Your knowledge about the techniques used in Data Science: you need to be well versed with concepts like Scaling, tokenization, and vectorization and know which situations all these should be applied.

To answer the question, let's break it down into steps:

Step 1: Scaling Scaling is a technique used in data science to normalize the values of different features or variables. It is applied when the features have different scales or units. The purpose of scaling is to bring all the features to a similar range, so that they can be compared and analyzed effectively. Common scaling techniques include standardization (subtracting the mean and dividing by the standard deviation) and normalization (scaling the values to a range between 0 and 1).

Step 2: Tokenization Tokenization is the process of breaking down a text or document into smaller units called tokens. These tokens can be words, sentences, or even characters, depending on the level of granularity required. Tokenization is an important step in natural language processing (NLP) tasks, as it helps in text analysis, sentiment analysis, and language modeling. There are various tokenization techniques available, such as word tokenization, sentence tokenization, and character tokenization.

Step 3: Vectorization Vectorization is the process of converting text or categorical data into numerical vectors that can be used as input for machine learning algorithms. It is a crucial step in data science, as most machine learning algorithms require numerical input. Vectorization techniques include one-hot encoding, count vectorization, and TF-IDF (Term Frequency-Inverse Document Frequency) vectorization. These techniques represent the text or categorical data in a numerical format, enabling the algorithms to process and analyze the data effectively.

In summary, scaling is applied to normalize the values of different features, tokenization is used to break down text into smaller units, and vectorization is used to convert text or categorical data into numerical