Introduction

• Overview of RapidMiner Studio
• Orientation to RapidMiner UI and features

CRISP-DM Methodology in RapidMiner

• Understanding CRISP-DM framework
• Application in estimation and projection of values

Data Understanding and Preparation

• Data import and exploration
• Preprocessing and cleaning techniques
• Advanced data transformation methods

Data Modeling with RapidMiner

• Introduction to data modeling
• Selection and application of machine learning algorithms
• Supervised learning algorithms
• Unsupervised learning algorithms

Model Evaluation and Deployment

• Techniques for model evaluation
• Strategies for model deployment
• Model realignment and optimization

Time Series Analysis and Forecasting

• Fundamentals of time series analysis
• Application of moving average models
• Time series preprocessing and data aggregation

Advanced Time Series Techniques

• Decomposition analysis
• Projection with time windows
• Projection with feature generation

ARIMA Modeling

• Understanding ARIMA models
• Practical application in RapidMiner

Summary and Next Steps