A chatbot operates using a blend of natural language processing (NLP) and machine learning to understand user inputs and deliver relevant responses.

Steps to Train a Chatbot:

1. Import Corpus

   • Gather the required data that the chatbot will use for training, ensuring it is relevant and comprehensive.

2. Process the Data

   • Clean the data by removing redundant or irrelevant entries.
   • Ensure the data is well-organized, clear, and beneficial for training.

3. Test Case Handling

   • Standardize the text by converting it to all uppercase or lowercase.
   • Address potential misinterpretations or misrepresentations to improve accuracy.

4. Tokenization

   • Break down sentences into individual words.
     • Example: "This is a blog" → ["This", "is", "a", "blog"].

5. Stemming

   • Extract the root form of words (etymology).
   • Identify and group similar words across different tenses or variations.
     • Example: "running" → "run".

6. Generating Bag of Words (BoW)

   • Represent words as numbers by generating vector embeddings.
   • Perform dot operations to compare vectors for similarity or relationships.

7. One-Hot Encoding

   • Convert categorical variables into a format that machine learning algorithms can understand.
   • Ensure data is clean and structured for better model performance.

Architecture :