Random Forest Classifier

Use the Random Forest Classifier bot enable bots to perform machine learning classification in an effort to accelerate predictions and increase data driven decisions making.

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.


  • The bot is based on Random Forest Classifier which is a supervised learning algorithm.
  • Use this bot as an essential companion for data driven stock selection strategy.
  • Provide the bot with data in a CSV format and wait for the predicted responses which can be saved in an Excel document.

The Random Forest Classifier enables developers to combine the benefits of supervised learning with the power of RPA to drive better decision making, perform cognitive automations, and proactively respond to market/resource/sector changes.

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


Bot Security Program
Level 1
Business Process
Automation Type
Last Updated
November 19, 2020
First Published
June 26, 2020
Community Version

See the Bot in Action

Bot Logic
Sample Input File
Sample Output File
Bot Logic
Sample Input File
Sample Output File

Setup Process


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


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


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.