diff --git a/docs/projects/natural-language-processing/chatbot-project-implementation.md b/docs/projects/natural-language-processing/chatbot-project-implementation.md new file mode 100644 index 00000000..b71ff64d --- /dev/null +++ b/docs/projects/natural-language-processing/chatbot-project-implementation.md @@ -0,0 +1,180 @@ + +# Chatbot Implementation Project + +## AIM +To develop a chatbot using Natural Language Processing (NLP) and a Naive Bayes classifier for intent classification. The chatbot takes user input, predicts the intent, and generates an appropriate response based on predefined intents and responses stored in a CSV file. + + +### DATASET LINK +[https://drive.google.com/file/d/1J7mGS16EkgCEtN7UJtBlJACeqoDbdS4F/view?usp=drive_link](https://drive.google.com/file/d/1J7mGS16EkgCEtN7UJtBlJACeqoDbdS4F/view?usp=drive_link) + + +### NOTEBOOK LINK +[https://colab.research.google.com/drive/1L2LKfbVv4pb4yzczcRnnU4AkEW-kCZSZ?usp=sharing](https://colab.research.google.com/drive/1L2LKfbVv4pb4yzczcRnnU4AkEW-kCZSZ?usp=sharing) + + + +## LIBRARIES NEEDED + +??? quote "LIBRARIES USED" + - nltk + - scikit-learn + - numpy + - pickle + + +--- + + +### DESCRIPTION + +??? info "What is the Requirement of the Project?" + -A chatbot is required to automate conversations and provide immediate responses to user queries. It can be used to answer FAQs, provide customer support, and improve user interaction. + +??? info "Why is it Necessary?" + - Chatbots are essential for improving user engagement and providing 24/7 service. + - They automate responses, saving time and providing immediate help. + +??? info "How is it Beneficial and Used?" + - Chatbots can be used for: + 1. Customer service automation. + 2. Answering user questions. + 3. Guiding users through processes on websites or apps. + + +#### "Initial Thoughts and Planning" + - **Intents and Responses**: Gathered and stored in CSV format. + - **Preprocessing**: Tokenized and lemmatized text to prepare for model training. + - **Model**: Built a Naive Bayes classifier to predict intents. + - **Deployment**: Deployed the model to predict user queries and return appropriate responses. + +--- + +#### Additional Resources Used + - [Scikit-learn Documentation](https://scikit-learn.org) + - **Tutorial**: Building Chatbots with NLP and Machine Learning + +--- + +### FEATURES IN THE DATASET + +| Feature | Description | +|-----------|---------------------------------------------------| +| `intents` | User query categories like greetings, farewells. | +| `responses` | Predefined chatbot responses for each intent. | + +--- + +#### STEPS AND IMPLEMENTATION + +=== "step 1" + Data Preprocessing + - Loaded the intents from CSV files. + - Cleaned data by removing duplicates and handling null values. + + + +=== "step 2" + Vectorization + - Used `TfidfVectorizer` to convert text into vectors. + - Split data into training and testing sets. + + +=== "step 3" + Model Training + - Trained a Naive Bayes classifier on the preprocessed data. + - Saved the model for future use with `pickle`. + - Created an intent-response mapping. + +=== "step 4" + Prediction and Response Generation** + - The chatbot predicts the intent based on user input. + - Fetches and returns the appropriate response. + +=== "step 5" + Testing + - Conducted live interaction tests with the chatbot. + + +--- + +### Features Not Implemented Yet +- Integration of a deep learning model (e.g., RNN or LSTM) for better context handling. + +--- + + +### Flow Chart + +```mermaid +graph TD + A[Data Preprocessing] --> B[Vectorization] + B --> C[Model Training] + C --> D[Prediction and Response Generation] + D --> E[Testing the Chatbot] + + A1[Load intents from CSV] --> A2[Clean data: remove duplicates and handle nulls] + A --> A1 + A --> A2 + + B1[Use TfidfVectorizer to convert text into vectors] --> B2[Split data into training and testing sets] + B --> B1 + B --> B2 + + C1[Train Naive Bayes classifier] --> C2[Save model with pickle] --> C3[Create intent-response mapping] + C --> C1 + C --> C2 + C --> C3 + + D1[Chatbot predicts intent] --> D2[Fetch appropriate response based on intent] --> D3[Return response to user] + D --> D1 + D --> D2 + D --> D3 + + E1[Live interaction with chatbot] --> E2[Test accuracy and responses] + E --> E1 + E --> E2 +``` +``` + +#### Example Chatbot Interaction: + +```text +You: Hello +Bot: Hi, How can I assist you? +``` + +--- + +## MODELS AND EVALUATION METRICS + +| Model | Accuracy | Precision | Recall | +|------------------|----------|-----------|--------| +| Naive Bayes | 92% | 91% | 90% | + +--- + +### CONCLUSION + +??? "What Have You Learned?" + - Building a chatbot using NLP techniques can automate interactions and provide user-friendly interfaces for businesses. + -The Naive Bayes classifier is an effective yet simple model for intent prediction. + +--- + +#### USE CASES + +=== "Application 1" + + **Customer Support Automation** + + -Provide 24/7 automated support for customers. + + +=== "Application 2" + + **FAQ Automation** + + - Automatically respond to frequently asked questions on websites or apps. + + diff --git a/docs/projects/natural-language-processing/index.md b/docs/projects/natural-language-processing/index.md index 2ba3ae9c..b64b4bf8 100644 --- a/docs/projects/natural-language-processing/index.md +++ b/docs/projects/natural-language-processing/index.md @@ -1,2 +1,15 @@ -# Natural Language Processing 🗣️ +# Natural Language Processing 🗣️ +
+ + + + Chatbot Illustration +
+

Chatbot Implementation Project

+

Developing a modern chatbot system using NLP techniques and AI algorithms.

+

📅 2025-01-21 | ⏱️ 15 mins

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+ + +