Gaussian Mixture Model

Bot takes in input CSV file & prepare the clusters using the Gaussian model where sub-population exists within normally distributed whole population & output Excel.

Top Benefits

  • Gaussian Models are powerful clustering algorithm & in order to represent a normally distributed sub-population.
  • GMM helps in rapid classification of objects and cases into relative clusters to augment analysis process up to 40%.
  • Clusters created by this bot could be used to increase operations efficiency, reduce cost, and improve sales.
  • API based access to this bot increases rolling out applications faster.

Tasks

  • Rapid segmentation of homogeneous sub-groups.
  • This bot model could be used for cluster and sub-groups creations for a customer analytics project.
  • Prepare CSV file to pass it to the model and processing MS Excel output.

Inputs:
• Path: Path of the CSV file  Ex.: C:~Wholesale customers.csv
• NoOfColumn: Number of Columns in the CSV file
• InputColumn: Header of the input columns of the CSV file Ex: 'Channel','Region','Fresh','Milk','Grocery','Frozen','Detergents_Paper','Delicassen'
• InputColumnDataType: Data Types of the input column (Available datatype: Numeric, String, Date) Ex: 'Numeric', 'Numeric', 'Numeric', 'Numeric', 'Numeric', 'Numeric', 'Numeric', 'Numeric'
• MissingNumberValueReplacement: By which value you have to replace the missing number values
• MissingStringValueReplacement: By what string you have to replace the missing string values
• MissingDateValueReplacement: By which date you have to replace the missing Date values (Format: YYYY-MM-DD) Ex: 2020-01-21
• IgnoreColumns: header of the Columns which you have to ignore in the model.  Ex: 'Detergents_Paper'
Input Ex:-
The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal.

Actions:
The Bot takes the CSV file and prepare the clusters using Gaussian mixture model

Outputs:
• Output: the result in the form of the MS Excel
• Viable Segments and segmental counts
Ex: The data has been divided into clusters using gaussian mixture model for the customer segmentation

Get Bot

Free

Bot Security Program
Level 1
Applications
Business Process
Category
Vendor
Automation Type
Bot
Last Updated
June 17, 2020
First Published
June 17, 2020
Enterprise Version
11.x
Community Version
11.3.1
ReadMe
ReadMe
Support

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Setup Process

Install

Download the Bot and follow the instructions to install it in your AAE Control Room.

Configure

Open the Bot to configure your username and other settings the Bot will need (see the Installation Guide or ReadMe for details.)

Run

That's it - now the Bot is ready to get going!

Requirements and Inputs

  • Path: Path of the CSV file.
  • NoOfColumn: Number of Columns in the CSV file.
  • InputColumn: Header of the input columns of the CSV file.
  • InputColumnDataType: Data Types of the input column (Available datatype: Numeric, String, Date).
  • MissingNumberValueReplacement: By which value you have to replace the missing number values.
  • MissingStringValueReplacement: By what string you have to replace the missing string values.
  • IgnoreColumns: header of the Columns which you have to ignore in the model.
  • PredictionColumnName: Header of the Column on which you want to predict.