Data exploration
When working in a methodological environment, datasets are often well known and preprocessed, such as Kaggle datasets. However, in real-world business environments, one important task is to define the dataset from all possible sources of data, explore the gathered data to find the best method for preprocessing it, and ultimately decide on the ML and natural language models that fit the problem and the underlying data best. This process requires careful consideration and analysis of the data, as well as a thorough understanding of the business problem at hand.
In NLP, the data can be quite complex, as it often includes text and speech data that can be unstructured and difficult to analyze. This complexity makes preprocessing an essential step in preparing the data for ML models. The first step of any NLP or ML solution starts with exploring the data to learn more about it, which helps us decide on our path to tackle the problem.
Once the data has been preprocessed...
