- Denniz is an AI chatbot that simulates a conversation with a user by processing natural language input and providing relevant responses. It is built using Python's Natural Language Toolkit (NLTK) and scikit-learn libraries. The chatbot uses a combination of techniques such as text preprocessing, bag-of-words model, and tf-idf vectorization to understand and respond to the user's input.
- Denniz chatbot follows a rule-based approach to generate responses to user input. It uses a combination of techniques to preprocess the user's input, such as tokenization, stop-word removal, and stemming. Then, it creates a bag-of-words model and transforms the input into a numerical vector using tf-idf vectorization. The chatbot then uses cosine similarity to find the most relevant response from its knowledge base and generates a response.
- Denniz chatbot performed reasonably well in most tests. It was able to understand and respond appropriately to a wide range of natural language inputs, including greetings, questions, requests for information, and assistance. The chatbot's responses were generally relevant and helpful, although in some cases, they could be improved to better address the user's needs or provide more accurate information. The chatbot also demonstrated some ability to recognize when it was unable to provide a satisfactory response and suggest an alternative or request clarification. However, it was occasionally unable to recognize ambiguous or poorly structured inputs, resulting in irrelevant or nonsensical responses. Overall, the chatbot's performance was satisfactory, but there is still room for improvement in terms of its ability to handle a wider range of inputs and provide more accurate and helpful responses.
- Contributions via pull requests are welcome!
- This project is licensed under GPL v3.