- 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
- Bot Security Program
- Business Process
- Finance & AccountingSales
- Cognitive AutomationInsightsProductivity
- Automation Type
- Last Updated
- November 19, 2020
- First Published
- June 26, 2020
- Community Version
See the Bot in Action
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.