Impact Analysis Using Correlation Model

This Bot measures, statistical parametric correlation operation among multiple numerical variables and provides the impact and degree of relationship.

Top Benefits

  • Improve Decision Making: Better decision making for the real world applications.
  • Increase Productivity: Save approx 30% of time in feature selection process and automation workflow creation.
  • Zero Maintenance: Model is provided as SaaS using serverless function architecture of Azure.
  • Runs in Cloud: Model runs in Azure and provides output without needing an infrastructure to run.
  • Ease of use: Model is easy to setup, highly scalable, fault tolerant (Each request is independent of another request).

Tasks

  • The correlation Bot performs parametric 'Pearson correlation' and provides correlation coefficients (r) among variables.
  • Input CSV file is provided to the model and processed MS Excel output is retrieved.
  • Help in understanding extent of relationship among numerical variables to make data driven business decisions.

Inputs:
1. Path: Path of the CSV file. For example: C:~Housing.csv
2. column-count: Count of columns available in CSV file being provided with input data.
3. InputColumnNames: Name of columns being provided in CSV file. For example 'SNo','Price','Lotsize','Bedrooms'......
4. InputColumnDataType: Data Type of columns being provided in CSV file in the same order as column names. For example: 'Numeric','Numeric','Numeric'......
5. MissingNumberValueReplacement: A value that can be used by Model in case CSV file has blank field/s or missing numerical value/s.
6. IgnoreColumns: Column name that the model should ignore while calculating correlation. For example: 'SNo','Driveway','Recroom'......

Actions:
The Bot takes CSV file as an input, performs parametric correlation operation on numerical variables and provides output in the MS Excel to exhibit the extent of relationship ('not causality').

Outputs:
1. Output: the result in the form of the MS Excel.
2. Column field1 and field2 has the column names on which correlation has been processed.
3. Column correlation has the extent of relationship for each combination.

NOTES: Output correlation coefficients (r) for continuous (interval level) data ranges in a range of -1 to +1. Refer to readme file for more details.

Get Bot

Free

Bot Security Program
Level 2
Applications
Business Process
Category
Vendor
Automation Type
Bot
Last Updated
July 24, 2020
First Published
April 9, 2020
Enterprise Version
11.3
Community Version
11.3.1
ReadMe
ReadMe
Support

See the Bot in Action

Code
Correlation Input
Output Sample
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Code
Correlation Input
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.
  • ColumnCount: Count of columns available in CSV file being provided with input data.
  • InputColumnNames: Name of columns being provided in CSV file.
  • InputColumnDataType: Data Type of columns being provided in CSV file in the same order as column names.
  • MissingNumberValueReplacement: A value that can be used by Model in case CSV file has blank field/s.
  • IgnoreColumns: Column name that the model should ignore while calculating correlation.