- Introducing data mining
- A simple affinity analysis example
- What is affinity analysis?
- Product recommendations
- Implementing a simple ranking of rules
- Support
- Confidence
- Ranking to find the best rules
- A simple classification example
- What is classification?
- Loading and preparing the dataset
- Implementing the OneR algorithm
- The algorithm
- Testing the algorithm
- The rule
- scikit-learn estimators
- Nearest neighbors
- Distance metrics
- Loading the dataset
- Moving towards a standard workflow
- Running the algorithm
- Setting parameters
- Preprocessing using pipelines
- An example
- Standard preprocessing
- Putting it all together
- Pipelines
- Loading the dataset
- Collecting the data
- Cleaning up the dataset
- Extracting new features
- Decision trees
- Parameters in decision trees
- Using decision trees
- Glossary for expanded standings
- Extra: Model Training Using GridSearch
- Random forests
- How do ensembles work?
- Parameters in Random forests
- Applying Random forests
- Engineering new features (a guide)
- Affinity analysis
- Algorithms for affinity analysis
- Choosing parameters
- The movie recommendation problem
- Obtaining the dataset
- Sparse data formats
- The Apriori implementation
- The Apriori algorithm
- Implementation
- Extracting association rules
- Evaluation