Practical-3

 

Aim: Orange tool

Theory:-

Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc.

Advantages of Orange Tool:-

    1. No need of learning the programming languages as it's visual programming.

    2. Data visualization becomes easier using orange tool.

    3. Much easier for implemeting different machine learning algorithms.

    4. Add-ons for: bioinformatics, network analysis, text mining.

    5. It is an Open-Source tool.

 

Disadvantages of Orange Tool:-

    1. Installation requires a huge data to download.

    2. It has very limited capablities of reporting

 

Dataset Description:-

How to do Basic Data Exploration?

    To perform the basic data exploration, firstly we need to do is to import data using file widget.

After importing file widget, select the data which you want to implement on. Then select data info widget and double click the widget and a new tab will open with the basic information.

  Now, to observe the data set in tabular form, select Data table widget and connect it with the file widget and then click on data table towidget to get the following result.

To Distribute or split data in different sets for training and testing purpose, Data Sampler can be used and then connect data table and then in table menu select all rows.

How to load your data in Orange and how to load external data from API in Orange?

    There are 3 different ways to load data in Orange:

        1. From file

        2. CSV file format

        3. Datasets

    File:-

        Reads attribute-value data from an input file.

        The File widget reads the input data file and sends the datasets to it's output channel.

CSV File Format:-

        Import a data table from a CSV fromatted file.

        Outputs:

            1. Data: datasets from the.csv file.

            2. Data Frame: Pandas Data frame object.

        It reads comma-separates files and sends the dataset to the respective output channel. File separators such as commas, semicolons, spaces, tabs or manually-defined delimeters can be done. 

Datasets:-

        

        Load a dataset from an online repository.

        Outputs

        Data: Output Dataset

        Datasets widget retrives selected dataset from the server to send to the output. The file is then downloaded to the offline storage and thus can be instantly available even without internet.