Notes on Data Mining

1. Getting Started with Data Mining

• 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

2. Classifying with scikit-learn Estimators

• 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

3. Predicting Sports Winners with Decision Trees

• 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)

4. Recommending Movies Using Affinity Analysis

• 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

5. Extacting Features with Transformers