Python In Practice - 15 Projects to Master Python - Extracting Features and Transforming the Reviews Data

The video tutorial covers the process of feature extraction from text data, specifically reviews and ratings. It begins with dividing data into input and output, followed by creating vectors using CountVectorizer. The tutorial explores vocabulary and feature names extracted from the text data. It then applies TF-IDF transformation to the features and prepares the data for model training, focusing on converting reviews into a digital format and associating them with ratings.

What are the categories used for ratings in the data division process?

• Excellent, Good, Average

• High, Medium, Low

• Average, Poor, Good

• Positive, Neutral, Negative

Which tool is used for feature extraction from text data in the second section?

• TF-IDF Transformer

• Word2Vec

• Count Vectorizer

• PCA

What is the purpose of transforming the input data using Count Vectorizer?

• To reduce dimensionality

• To extract vocabulary and feature names

• To improve data accuracy

• To normalize the data

What transformation is applied to the features after extraction in the final section?

• Standardization

• TF-IDF Transformation

• Min-Max Scaling

• Normalization

What is the next step after preparing the input with TF-IDF transformation?

• Feature selection

• Data cleaning

• Model training

• Data visualization