Support Vector Machine

Business users could use this bot to classify large volume high dimensional data to solve business classification problems (Principles of Support Vector Machine).

Top Benefits

  • Increase Productivity: SVM Model helps in increasing the analytics efficiency by 30%.
  • Quantitative Customer Preference : Helps to analyze the polarity of the customer preference for targeting.
  • Zero Maintenance: Model is provided as SaaS using serverless function architecture of Azure.

Tasks

  • Support vector machines (SVMs) are powerful yet flexible supervised machine learning algorithms.
  • SVM extremely effective in its ability to model complex problems using non-linear classification.
  • The Bot ingests labeled input data in CSV file and performs the SVM algorithm driven classification.

Inputs:
• Path: Path of the CSV file Ex.: C:~Marketing-Customer-Value-Analysis.csv
• NoOfColumn: Number of Columns in the CSV file
• InputColumn: Header of the input columns of the CSV file Ex: 'Customer ID','Coverage','Gender','Income','Marital Status','Monthly Premium Auto','Months Since Last Claim','Policy Type','Sales Channel','Total Claim Amount','Vehicle Class','Vehicle Size','Response'
• InputColumnDataType: Data Types of the input column (Available datatype: Numeric, String, Date) Ex: 'String','String','String','Numeric','String','Numeric','Numeric','String','String','Numeric','String','String','String'
• 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.  Ex: Marital Status
• PredictionColumnName: Header of the column on which you want to predict Ex: 'Response'
Ex: The below examples shows that the responses of the customer value analysis for the car and getting the response in yes or no i.e if the customer is satisfied or not

Outputs:
• Output: the result in the form of the Excel
• Binary class prediction output
Ex: The below the example shows the two classes explained below:
○ Gold_Class: This column is taken as the predicted target
○ Predicted_Class: This tells us the response of the customer

Get Bot

Free

Bot Security Program
Level 1
Applications
Business Process
Category
Vendor
Automation Type
Bot
Last Updated
June 26, 2020
First Published
June 26, 2020
Enterprise Version
11.x
Community Version
11.3.1
ReadMe
ReadMe
Support

See the Bot in Action

Code
Input Sample
Output Sample
PREV NEXT
Code
Input Sample
Output Sample

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.