Random Forest Classifier

A business user could use this bot, which is based on the principles of ‘Random Forest’ Classifier and provide output in the MS excel.

Top Benefits

  • Increase Productivity: Expedites business decision process time by 30%.
  • Quantitative Stock Selection Strategy: Helpful for quantitative traders in optimized decision making.
  • Zero Maintenance: Model is provided as SaaS using serverless function architecture of Azure.

Tasks

  • The bot is based on Random Forest Classifier which is a supervised learning algorithm.
  • The bot is an essential companion for data driven stock selection strategy.
  • Bot takes in the input data in CSV file & performs Random Forest Classifier methods to provide output in the MS excel.

Inputs:
Path: Path of the CSV file Ex.: C:~Stock Market.csv
NoOfColumn: Number of Columns in the CSV file
InputColumn: Header of the input columns of the CSV file Ex: 'Share','Category','Sector','RM','Up','Last Traded Price','Percentage Change','High Price','Low Price','% High Movt','% Low movt','Yearly Gainner','27th Dec','1st Feb','1st March','1st April','TB','RH','Corr','PeRatio','New Pe','W52_High','Corre','latest','Annual_Pat','Cum PAT 3 Quarter','Pat Jump','Annual Growth','Year End','Mar_17_ReportedPAT'
InputColumnDataType: Data Types of the input column (Available datatype: Numeric, String, Date) Ex: 'String','String','String','String','String','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','Numeric','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: 'Sector'
PredictionColumnName: Header of the Column on which you want to predict Ex: 'Category'
Input Ex:
The second column, Category, gives a list of all the stocks that a user needs to buy, hold, or exit

Outputs:
Output: the result in the form of the Excel
Predicts the suitable stock
Ex: The Below result shows the predicted category of stock and those predictions are made on the below class:

○ Gold_Class: This column is taken as the predicted target
○ Predicted_Class: This tells us the Category of the Share in which it falls
Below are the definition of the Categories:
○ Exit- Sell the stock
○ Buy- Buy the stock
○ RS- (Relative Strength) It compares a stock's price performance versus the S&P 500 index
○ NL-It states that the share belongs to no-load fund

Get Bot

Free

Bot Security Program
Level 1
Applications
Business Process
Category
Vendor
Automation Type
Bot
Last Updated
July 24, 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
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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.
  • PredictionColumnName: Header of the Column on which you want to predict.