E-Commerce Chatbot Development using Rasa NLU

Business Goal

This project focuses on constructing an advanced chatbot for an E-Commerce platform using Rasa NLU. A chatbot serves as a versatile tool for emulating and handling human-like conversations, ranging from rule-based responses to sophisticated machine learning-driven interactions.

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Chatbot Categories

1. Rule-based Chatbots: Designed for structured queries, adhering to predefined rules.
2. AI-based Chatbots: Incorporating machine learning, our project falls into this category, emphasizing understanding context and delivering natural language responses.

The efficacy of AI-driven chatbots hinges on grasping two fundamental elements:

• Intent: User message's intention or purpose.
• Entity: A specific extractable data point or value from a conversation.

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Data Insight

Our E-Commerce chatbot derives data from sources like the Rasa NLU Trainer and Chatito. The critical aspects considered for our business case are:

• Intents: product_info, ask_price, cancel_order
• Entities: product, location, order_id

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Technological Arsenal

• Language: Python
• Libraries: pandas, matplotlib, Rasa, pymongo, TensorFlow, spaCy

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Strategic Approach

1. Data Collection: Gather relevant data from diverse sources.
2. Library Integration: Import necessary packages and libraries.
3. Data Integration: Import and structure the acquired data.
4. Data Transformation: Convert data into training and testing dataframes.
5. Data Serialization: Convert dataframes to JSON files.
6. Exploratory Analysis:
   • Visualize data for insights.
7. Configuration Setup: Create YAML files for spaCy and TensorFlow configurations.
8. Model Development:
   • Establish a function for Rasa NLU model training.
9. Performance Evaluation:
   • Develop a function for model evaluation using test data.
10. Training Iterations:

• Train the model using spaCy and TensorFlow pipelines.

11. Evaluation Metrics:

• Construct confusion matrices for both models.

12. Model Understanding:

• Interpretation of model decisions.

13. Database Integration:

• Install MongoDB and integrate pymongo.

14. Chatbot Components:

• Implement IntentFlow and ContextManager classes.

15. Message Processing:

• Develop a function for message processing.

16. Testing and Deployment:

• Validate the chatbot's functionality.

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Code Structure

1. Input Folder: Holds data and configuration files.
2. Src Folder: Encompasses modular code in Engine.py and ML_Pipeline directories.
3. Output Folder: Stores the optimized model for future deployment.
4. Lib Folder: Houses reference IPython notebook.

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