Predictive Analytics with TensorFlow 6.4: TF-IDF Model for Predictive analytics

The video tutorial covers the concept of TF-IDF, a method used in text analysis to determine the importance of words in documents. It explains how to compute term frequency (TF) and inverse document frequency (IDF), and how to implement TF-IDF in Python using user-defined functions and the SKLearn library. The tutorial also demonstrates how to improve predictive models with TF-IDF and NLTK, and discusses training and testing models with TF-IDF features. Finally, it introduces the next topic of using word2vec for sentiment analysis.

How can the SK learn Python module assist in feature extraction?

What steps are involved in normalizing text for TF-IDF?

What is the significance of plotting accuracy over time in model evaluation?