NCA-GENM Question 139
Select 3You are tasked with building a text classification pipeline to identify whether customer reviews are positive or negative. Which Python packages would be most suitable for preprocessing text data and building a deep learning classification model?
- A
spaCy
- B
NumPy
- C
Keras
- D
Pandas
- E
Seaborn
Show answer and explanation
Correct answers: A, B, C
Explanation
To implement a text classification pipeline, spaCy is used for preprocessing the text data (e.g., tokenization and lemmatization), NumPy is used for managing numerical data representations, and Keras is used to design and train the deep learning classification model. While Pandas and Seaborn are useful for other tasks, they are not directly relevant to the requirements of this specific scenario.
- A. Correct.
spaCy is a powerful library for natural language processing (NLP) tasks, including tokenization, lemmatization, and named entity recognition, which are useful for preprocessing text data.
- B. Correct.
NumPy is essential for handling numerical data and arrays, which are common in machine learning workflows. It is often used to manipulate data before feeding it into models.
- C. Correct.
Keras is a high-level deep learning library ideal for building and training classification models, such as the one required for this task.
- D. Incorrect.
Pandas is a data manipulation library. While it is useful for handling tabular data, it is not specialized for text preprocessing or model building in this specific context.
- E. Incorrect.
Seaborn is a visualization library and does not directly contribute to text preprocessing or model building for classification tasks.