Our second case study will take a look at hotel reviews data and attempt to cluster the reviews into topics. We will be employing a latent semantic analysis (LSA), which is a name given to the process of applying a PCA on sparse text document—term matrices. It is done to find latent structures in text for the purpose of classification and clustering.
Case study 2 - predicting topics of hotel reviews data
by Divya Susarla, Sinan Ozdemir
Feature Engineering Made Easy
- Preface
- Introduction to Feature Engineering
- Motivating example – AI-powered communications
- Why feature engineering matters
- What is feature engineering?
- Evaluation of machine learning algorithms and feature engineering procedures
- Feature understanding – what’s in my dataset?
- Feature improvement – cleaning datasets
- Feature selection – say no to bad attributes
- Feature construction – can we build it?
- Feature transformation – enter math-man
- Feature learning – using AI to better our AI
- Summary
- Feature Understanding – What's in My Dataset?
- Feature Improvement - Cleaning Datasets
- Feature Construction
- Feature Selection
- Feature Transformations
- Dimension reduction – feature transformations versus feature selection versus feature construction
- Principal Component Analysis
- Scikit-learn's PCA
- How centering and scaling data affects PCA
- A deeper look into the principal components
- Linear Discriminant Analysis
- LDA versus PCA – iris dataset
- Summary
- Feature Learning
- Case Studies
- Other Books You May Enjoy
Case study 2 - predicting topics of hotel reviews data
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