Python-Based Chatbot with Rasa Framework

This project builds a task-oriented chatbot using the Rasa open-source framework, designed for a customer-support use case such as answering FAQs, checking order status, and escalating to a human agent when needed. Rasa NLU is trained on a custom set of intents and entities (e.g., order_id, product_name) extracted from sample user utterances.

Dialogue flow is managed through Rasa's story/rule system, which defines how the bot should respond across multi-turn conversations, including handling interruptions and fallback responses when the bot doesn't understand the user's message with sufficient confidence.

The chatbot is connected to a simple web front end via Rasa's REST channel, and the project includes an evaluation of intent-classification accuracy and a discussion of how the confidence-threshold fallback mechanism reduces incorrect automated responses in a production-style deployment.

Detailed Project Description

Python-Based Chatbot with Rasa Framework applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- Rasa
- NLU
- Chatbot
- Dialogue Management

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Rasa Open Source, SpaCy) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface