Naive Bayes Model

This Bot performs text classification and real-time predictions of various analytics problems using Naive Bayes as a Machine Learning model.

Top Benefits

  • No ML skill required: Anyone with business understanding of data can create workflow and execute model.
  • Ease of use: Model is easy to setup, highly scalable, fault tolerant (Each request is independent of another request).
  • Zero Maintenance: Model is provided as SaaS using serverless function architecture of Azure.


  • As Naive Bayes is superfast, it can be used for making quick predictions in real time.
  • Naive Bayes algorithm affords fast, highly scalable model building and scoring.
  • Naive Bayes algorithms are mostly used in sentiment analysis, spam filtering, recommendation systems etc.
  • The Bot takes in the CSV file and performs the naïve bayes-based classification and output in the MS Excel.

Path: Path of the CSV file Ex.: C:~Womens Clothing E-Commerce Reviews.csv
ColumnCount: Count of columns available in CSV file being provided with input data
InputColumnNames: Name of columns being provided in CSV file. For example 'SNo','Clothing ID','Age','Title','Review Text','Rating','Recommended IND','Positive Feedback Count','Division Name','Department Name','Class Name'
InputColumnDataType: Data Type of columns being provided in CSV file in the same order as column names. For example 'Numeric','Numeric','Numeric','String','String','Numeric','Numeric','Numeric','String','String','String'
MissingNumberValueReplacement: A value that can be used by Model incase CSV file has blank field or missing a number value.
MissingStringValueReplacement: A value that can be used by Model in case CSV file has a blank field or missing string value.
IgnoreColumns: Names of the column that model shall ignore while calculating correlation. For example 'SNo', 'Title', 'Review Text'
PredictionColumnName: Header of the column on which you want to predict Ex: Department Name (Input) and Gold Class (Output)

Input Ex: The below dataset contains information about the Women's clothing i.e. basically which type of clothes women mostly wear and this is the dataset for E-Commerce Company for the production of the clothes. The following data is described using the mentioned features.

Output: the result in the form of the MS Excel
Using the above dataset we are predicting the type of dress women mostly likes to wear and thus the below predicted has been the output with some of the different features described :
§ Gold_Class: This column is taken as the predicted target based on preference
§ Predicted_Class: This tells us the predicted type of the women cloth that she likes to wear i.e. Tops, Bottoms, etc based on preference
Provided the predictive class for effective decision making

Get Bot


Bot Security Program
Level 1
Business Process
Automation Type
Last Updated
July 24, 2020
First Published
June 26, 2020
Enterprise Version
Community Version

See the Bot in Action

Input Sample
Output Sample
Input Sample
Output Sample

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.