Introduction

What is data mining?

Data mining is a process that uses various data analysis tools to discover patterns and relationships in data that can be used to make valid predictions.

It involves:

• Understanding business/research problems
• Collecting and preparing data
• Analyzing data to find patterns
• Building predictive models

Evaluating CRISP-DM Methodology - the standard process for data mining projects follows these phases:

• Business understanding: Clarifying objectives and requirements
• Data understanding: Exploring data to identify quality issues and insights
• Data preparation: Cleaning and transformation data for analysis
• Modeling: Applying various data mining techniques
• Evaluation: Assessing model performance
• Deployment: Implementing the solution

Key concepts

Types of data mining tasks:

• Classification (prediction categories)
• Regression (prediction numerical values)
• Clustering (grouping similar items)
• Association analysis (finding relationships)

Data understanding and preparation

• Data types (categorical, numerical, etc)
• Data quality issues (missing values, outliers)
• Data transformation techniques
• Feature selection

Model evaluation

• Performance metrics (accuracy, precision, recall)
• Validation techniques
• Avoiding overfitting/underfitting