This project aims to create a chatbot using Python that can understand and respond to user queries. The chatbot will be designed to handle predefined intents and respond appropriately based on the user's input.
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Data Loading and Preprocessing:
- The dataset containing predefined intents and associated patterns is loaded from a JSON file.
- Data preprocessing involves tokenization, lemmatization, and preparation for training.
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Neural Network Model Construction:
- Keras is used to build a feedforward neural network model for intent classification.
- The model architecture consists of dense layers with ReLU activation functions and dropout layers for regularization.
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Model Training:
- The model is trained using the prepared dataset, optimizing parameters to minimize categorical cross-entropy loss.
- Training involves multiple epochs and evaluation based on accuracy metrics.
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Saving the Model:
- Once trained, the model is saved along with vocabulary and intent labels using pickle serialization.
- A functional chatbot capable of understanding user queries and providing appropriate responses based on predefined intents.
- Potential for further improvement through fine-tuning, optimization, and integration with advanced NLP techniques.
- Integration into websites, messaging apps, or customer service portals for automated assistance and FAQs handling.
- Natural Language Processing (NLP)
- Neural Network Modeling
- Data Preprocessing
- Model Evaluation and Optimization
- Serialization and Persistence
- Python
- NLTK
- Keras
- JSON
- Pickle
- Integration with speech recognition for voice-based interactions.
- Implementation of context-awareness and sentiment analysis for improved responses.
- Clone this repository.
- Install the required dependencies using
pip install -r requirements.txt. - Run the script to train the chatbot model.
- Interact with the chatbot by providing input queries.
- Mohamed Saifeldin msaifeldin46@gmail.com
This project is licensed under the MIT License - see the LICENSE file for details.