About Preprocessing

**Preprocessing **is crucial for getting clean and structured text data before feeding it into a deep learning model. Since you're working on predicting the next word in marketing texts, here are the best steps to follow:

1. Lowercasing – Convert all text to lowercase to maintain consistency and reduce vocabulary size.

2. Removing Special Characters and Punctuation – This helps eliminate unnecessary noise, keeping only meaningful words.

3. Tokenization – Split sentences into individual words or subwords.

4. Removing Stopwords – Common words like "the," "is," and "at" don’t add much value to prediction models, so they can be removed.

5. Lemmatization/Stemming – Convert words to their root form (e.g., "running" → "run"), making it easier for the model to generalize.

6. Padding and Truncating Sequences – Ensure that input sequences have a fixed length, which is especially important for training neural networks.

7. Encoding Words as Numbers – Convert text into numerical representation using word embeddings or one-hot encoding.

Given that you're using TensorFlow, you might also want to consider techniques like TextVectorization, which can handle tokenization and encoding efficiently.