the breast cancer dataset and set reference to diagnosis - Benign (1) vs Malignant (2)

User: load breast cancer dataset

Alfa:

Running command in background!

Loaded breast cancer tumor

Showing Statistics for breast cancer tumor

Loaded file: breast_tumor_data.csv

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User: set reference to diagnosis

Alfa:

Setting reference to diagnosis

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Run k means on the complete dataset using k = 2. ALFA automatically determines the k value from the input statement provided by the user

User: kmeans of breast cancer dataset using k = 2

Alfa:

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Alternatively, you can run k means on a selected set of features/variabels

User: k means of area compactness concave.points concavity and radius using k = 2

Alfa:

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Let's try k means for different values of k on the breast cancer dataset

User: k means on breast cancer dataset using k = 3

Alfa:

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User: k means on breast cancer dataset using k = 10

Alfa:

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ALFA provides support for other clustering algorithms such as spectral clustering and hierarchical clustering as well.

User: spectral clustering on breast cancer dataset using k = 2

Alfa:

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User: hierarchical clustering on breast cancer dataset using k = 2

Alfa:

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User: save notebook

Alfa: